Technical Report on

Cost – Effectiveness of Remote Sensing for

Agricultural Statistics in Developing and Emerging

Economies

Publication prepared in the framework of the Global Strategy to improve Agricultural and Rural Statistics

December 2015

Technical Report on

Cost-Effectiveness of Remote Sensing for

Agricultural Statistics in Developing and Emerging

Economies

Table of contents

Figures……………………………………………………………………………………………………………… 4 Tables………………………………………………………………………………………………………………. 5 Acronyms and Abbreviations……………………………………………………………………………. 6 Preface…………………………………………………………………………………………………………….. 10 Executive Summary………………………………………………………………………………………….. 12 Acknowledgments……………………………………………………………………………………………. 16 1. Introduction…………………………………………………………………………………………………. 17 2. Application Domains…………………………………………………………………………………….. 18

2.1. Land cover mapping…………………………………………………………………………. 18 2.2. Census and survey frame construction……………………………………………… 20 2.3. Production of field survey documents………………………………………………. 21 2.4. Estimation of crop areas…………………………………………………………………… 21 2.5. Forecasting and monitoring crop yields……………………………………………. 23 3. Who does what?............................................................................................. 24

3.1. International investments…………………………………………………………………. 24 3.2. National remote sensing activities in developing and transition Countries…………………………………………………………………………………………..

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4. The Cost-Efficiency Literature……………………………………………………………………….. 47 5. Cost case Studies………………………………………………………………………………………….. 56

5.1. Haiti: Point Area Frame Sampling……………………………………………………... 56 5.2. Morocco: Area Frame Sampling………………………………………………………… 68 5.3. China: Area Frame Sampling and Regression Analysis………………………. 74 5.4. India: Area Frame Sampling and Pixel Counting………………………………… 84

6. What’s next?................................................................................................... 94 6.1. Imagery access: the 2015 situation…………………………………………………… 94 6.2. The immediate future of imagery and software………………………………… 99 6.3. The use of drones for field surveys: Malawi and China……………………... 100

References………………………………………………………………………………………………………… 104

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Figures

Figure 1 - Haiti, RENOP Land – Use map……………………………………………………………….. 58 Figure 2 - Comparison of the standardized sampling variances: a) Haiti survey, b) French TerUti survey…………………………………………………………………………

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Figure 3 - Stratification Efficiency versus proportion of Non- Sampled Area in a Province……………………………………………………………………………………………….

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Figure 4 - Administrative Divisions of the People’s republic of China………………….. 75 Figure 5 - China, relation between wheat CVs and areas at county level in Anhui province…………………………………………………………………………………….

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Figure 6 - China, relation between middle rice CVs and areas at county level in Anhui province…………………………………………………………………………………….

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Figure 7 - CV as a Function of Rice Crop Area, by State……………………………………….. 89

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Tables

Table 1 - Main uses of Remote Sensing in the 31 countries analyzed…………………… 27 Table 2 - Haiti, Point Area Frame Land Covers Percentages…………………………………. 57 Table 3 - Haiti, Stratification plan…………………………………………………………………………. 58 Table 4 - Haiti, Sampling Plan for the 2013 growing Season…………………………………. 60 Table 5 - Haiti, Estimated Crop Areas (2013, first growing season)………………………. 63 Table 6 - Sample Allocation by Stratum………………………………………………………………… 66 Table 7 - Variance Gains at the Department and National Levels…………………………. 66 Table 8 - Morocco Estimated Crop Areas……………………………………………………………… 70 Table 9 - Morocco, stratification relative efficiencies at national level…………………. 71 Table 10 - Morocco, stratification relative efficiencies at province level……………… 73 Table 11 - Area Frame Surveys in China……………………………………………………………….. 76 Table 12 - China, Official Statistics for 2013 in Area Frame Provinces…………………… 77 Table 13 - China, design level crop areas in Anhui province…………………………………. 78 Table 14 - Comparison of ER Calculation Methods……………………………………………….. 80 Table 15 - China Efficiencies at the County Level………………………………………………….. 81 Table 16 - India Annual Forecasts based on Remote Sensing……………………………….. 85 Table 17 - Rice Sampling Plan, CV Stratification Efficiency……………………………………. 88 Table 18 – Punjabi State LISS III Data (24 Sept 2014)……………………………………………. Gujarat State Landsat Data (31 Dec 2014)……………………………………………

90 91

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Acronyms and Abbreviations AAIC Agricultural Assessments International Corp. ADB Asian Development Bank AFDB African Development Bank AF Area Frame AFSIS Asian Network of Country Agricultural Statisticians AGHRYMET Centre Regional de Formation et d'Application en Agrométéorologie et

Hydrologie Opérationnelle AGRICAB Framework for Enhancing Earth Observation Capacity for Agriculture

and Forest Management in Africa AGRIMONIS Tarbil Integrated Agricultural Monitoring System ALOS Advanced Land Observing Satellite AMIS Agricultural Market Information System ASPRS American Society of Photogrammetry and Remote Sensing AVHRR Advanced Very High Resolution Radiometer AWIFS Advanced Wide Field Sensor BAS Bureau of Agricultural Statistics, Philippines BELSPO Belgian Scientific Policy BFAP Bureau for Food and Agricultural Policy, South Africa BIOMA Biophysical Models Applications BPS Badan Pusat Statistik, Indonesia CAAS Chinese Academy of Agricultural Sciences CAERS Cropland Acreages Estimation by using Remote Sensing and Sample

Survey CBA Cost/Benefit Analysis CCD Charge-Couple Device CDL Crop Data Layer CGMS Crop Growth Monitoring System CHARMS Chinese Agricultural Remote Sensing Monitoring System CIMMYT Centro Internacional de Mejoramiento de Maíz y Trigo CNDVI Corine Normalized Difference Vegetation Index CNIGS Centre National d’Information Geo-Spatiale, Haiti CNT Centre National de la Cartographie et de la Télédetection, Tunisia CONAB Companhia Nacional de Abastecimento, Brazil CORINE Coordination of Information on the Environment CSA Central Statistical Agency, Ethiopia CSE Centre de Suivi Ecologique, Senegal CSIR Council of Scientific and Industrial Research, India CSIRO Commonwealth Scientific and Industrial Research Organization CV Coefficient of Variation CVM Contingent Valuation Method DAFF Department of Agriculture, Forestry and Fisheries, South Africa DAPS Direction de l'Analyse de la Prévision et des Statistiques, Senegal

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DGEDA Direction Générale des Etudes et du Développement Agricole, Tunisia DIMPE Direction de Metodología y Producción Estadística, Colombia DMC Disaster Satellite Monitoring DMN Direction de la Météorologie Nationale, Morocco DRSRS Department of Resource Surveys and Remote Sensing, Kenya DSNA National Directorate for Agrarian Services, Mozambique DSS Direction de la Stratégie et des Statistiques, Morocco EC European Commission EC-FED EC Fond Européen de Développement EMPRABA Empresa Brasiliera de Pesquisa Agropecuària ERMEX-NG Estación de Recepción México Nueva Generación ESA European Space Agency ESA LC-CCI European Space Agency Land Cover–Climate Change Initiative EUMETSAT European Organization for the Exploitation of Meteorological

Satellites EUSI European Space Imaging FAO Food and Agriculture Organization of the United Nations FAO-GIEWS FAO Global Information and Early Warning System on Food and

Agriculture FAORAP FAO Regional Office for Asia and the Pacific FAS Foreign Agriculture Service, USDA FASAL Forecasting Agricultural Outputs using Space, Agro-Meteorology and

Land-Based Observations FEWS-NET Famine Early Warning Systems Network Geoglam Global Agricultural Geo-Monitoring GeoSAS GeoSAS Consulting Service PLC, Ethiopia GEOSS Group on Earth Observation System of Systems GISTDA Geo-Informatics and Space Technology Development Agency, Thailand GMES Global Monitoring for Environment and Security, European Union GMFS Global Monitoring for Food Security; ESA project GPS Global Positioning System GSD Ground Sampling Distance IBGE Instituto Brasiliero de Geografia e Estatística ICALRD Indonesian Centre for Agricultural Land Resources Research and

Development ICRISAT International Crops Research Institute for the Semi-Arid Tropics IGN Institut Géographique National, France ILRI International Livestock Research Institute IKI Russian Space Research Institute, Russia INAM Instituto Nacional de Meteorologia, Mozambique INE Instituto Nacional de Estadística, Guatemala INEI Instituto Nacional de Estadística e Informática, Peru INPE Instituto Nacional de Pesquisas Espacias, Brazil INRA Institut National de la Recherche Agronomique, Morocco IRRI International Rice Research Institute ISPRS International Society for Photogrammetry and Remote Sensing ISRO Indian Space Research Organization

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ISTAT Istituto Nazionale di Statistica, Italy ITA Consorzio Italiano per il Telerilevamento dell’Ambiente, Italy ITC Faculty of Geo-Information Science and Earth Observation, University

of Twente, Netherlands JFPR Japan Fund for Poverty Reduction JAXA Japan Aerospace Exploration Agency JECAM Geoglam Joint Experiment for Crop Assessment and Monitoring JFPR Japan Fund for Poverty Reduction JPL Jet Propulsion Laboratory, NASA JRC Joint Research Centre, European Commission JRC-MARS JRC Monitoring Agricultural Resources unit KKU Khon Kaen University LF List Frame LISS Linear Imaging Self Scanner LPSA Levantamento Sistemàtico de Produçào Agricola, Brazil LUCAS Land Use/Land Cover Area Frame Survey MAGyP Ministerio Agricultura, Ganadería y Pesca, Argentina MARNDR Ministère de l’Agriculture, des Ressources Naturelles et du

Développement, Haiti MARS Monitoring Agricultural Resources, EC MERIS Medium Resolution Imaging Spectrometer MF Multiple Frame MNCFC Mahalanobis National Crop Forecast Centre, India MODIS Moderate Resolution Imaging Spectroradiometer MS Multispectral Imagery NASA National Aeronautics and Space Administration NASS National Agricultural Statistics Service, USA NBS National Bureau of Statistics, China NDVI Normalized Difference Vegetation Index NGCC National Geomatics Centre of China NISR National Institute of Statistics, Rwanda NRCS Natural Resources Conservation Service, USA OSS Observatoire du Sahara et du Sahel PAN Panchromatic Imagery PICES Producer Independent Crop Estimates System (South Africa) PITDD Programme d'Informations Territoriales pour le Développement

Durable, Haiti PPS Probability Proportional to Size PREVS Pesquisa de Previsão e Acompanhamento de Safras, Brazil PRISM Philippine Rice Information System PROBAV Project for Onboard Autonomy (satellite) – Vegetation PSU Primary Sampling Unit RADI Institute of Remote Sensing and Digital Earth, China RCMRD Regional Centre for Mapping Resources for Development, Kenya RENOP Référentiel National d’Observations Ponctuelles, Haiti RESTEC Remote Sensing Technology Center of Japan RGA Recensement Général de l'Agriculture, France

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RIICE Remote Sensing-Based Information and Insurance for Crops in Emerging Economies

RISAT Radar Imaging Satellite RMA Risk Management Agency, USDA RS Remote Sensing SAC-ISRO Space Applications Centre – Indian Space Research Organization SAGARPA Segretería de Agricultura, Ganadería, Desarollo Rural, Pesca y

Alimentación, Mexico SANSA South African National Space Agency SAR Synthetic Aperture Radar SDSA Sous-Direction des Statistiques Agricoles, Tunisia SEDENA Secretaría de la Defensa Nacional, Mexico SIAP Servicio de Información Agroalimentaria y Pesquera, Mexico SPOT Satellite pour l’Observation de la Terre SRS Simple Random Sampling SSU Secondary Sampling Unit STARS Spurring a Technology for Agriculture through Remote Sensing SUPARCO Pakistan Space and Upper Atmosphere Research Commission Tarbil Tarımsal İzleme ve Bilgi Sistemleri, Turkey THEOS Thailand Earth Observation Satellite TNAU Tamil Nadu Agricultural University UBA University of Buenos Aires UCL Université catholique de Louvain UEM Universidade Eduardo Mondlane UMD-GEOSS University of Maryland – Global Earth Observation System of Systems UNEP-DEWA United Nations Environment Programme – Division of Early Warning

and Assessment UNSTAT United Nations Statistics Division UPM Universidad Politécnica de Madrid USAID United States Agency for International Development USDA United States Department of Agriculture USGS United States Geological Survey VEGETATION Part of SPOT System VOI Value of Information WAOB World Agriculture Outlook Board (USDA) WASDE World Agricultural Supply and Demand Estimates (USDA) WFP-VAM World Food Programme – Vulnerability Analysis and Mapping

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Preface This Technical Report on Cost-Effectiveness of Remote Sensing for Agricultural Statistics in Developing and Emerging Economies was prepared in the framework of the Global Strategy to Improve Agricultural and Rural Statistics. The Global Strategy is an initiative that was endorsed in 2010 by the United Nations Statistical Commission. It provides a framework and a blueprint to meet current and emerging data requirements and the needs of policy makers and other data users. Its goal is to contribute to achieving greater food security, reduced food price volatility, higher incomes and greater well-being for rural populations, through evidence-based policies. The Global Strategy consists of 3 pillars: (1) establishing a minimum set of core data; (2) integrating agriculture in National Statistical Systems (NSS); and (3) fostering statistical systems’ sustainability, through governance and statistical capacity building.

The Action Plan to Implement the Global Strategy includes an important Research programme, to address methodological issues for improving the quality of agricultural and rural statistics. The envisaged outcome of the Research Programme consists in scientifically sound and cost-effective methods that will be used as inputs in preparing practical guidelines for use by country statisticians, training institutions, consultants, etc.

To enable countries and partners to benefit, at an early stage, from results of the Research activities, it was decided to establish a Technical Reports Series, to allow widespread dissemination of the technical reports and advanced draft guidelines and handbooks available. This would also enable countries to provide earlier and more feedback on the papers.

The Technical Reports and draft guidelines and handbooks published in this Technical Report Series are prepared by Senior Consultants and Experts, and reviewed by the Scientific Advisory Committee (SAC) of the Global Strategy, the Research Coordinator at the Global Office and other independent Senior Experts. For some of the research topics, field tests are organized before final results can be included in the relevant guidelines and handbooks.

The main purpose of this Technical Report on Cost-Effectiveness of Remote Sensing for Agricultural Statistics in Developing and Emerging Economies is to carry out a cost-efficiency analysis on the use of remote sensing through a literature review and the analysis of case studies in a series of 31 developing

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and emerging countries. In particular, the report reviews the main uses of remote sensing such as frame construction, stratification, production of survey documents, crop area estimation, forecasting and monitoring of crop yields. The report also provides an analysis of the current situation of imagery access and future developments in the sector.

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Executive Summary The use of Remote Sensing for agricultural statistics began in the 1970s, with the launch of the Landsat MSS satellite. At that time, NASA (in the United States) as well as other space agencies, such as ISRO (India) launched research programs aiming to use remote sensing for agricultural statistics. Later, many other space centres became active (Brazil, Canada, China, EU, France, Russia). Today, approximately 200 earth-observation satellites are operational.

High-resolution optical and radar data are now readily available from these satellites. The main constraints limiting their use are: i) cost – very high resolution imagery costs between US$20 and US$40 per km2; ii) data size – the terabyte is the storage unit when working at country level; and iii) the technological limitations upon using GIS and of image-processing software.

The current policy of the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) is to make imagery from the Landsat 8 and Sentinels satellites available free of charge and downloadable within 24 hours as processed products such as vegetation indexes, soil moisture and land-cover maps. This opens the way to innovative uses, especially in developing countries.

It is worth noting that the private sector is also now involved in the field of satellite remote sensing: government-supported companies such as DigitalGlobe deliver imagery, and start-ups such as Skybox, Planetlab and Satellogic are developing micro-satellites, of which about 400 will be launched in 2015 and 2016. The question of the cost of high-resolution imagery from micro-satellites is not yet settled.

The use of remote sensing for agricultural statistics has various facets.

In censuses and surveys, the first stage consists in elaborating a population frame. Satellite imagery is of the utmost value for this purpose, as provides a s table reference when elaborating the population frame; in particular, it may be used to subdivide areas of interest into enumeration areas in which list frames of holdings are defined, and it helps with the definition of area frames composed of primary and secondary sampling units, which are readily identifiable in the digital imagery. Remote sensing also supports new approaches to population stratification: it enables, for example, the use of the

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crop intensity classes derived from archived imagery, which in association with various sampling fractions can significantly reduce sampling variances.

Whatever the census or survey approach, imagery will enhance the efficiency of field surveyors by enabling optimized displacements, facilitating the location of the various survey elements and providing more precise information for farmers as to the fields surveyed.

The classified imagery can deliver crop data layer maps, which are issued annually before harvests to provide an initial estimation of current crop areas and which also provide information for private-sector organizations that will enable them to improve the agricultural business. When sufficiently reliable, the imagery is merged with ground survey data in the estimation process. Calibration estimators will usually help to reduce sampling variance.

Low-resolution imagery, such as that available from MODIS, AVHRR and PROBA-V satellites, can provide seamless coverage of the target area. This imagery can be merged with crop-yield models and meteorological information to give pre-harvest crop yield forecasts, which in turn enhances the possibilities for sound management of national food security.

The literature on cost-efficiency is unbalanced with regard to agricultural cost/benefit publications and the image value analyses carried out by space agencies.

Information on the costs relating to agriculture applications remains scarce. In precision agriculture, the maximum acceptable cost for farmers is estimated at US$10/ha. In the context of statistical surveys, the Space and Upper Atmosphere Research Commission (SUPARCO) project in Pakistan, however, has reduced survey costs from US$7 million to US$300,000 and staff from 3,500 to 18 by moving from classical list frame surveys to area frame surveys using satellite imagery.

Image value analyses are abundant, and usually attribute high value to Landsat data for agriculture: remote sensing applications in the state of Iowa are valued at US$858 million per year; the value of Landsat imagery in the world agricultural supply and demand estimates is thought to be US$3 million; and USDA Crop Data Layer at US$4 million. The recommended way to evaluate the efficiency of remote sensing is the derivation of stratification or regression relative efficiencies, expressed as ratios of variances. These are discussed in the four case studies in Chapter 5.

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To select case studies for cost-efficiency analysis, 31 developing or emerging countries were reviewed in terms of their uses of remote sensing.

Stratification based on remote sensing – land cover mapping and point screen photo-interpretation – was encountered in Argentina, Brazil, China, Ethiopia, Guatemala, Haiti, India, Kenya, Malawi, Morocco, Mozambique, Nigeria, Pakistan, Peru, Rwanda, Senegal and South Africa.

Crop area estimation based on remote sensing – regression analysis and pixel counting – was being tested in Argentina, Bangladesh, Brazil, China, India, Kenya, Malawi, Mexico, Morocco, Mozambique, Pakistan, the Philippines, Russia, Senegal, South Africa, Thailand, Tunisia and Turkey.

Crop yield forecasts were made in Argentina, Brazil, China, India, Kenya, Morocco, Russia, Senegal, Thailand, Tunisia and Turkey.

Haiti, Morocco, China and India were selected for case studies.

In Haiti, the point frame survey by the Centre National de l’Information Géo-Spatiale (CNIGS) was analysed. The cost of stratification reflected increased field survey costs of 3 percent, but decreases in variances of as much as 50 percent at the regional level were obtained.

In Morocco, the stratification based on land-cover maps of 66,000 km2 derived from expensive Satellite Pour l’Observation de la Terre imagery increased annual survey costs by 30 percent. However, in view of the efficiencies obtained, the investment is worthwhile.

In China, remotely sensed stratification covered 1.65 million km2. The cost increase was only 3 percent, but in Anhui province stratification relative efficiencies of 2.8 for rice and 1.4 for corn were obtained.

In India, radar and optical imagery is used to monitor 90 percent of the production of the eight major crops. A stratification efficiency for rice of between 1.2 and 3.3 was achieved, and bias induced by pixel counting could be evaluated.

Current conditions of data availability, access, price and pre-processing enable an efficient use of remote sensing for agricultural statistics. The main use – regression analysis integrating ground surveys and image classification – is rarely applied in statistical processes in the countries studied. Instead, it was

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noted that the use of remote sensing for land-use mapping and point photo-interpretation supporting frame optimization was widespread; the case studies also showed that these uses had resulted in improved cost efficiency.

In view of current technical progress, the future of remote sensing in agricultural statistics looks bright – though care is needed in applying it in areas such as sub-Saharan Africa, where the small size of the fields, crop mixtures and heterogeneity of crop phenology are common limiting factors.

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Acknowledgments This report was prepared by Jacques Delincé (FAO) and profits from studies published by the Global Strategy Office –www.gsars.org/category/publications/ – and from support provided by the authorities of the countries where the case studies were carried out.

The work and support of a large number of experts is warmly acknowledged:

Aguilar, J. (SAGARPA) Ambrosio, L. Iglesias, L. (UPM) Arrach, R. and Kamili M. (DSS) Astrand, P., Games, M., Gallego, J., Lemoine, G., Léo, O., Kerdiles, H. and Rembold, F. (JRC) Bartalev, S. (IKI) Bayma-Silva, G. (Embrapa) Berk, B. (Tarbil) Beukers, R. and Collette, A. (DAFF) Boubée, P. (Airbus D.S.) Brown, M. and Doorn, B. (NASA) Bolliger, F. (FAO) Becker, I. (UMD-GEOSS) Bydekerke, L. (Eumetsat) Chen, Z. (CAAS) Conde, M.C. (UBA) Craig, M., Hanuschak, G. and Vogel, F. (formerly NASS) Defourny P. (UCL) Du Preez, E. (SiQ Ltd.) Ferreira, F. (GeoterraImage) Genovese, G. and Massart, M. (EC) Giovacchini, A. (ITA) Hardjo, H. (BPS)

Imala, V. (DRSRS) Harris, M., Johnson, D., Miller, M. & Mueller, R. (NASS) Justice, C. (UMD) Latham, J. (FAO) Laur, H. (ESA) Maligalig, D.S. (ADB) Manzi, S. (NISR) Medfai, A. (DGEDA) Meyer, F. and Van der Burgh, G. (BFAP) Nelson, G. (formerly IFPRI) Ngendakumana, V. (AfDB) Parihar, J.S. (ISRO) Piffer, T. (CONAB) Ray, S. (MNCFC) Recide, R. (BAS) Rojas, J. (FAOSLM) Schichor, P (EUSI) Singh, D. (FAORAP) Tenorio, J. (SIAP) Van Speybroek, D. (BELSPO) Verstraete, M. (SANSA) Wigton, W. (AAIC) Yu, X. and Zhou, W. (NBS)

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1 Introduction Since the launch of the Landsat series in July 1972, agriculture has been a major beneficiary of satellite imagery. Among the first applications of the technology in this field was that occurring in the efforts to improve agricultural statistics (Hanuschak and Delincé, 2004; Taylor et al., 1997), even though constraints posed by lack of the required expertise in statistics, image software and budget availability often limited its adoption.

With spatial resolution brought down to 0.5 m (Marchisio, 2014), farmers’ declarations could be checked (Kay et al., 1997) and precision farming (Schumpeter, 2014) became feasible, because the economic value of these operations easily justified the expense. On other scales, land-cover mapping (Defourny et al., 2011; Chen, 2014) and early-warning systems (Brown and Brickley, 2012; Rembold et al., 2006 & 2013) became a reality. It is worth noting that the cost of imagery and software followed a kind of Moore’s law, and today’s free access to images from Landsat 8 and Sentinel 1 and 2 and image analysis software such as Skybox opens up new possibilities.

This study concentrates on the cost-efficiency of using remote sensing for agricultural statistics in developing and emerging economies. Western Europe and North America are therefore not considered, though many of the examples discussed herein are the result of close collaboration with agencies from these areas.

The reader interested in the state of African statistical systems will find detailed information in FAO (2010) and AFDB (2014); however, these do not report on the use of remote sensing techniques by national statistical services.

This report has six chapters. After an introductory chapter, Chapter 2 considers various applications of remote sensing for agricultural statistics; Chapter 3 looks at “who does what” with imagery in 31 countries; Chapter 4 reviews the publication and study methods of cost-efficiency aspects over the last 20 years; and Chapter 5 describes case studies of the use of imagery for creating area frames in Haiti and Morocco and the use of imagery at the estimation level in China and India. Chapter 6 examines the future developments in the imagery sector.

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2 Application Domains 2.1. Land cover mapping

The daily availability of low-resolution imagery from the AVHRR (1 km), VEGETATION (1 km), MODIS (500–250m), MERIS (250m) and PROB-V (100 m) instruments enables the derivation of cropland masks at the global scale. With the free access to current Landsat 8 acquisitions and to the entire Landsat archive, mapping at 30 m resolution is feasible provided that adequate computing power is available. Such digital maps at resolutions of 1 km to 30 metres provide global-level references for total cropland, which may be split between rain-fed and irrigated land (Portmann et al. 2009). The reader interested in cropland mapping should consult Basso et al. (2014b). For Africa only, recommended reading is Vancutsem et al. (2013), for an analysis of ten cropland data sets. Current data sets come from AVHRR, VEGETATION, JERS-SAR (Thenkabail et al., 2009), MODIS (Pittman et al., 2010; Friedl et al., 2010 and Fritz et al., 2015), MERIS (Arino et al., 2008) and Landsat (Yu et al., 2013).

Although these data sets do not fully agree as to total available cropland or exact locations, their use can at least enable stratification in terms of agricultural and non-agricultural land and hence prevent wastage of samples where crop finding probabilities are near zero. Assuming, for example, a non-agricultural stratum occupying a third of an administrative area of interest, reallocation of the entire sample to the remaining cropland area will enable stratification with relative efficiency of 1.51 at almost no cost. The fact that the crop mask contains errors will have no effect on bias, but it will reduce relative efficiency if too many samples are wrongly classified. Such cropland maps are not uniformly accurate, and regions that are less intensively cultivated are relatively poorly classified. The efficiency of stratification will in general be lower in developing countries than in intensively farmed regions.

1 Without stratification the variance of the mean will be σ2/(2/3)*n – 33 percent of sample size is lost in a region without crops. With stratification the variance will become σ2/n, so the relative efficiency is Er= {σ2/(2/3)*n}/{σ2/n} =3/2 =1.5.

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Examples of national land-cover maps include the Sudan (Latham, 2011 and 2012) and Pakistan (SUPARCO, 2012), where the maps have a minimum polygon size of 1 ha. In northern Sudan alone – an area of 1.9 million km2 –240 man-months were needed for photo-interpretation to regroup the 83 original classes into seven major classes: this shows that the stratification of an agricultural frame cannot by itself justify the adoption of land-cover classification. Nonetheless, once they have been created for public decision-making land-cover maps are a unique tool for optimal stratification.

Google Earth should also be considered for stratification when very high resolution imagery is required. Although image geometry is not guaranteed and acquisition dates may be uncertain or unknown, the system provides a cost-efficient way of envisaging stratification, especially point area frame sampling. Experience in Europe with black-and-white orthophotos and in Kenya, Malawi and Mozambique, shows that an operator using the appropriate tools could classify between 600 and 800 points per day. Hence a team of five could stratify 1 million points in less than a month, which shows that stratification of an entire country can be done efficiently. In Europe, the observed stratification relative efficiency with black-and-white orthophotos was 1.6 for cereals and 3.0 for olives (Gallego and Delincé, 2010).

Under the Global Strategy, FAO is seeking to improve methods for using existing land-cover and land-use databases for agricultural statistics (Hill, 2014). To evaluate the potential of satellite imagery in land-cover mapping, test sites of 400 km2 were identified in Bangladesh, Ethiopia, Indonesia, Malawi, Pakistan and Tanzania. Images with resolutions from MODIS at 250 m to Worldview3 at 31 cm have been ordered and will be used to construct land-cover maps. Cost efficiency will be assessed in terms of accuracy and related cost. Final reports are expected in 2016.

The European Space Agency (ESA) has launched the Land Cover Climate Change Initiative (LC-CCI), which offers world-wide land-cover imagery at a resolution of 300 m derived from MERIS for 1998–2002, 2003–2007 and 2008–2012. Three consistent global land-cover products are supplemented by climatological seven-day time series – NDVI, snow cover and burnt areas. With Sentinel-2 now operational, coverage in Africa will be refined to 20 m resolution with monthly updates (Defourny et al., 2014).

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2.2. Census and survey frame construction

In its 2009 Handbook on Geospatial Infrastructure in support of Census Activities, the United Nations Statistics Division (UNSD) began to recommend the use of satellite and aerial imagery for census activities. This was taken up by several census offices, which used the integration of image-analysis functions into GIS software and open-source software running on personal computers. Among the countries implementing or considering this approach are Botswana, Ethiopia, The Gambia, Ghana, Kazakhstan, Lesotho, Malawi, Namibia, Nigeria, the Russian Federation, the Seychelles, South Africa, South Sudan, Swaziland, Tanzania and Zambia (Geospace, 2015; Eze, 2009).

The imagery has several advantages, especially in the absence of digital maps:

• Generally falling within a well-defined projection system, the imagery provides a cartographic framework with accurate representation of territory; it facilitates observance of the usual enumeration area properties such as completeness, non-overlap, respect of administrative divisions, accurate delineation and allocation of attributes.

• Updating and verification require less time and fewer staff because the high-resolution imagery provides adequate detail; the counting and categorization of dwellings could largely become an office task.

• Using personal digital assistant (PDA) devices will minimize the need to print maps and will facilitate planning, because imagery will be downloaded as required for daily needs.

• Surveyors will work more efficiently because image-based maps will make navigation easier.

• The imagery will favour the adoption of integrated frames, especially if a survey area frame approach is adopted.

Area frame construction is another major opportunity to use remote sensing in agricultural statistics.

At the design level, the existence of a positive spatial correlation will favour the selection of numerous small secondary sampling units (SSUs), usually regrouped into primary sampling units (PSUs). A territory may be subdivided on topographical maps by superimposing a grid or by delineating blocks, but frame design will be enhanced if imagery is incorporated. The NASS has always used frames composed of PSUs and SSUs with physical boundaries extracted from Landsat imagery: only a sample of PSUs will be observed, but

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the sampling variance associated with PSUs can be minimized if they are made as similar as possible – that is, they are each as representative as possible of the stratum they belong to. Photo-interpretation of satellite imagery is an efficient way to achieve this.

Imagery is essential for the stratification of an area frame. Photo-interpretation or automatic classification of imagery into land-cover classes enables the definition of homogeneous contiguous or non-contiguous sets within which sampling variance will be minimized. As the sampling variance between strata reaches zero because all strata are sampled or considered to contribute a zero value to the total estimate, the more the strata differ as a result of sound image analysis and the better the outcome. If the area reduces to a point, the reasoning remains valid: the SSU size varies for each point and equals the area of the selected field; if points have been stratified from imagery, their land uses within strata will be more similar than between strata. This ensures that the sampling error will be minimized.

2.3. Production of field survey documents

Whether list or area frames are used, surveyors will profit from the availability of imagery. Aerial photographs or very high-resolution satellite images will help them to access land and locate farmers. Imagery will minimize major declaration and measurement errors. They will also motivate farmers by giving them free access to the most recent technology.

2.4. Estimation of crop areas

GEOSS (2009) and Craig and Atkinson (2013) review the literature on the estimation of crop areas. Duveiller and Defourny (2010) present an attractive conceptual framework for considering the effectiveness of imagery in function of the agricultural landscape.

Two main methods derive crop area statistics from remotely sensed data: the pixel counting method and the calibration method.

Pixel counting is the more direct way: although often criticized (Gallego et al., 2010), this approach is almost the only current operational use of remote sensing.

To create crop data layers (CDLs), NASS uses pixel counting in remotely sensed images, having abandoned pre - harvest calibration methods ten years

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ago. Using multiple sensors and images of various dates from Landsat, Resourcesat and Disaster Satellite Monitoring (DMC), supervised automatic image classification using See5 software is based on current year ground truth provided by the Farm Service Agency Program (FSA) (Mueller and Harris, 2013). A major advantage of a CDL is its availability at end June, when the June enumerative survey results are issued. On the basis of the accuracies given by Johnson (2010), it is evident that results are highly dependent on crop diversity. In Iowa, where corn and soybeans are the dominant crops, classification accuracy reaches 95 percent and area bias can be calculated as less than 1 percent. In North Dakota, classification accuracy is 70 percent, and in Idaho 80 percent, but bias for corn is acceptable at 2 percent in North Dakota and 2 in Idaho; bias for spring wheat is of the same order of magnitude at 4.5 percent in North Dakota and 2 percent in Idaho. In the three states mentioned, the largest bias encountered was 10 percent for 93,000 ha of canola in North Dakota. It is worth nothing that the literature does not provide any evidence as to the necessity of imposing random sampling for collecting the training sets, but considers cost to fall within the first selection criteria. Readers interested by the bias aspects of pixel counting should consult Marley et al. (2014).

Since 2012 the Mahalanobis National Crop Forecasts Centre in India has forecast crop areas for 11 major crops (Ray et al., 2014) on the basis of current-year ground truth from 5,680 field data items and the classification of optical and radar satellite imagery. Preferential prices for Resourcesat and Risat images make the project particularly cost-efficient. The system may be described as a forecast system, because the initial results (on crop area and production) are available before harvest. Results are available several months before the official statistics of the Directorate of Economics and Statistics are issued. The discrepancies – 4 percent for rice on 37 million ha in 2013/14 – are the result of imprecision in both approaches.

Statistics Canada joined the AgriFood Canada agriculture monitoring framework in 2009 and has since carried out annual crop classification using Landsat imagery (Daneshfar et al., 2014). Accuracies in 2013 varied from 74 percent to 90 percent in the nine provinces involved. In 2011 the derived crop areas were compared with census figures, with mixed results. CDLs were derived in JECAM 2013 from Rapideye 5-metre and Radarsat2 images with accuracy above 95 percent, which was considered equivalent to field observations.

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Calibration methods (Benedetti, 2014) are used to integrate field survey data and image classification results. The United States Department of Agriculture (USDA) is the only agency that utilizes a regression estimator based on remote sensing, although limited to the February post-harvest county crop area estimates, when the imagery contains enough information, and to the administrative level where the high field survey variances justify the additional work. Argentina, China and Pakistan are carrying out trials in which estimators reduces sampling errors in ground surveys by integrating auxiliary information known for entire populations: if the auxiliary variable is well correlated with crop areas, the ratio or regression estimators remain unbiased and gain from reduced sampling variance. Coverage by satellite imagery on two dates usually enables classification with an accuracy of between 60 percent and 70 percent depending on crop phenology, field size and landscape complexity. The use of freely downloadable Landsat and Sentinel imagery and open-access software reduces costs and increases the chances that a relative efficiency above 2, guarantees cost-efficiency.

2.5. Forecasting and monitoring crop yields

The forecasting and monitoring of crop yields is effectively supported by daily delivery of weather information and images from satellites. Basso et al. (2014a and 2014b) review the projects and methods in detail. They first describe crop simulation models based on statistical, mechanical or process-based approaches; these rely on soil, weather and farming conditions that are not readily picked up by remote sensing. They list 26 remote sensing vegetation indexes commonly used in models as proxies for soil, crop development and stress conditions but their conclusions are not optimistic, especially in the case of small fields and mixed cropping. Early-warning systems are among major users of this kind of monitoring to detect crop sowing, development stage and harvests and the effects of droughts or floods or population changes. The most active systems are FEWS-NET, FAO-GIEWS, UNEP-DEWA, WFP-VAM and JRC-MARS.

National Crop-monitoring systems based on remote sensing include Brazil’s CONAB, China’s RADI, India’s MNCFC, Morocco’s DMN, Mozambique’s DSNA, Pakistan’s SUPARCO, Senegal’s CSE and Tunisia’s CNT.

A review of the scope, evaluation criteria, current approaches, evolution and organization of crop yield and production forecasting frameworks can be found in Delincé (2015).

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3 Who does what? 3.1. International investments

Many international projects promote the use of remote sensing for agricultural statistics. Rowland et al. (2007) list 66 projects for Africa and the 43 active remote sensing entities located in Africa. This section looks at the more recent projects, which are not listed in the 2007 paper.

In June 2011 the G20 launched the Global Agricultural Geo-Monitoring (Geoglam) and Agricultural Market Information System (AMIS) initiatives with a view to improving crop production forecasts and increasing transparency through a global agricultural monitoring “system of systems” based on satellite and earth-based observations. The Geoglam crop monitor provides a multi-source assessment of crop growing conditions, status and agro-climatic factors likely to affect global production. It covers the four primary crops (wheat, maize, rice and soy) in the main crop-producing regions of the AMIS countries. The assessments, which have been produced since September 2013, are published in the AMIS Market Monitor bulletin.2 Representatives from 30 organizations are participating in the assessments.

The EC-FP7 SIGMA project (Gilliams, 2014) focuses on multi-annual changes such as variations in cultivation practices and extension, reduction or abandonment of agricultural land. Using the Geoglam Joint Experiment for Crop Assessment and Monitoring (JECAM) test sites, Sigma monitors sites in Argentina, Brazil, Burkina Faso, China, Ethiopia, France, Madagascar, the Russian Federation, Spain, Tanzania, the Ukraine, the USA and Viet Nam.

The ASIA-Rice project led by JAXA in collaboration with the Asian Network of Country Agricultural Statisticians (AFSIS) provides monthly crop forecasts for Geoglam (Okumara et al., 2014) covering Indonesia, Thailand and Viet Nam. The project uses the Jasmin software provided by RESTEC to monitor rice growing through six weather indicators derived from MODIS and meteorological satellites.

2 http://www.amis-outlook.org/fileadmin/user_upload/amis/docs/AMIS_brochure

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The Global Monitoring for Food Security (GMFS) project funded by ESA from 2003 to 2013 as part of the Global Monitoring for Environment system focused on Ethiopia, Kenya, Malawi, Mali, Mozambique, Niger, Senegal, the Sudan and Zimbabwe,3 providing seven services: crop yield and vegetation monitoring, FAST precipitation and evapo-transpiration, soil moisture monitoring, support for the national agricultural survey, agricultural mapping, SAR knowledge transfer and support for crop and food supply assessment missions.

The AGRICAB EC-FP7 research project, which started in 2011 and ended in 2015, focused on agriculture and forestry management with a view to improving data access, agro-meteorological modelling, early warning, agricultural statistics, livestock monitoring and forest mapping. These were developed in case studies in Kenya, Mozambique, Niger, Senegal, South Africa and Tunisia. Africa was represented by ILRI, RCMRD, INAM, AGHRYMET, CSE, DRSRS, UEM, OSS, CSIR and GeoSAS, and South America by INPE.

The 2011–2014 E-AGRI EC-FP7 project sought to support the uptake of European ICT research results in China, Kenya and Morocco. Its six activities were crop yield monitoring using the CGMS and BIOMA platforms, crop yield forecasting and area estimation using remote sensing indicators and statistical analysis, and capacity-building.

Funded by the Swiss Agency for Development and Cooperation (Nelson et al., 2014), the Remote Sensing-Based Information and Insurance for Crops in Emerging Economies (RIICE) project tested SAR-based mapping of rice areas in Cambodia, India, Indonesia, the Philippines, Thailand and Viet Nam between late 2012 and early 2014. The main actors were IRRI, the Swiss company Sarmap, PhilRice, ICALRD, TNAU and GISTDA. The mapping was done with X-SAR imagery from ten dates, mostly SLC 3-metre resolution from Cosmoskynet and Terrasar on 13 sites covering an area of 4,780km2. For site-level parametrization of the decision rule algorithm, field measurements were made on eight dates at 20 fields per site. For validation, 100 locations were selected providing 1,338 GPS-located measurement points, of which 50 percent were rice fields and 50 percent areas used for other purposes. At the site level, mapping accuracy varied between 86 percent and 97 percent. Most errors occurred in river zones for which crop masks could be created; omission errors resulted from rice-crop calendars that were unsuited to some problem areas.

3 Documentation can be found at: http://www.gmfs.info/

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The Asian Development Bank’s 2014 Innovative Data Collection Methods for Agricultural and Rural Statistics project is funded by the Japan Fund for Poverty Reduction (JFPR); the technical adviser and development partner is the Japan Aerospace Exploration Agency (JAXA) (ADB, 2014). It will be piloted in the Lao People’s Democratic Republic, the Philippines, Thailand and Viet Nam with a view to promoting the use of satellite technology in formulating and monitoring food security policies. The agencies entrusted with the mandate of compiling agricultural statistics should be the national project implementing agencies. IRRI and GISTDA, which have experience with remotely sensed monitoring of rice, are potential partners.

The 2014–2016 Spurring a Technology for Agriculture through Remote Sensing (STARS) project4 is financed with US$7.5 million from the Bill & Melinda Gates Foundation and led by ITC-Twente in collaboration with CSIRO, ICRISAT, UMD and CYMMIT. The project is “... a coordinated set of activities, designed to learn, identify opportunities and challenges, and test hypotheses around the potential exploitation of remote sensing technology in improving the productivity of crop-based smallholder production systems and livelihoods of smallholder farmers in sub-Saharan Africa and South Asia.”

4 http://www.stars-project.org

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3.2. National remote sensing activities in developing and transition countries

Table 1 below summarizes the findings for the 31 screened countries in this chapter.

Table 1: Main uses of Remote Sensing in the 31 countries analysed

LF = list frame AF = area frame MF = multiple frame Admin = registers OP = operational Exp = experimental

LF&AF Exp Exp Exp

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3.2.1. Argentina

The Ministry for Agriculture, Breeding and Fisheries produces the official agricultural statistics. A new survey system initiated in 2010 consists of an area frame with stratification based on classified MODIS multi-temporal images with an ISODATA algorithm (MAGyP, 2010). The clusters are re-grouped on the basis of their temporal profiles into arable, pasture and non-agriculture classes. By superimposing a hexagonal tessellation of 2,500 ha, four strata are created as functions of agriculture intensity. The sample is obtained by superimposing a regular grid of points on the area frame, each point generating a rectangular segment of 400 ha. For accessibility, the points are moved along the perimeter of an expandable circle to the point at which they come into contact with the road network. In each segment, land uses are drawn on a reference document for further area computation. Additional research evaluates the use of classified imagery for regression and calibration estimations.

3.2.2. Bangladesh

In pilot studies in Bangladesh involving the Ministry of Agriculture, the Bureau of Statistics and the Space Research and Remote Sensing Organization, rice production estimates were made in Aman and Boro regions 2011–2012 (Rahman, 2014) by merging area estimates from satellite imagery with ground measurements of yields. Crop masks of non-agriculture land are derived from Landsat images every three years, and SAR data are acquired on three dates during the growing season in parallel with weekly MODIS data.

3.2.3. Brazil

Agricultural statistics are issued in Brazil by the Instituto Brasileiro de Geografia e Estatistica (IBGE). In addition to the ten-year census based on enumeration areas and interviews – the last was in 2006 – annual agricultural statistics are produced by the Levantamento Sistemàtico de Produçào Agricola (LSPA) survey, in which statistics are derived from expert opinions (Bolliger, 2014). In view of the disadvantages of the LSPA approach, IGBE and INPE implemented the PREVS project from 1987 to 2000 (IBGE, 2005) in which photo-interpretation of Landsat TM images led to the construction, using a NASS approach, of an area frame for the states of Paraná, Santa Catarina, São Paulo and the Federal District. Although results and costs were satisfactory – 66 percent of the estimates had coefficients of variation (CVs) below 25 percent – it was seen mainly to confirm the reliability of the expert estimates, which were often in the confidence interval of the area frame results.

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Additional results come from the new Project of Land Use and Coverage, developed by the IBGE Geosciences Directorate, based on ten classes of land-cover mapping with MODIS images every two months (Freitas et al., 2013). INPE is testing the Monitoring Agriculture by Brazilian Remote Sensing (MoBARS) approach, which uses Landsat-like images in the states of Rio Grande do Sul, Paraná and São Paulo for soybean, corn and sugarcane. Crop areas are estimated every two months during the growing season; in parallel, MODIS data provide crop-layer maps virtually without the need for fieldwork.

In terms of data distribution, Embrapa has created a GIS website called Soma Brazil, in which maps of agricultural themes and the results of IBGE surveys can be visualized. IBGE is considering improving LSPA surveys by constructing a nation-wide master frame that could be based on administrative registers of farmers and family farms.

At the same time, the Companhia Nacional de Abastecimento (CONAB) produces monthly statistical information on areas of cotton, rice, beans, castor oil, millet, soya, oats, canola, rye, barley, wheat and triticale (CONAB, 2014). The Geosafras project is discussed in Da Silva (2013), GEOSS (2010) and Fontana et al. (2008). National multi-temporal MODIS coverage is used for grain areas and yields; Landsat and Spot data are used for coffee and citrus crops.

3.2.4. Chile

The Ministry of Agriculture and the National Statistics Institute organized their last census in 2007. Since 2013, the entire territory of Chile has been covered with an area frame (Ambrosio, 2014) using a stratified two-stage sampling plan. After exclusion of urban areas, rural areas are stratified according to land use and agricultural intensity. The census results were preferred to imagery for stratification due to the complexity of the landscape. Square segments of 25 ha are used, with blocks in which five replicates are chosen systematically. The sampling fraction of 1.25 percent was chosen to achieve a CV of 15 percent on major production sectors at the regional level.

3.2.5. China

In China, official agricultural statistics are the responsibility of the National Bureau of Statistics (NBS). Remote sensing replaced the list frame approach in the grain-producing counties in Liaoning, Jilin, Jiangsu, Anhui, Shandong, Henan and Hubei provinces in 2003. The sampling plan (Pan et al., 2012) uses

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a stratified approach in which a 5x5 km grid is superimposed upon province/county boundaries, images are classified and grids are clustered into three strata as functions of total arable land and the three main crop areas. A sample of 150 grid cells per province is selected by PPS, and five segments are randomly selected per grid cell; replicates are used to estimate accuracy. The Rapideye and Spot satellite imagery used until 2013 has been replaced with Chinese GF-1 imagery, taken with a P/MS sensor at 2/8 metres and a 16-metre WFV sensor.

The Cropland Acreages Estimation using Remote Sensing and Sample Survey final sample size targets a 5 percent CV for major categories at the province level, covering wheat, corn, rice, cotton and soybeans. Of the 226 villages in Beizheng district in Liaoning province (Yu, 2014), for example, 24 were field surveyed in a total of 120 segments using direct expansion estimators giving a CV for rice of 6.9 percent and for corn 5.4 percent; using regression estimators, the CV for rice was reduced to 5.3 percent and for corn 3.7 percent. This corresponds to relative efficiencies of the regression estimator of 2.2 for rice and 3.1 for corn.

The Ministry of Agriculture also uses remote sensing to monitor agriculture. The Chinese Academy of Agricultural Science (CAAS) started the Chinese Agricultural Remote Sensing Monitoring System (CHARMS) in 1999. The system monitors changes in crop areas, yields, production, growth and drought for wheat, rice, maize, soybeans, cotton, canola and sugarcane. It reports the monitoring information to the Ministry of Agriculture and agriculture-management organizations more than 100 times per year.

The CAAS was a partner in the E-AGRI project in which Mengcheng county – 2,149 km2 – and Guoyang county – 2,107 km2 – were the objects of an area frame survey (Chen et al., 2013). The methods tested were physical boundary segments and circular segments: in both, a regular 4 km grid enables the selection of points on Google Earth for photo-interpretation. In the first case, average segment size was 5.9 ha; in the second 4.6 ha; sample sizes were 100 and 193 segments. A CV of 3 percent was obtained for wheat on 148,000 ha in Mengcheng. Classification of a merged SPOT5/Landsat TM image was carried out. The observed relative efficiency for wheat was 2.6 percent, for soybeans 2.7 percent and for maize 2 percent.

Based on the CAAS Institute of Remote Sensing and Digital Imagery, the CROPWATCH system (Wu, 2014a) uses high-resolution images from IRS,

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Landsat, SAR and HJ-1 and medium-resolution images from FY-3A, MODIS and AVHHR to issue forecasts for crop areas, yields and production. Four bulletins per year review the world’s main production zones (Wu et al., 2014b) in terms of the supply and demand balance for rice, wheat, soybeans and maize. Crop area estimation is implemented in Argentina, Brazil, China, France, Germany, the Ukraine and the United States; crop-condition indicators are derived for the 31 main food-producing countries. The team is composed of 15 remote sensing analysts, one agro-meteorologist and a communications officer.

For China (Wu, 2014c), the crop areas are derived as the product of total farmland and the planted proportion with the crop-type proportion. The planted proportion is derived from unsupervised classification of high-resolution satellite images from Landsat TM, IRS P6 AWIFS and HJ-1 CCD, with accuracy above 95 percent. The crop-type proportion comes from thousands of photographs taken along transects – the GVG system. The yield estimate comes from three approaches based on remote sensing indicators or agro-meteorological indicators.

Global Land Cover 30 (Chen, 2014) is a product delivered by the National Geomatics Centre of China (NGCC). It was created for reference years 2000 and 2010, but only the 2010 land cover is publicly available on the web. The classification system includes ten land-cover types – cultivated land, forest, grassland, scrubland, wetland, water bodies, tundra, artificial surfaces, bare land and permanent snow and ice. Derived from Landsat and HJ-1 imagery, the raster land-cover product is rendered with 30-metre resolution and structured into 853 tiles.

3.2.6. Colombia

The frame used by the National Department of Statistics for the national agro-pastoral survey was established in 1985/86 (DIMPE, 2013) following FAO recommendations. It merges a list frame for large farms and an area frame of 6,000 physical-limit segments averaging 200 ha of an estimated total of 179,000 SSUs and 61,733 PSUs. The sampling plan based on PSUs and SSUs is stratified into six strata in 23 of the 33 departments. Stratification and PSU/SSU delineation based on aerial photographs at 1/20 k to 1/50 k were transferred to topographical maps. Samples are extracted, as in Morocco and Rwanda, by replicated PPS systematic sampling of PSUs, with one SSU randomly chosen by PSU leading to two-stage self-weighting sampling. The frame needs adjustment (Saa-Vidal et al., 2012): in addition to doubling the

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sample size to reach the 15 percent CV target at the department level, stratification and segment delineation should be revised in accordance with the latest imagery.

3.2.7. Ethiopia

In preparation for its next census in 2018 Ethiopia used using remote sensing from 2008 to 2013 (Tariku, 2014; Srivastava, 2014) to test an area frame approach in Oromia region – 284,000 km2–. The frame is composed of PSUs – enumeration areas containing 200 households – from the CSA list frame used for crop production surveys, and SSUs – segments of 40 ha. Using SPOT satellite images, the PSUs were stratified as functions of agricultural land use; 239 PSUs were selected with probability proportional to the number of segments and hence population. Two segments were chosen per PSU. The CVs were usually above 30 percent for crops covering 50,000 ha. No use was made of image classification by the CSA.

3.2.8. Guatemala

In 2013, INE conducted a multiple-frame survey to estimate national crop areas and production (Barrientos, 2014). For the area frame, a 1 km2 grid was used in which the squares were subdivided into segments of 6.25 ha, 25 ha, 50 ha or 100 ha, for a total of 190,100 segments. Depending on the stratum involved, the squares were subdivided into segments of 6.25 ha, 25 ha, 50 ha or 100 ha over 190,100 ha. The sample was finally composed of 1,500 segments structured in five replicates. Additional information on the area frame characteristics can be found in Ambrosio (2014). No use of image classification is made in estimating crop areas.

3.2.9. Haiti

The National Network of Points Observation in Haiti has provided annual crop-area estimates at the district level since 2012. Organized by the Centre National de l’Information Geo-Spatiale in collaboration with the Ministry of Agriculture and the Institute for Statistics, the survey consists of multi-purpose field surveys during the two growing seasons and one yield survey for maize, beans, yams, bananas and rice. The area frame was created by superimposing a regular 125x125 metre grid on to a map of the country; the resulting 1,728,000 points were photo-interpreted with the support of IGN aerial imagery. A sample was then drawn by Bethel allocation and 20,000 points were visited on the ground. The 2013 measurements at the national level go from CVs of 2 percent for

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maize on 214,500 ha to 7 percent for rice on 16,314 ha. At the district level, accuracy varies according to the extent of a crop: for maize, CVs go from 5 percent in Centre district, where maize covers 51,000 ha, to 11 percent in North-East district, where the area is only 6,000 ha.

3.2.10. India

Official agricultural statistics produced by the Department of Economics and Statistics integrate estimation based on remote sensing with field surveys. Parihar and Oza (2006) describe a method used until 2012 and explain the roles of state agricultural statistics authorities and the Directorate of Economics and Statistics in the Department of Agriculture and Cooperation.

The Mahalanobis National Crop Forecasts Centre was created in 2012 by the Ministry of Agriculture by transforming the former Forecasting Agricultural Outputs using Space, Agro-Meteorology and Land-Based Observations (FASAL) programme ( Parihar and Oza, 2006) into operational activities (Pariharand and Manjunath, 2013; Ray et al., 2014). State estimates of areas and yields are produced at several dates for rice, jute, rapeseed, potatoes, wheat, cotton and sugarcane, ensuring 90 percent coverage of national production.

The country is covered with a regular square 5 km grid, with each segment classified by satellite imagery; any segment which comprises more than 5 percent of agricultural area is included in the definition of agriculture strata (Parihar et al., 2012). Four crop strata are defined on the basis of crop intensity in the classified images using the Dalenius-Hodges method. For area estimation, a 15 percent sample is randomly selected in each stratum to enable computation of standard errors. Ground visits are used for training in the classification rule, but regression and calibration are not applied. Classified SAR RISAT imagery enables estimation of rice and jute areas (Chakraborty et al., 2013). In the same way, AWIFS and LIS III images lead to estimations for other crops. Crop areas result from the multiplication by stratum of the percentage of pixels classified as the target crop with population segment size and area per segment.

Yield estimates for 11 crops are made either (i) at the beginning of the crop season through regression models linking vegetation indexes and remote sensing or (ii) during the growing season with DSSAT biophysical models and remote sensing spectral models integrating low-resolution imagery from INSAT-3 and meteorological information (Tripathy et al., 2013).

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3.2.11. Indonesia

Indonesia has been testing point area frame sampling since 1998 (Mubekti, 2014) to determine rice areas in different stages of development; 25 ha square segments have been used since 2005 as PSUs, with four grid replicates per 10x10 km block and with SSUs located on a 5x5 grid. The aim is to observe phenological stages of rice at each sample point with a view to estimating the area of rice at each stage. Stratification results from maps of land used for rice, arable upland and non-arable land and imagery enables the creation of field documents on a scale of 1/5,000. PSUs are chosen (f=1 percent) on a square grid, and sample size is set to produce estimates at the district level. In Indramayu district – 2,000 km2 – the 2012 survey was based on 54 segments with 1,300 points observed and completed within a week including SMS transmissions and real-time revision of the estimates.

The Ministry of Agriculture and the Presidential Office for Development Monitoring are among the institutions supporting use of the method for national-level implementation in 2015.

3.2.12. Kenya

The Department of Resource Survey and Remote Sensing has provided annual statistical estimates of crop areas and yields for the Food Security Steering Committee (FSSC) since 1984. The approach, described in Imata (2013a), involves aerial surveys of points to estimate areas of maize and wheat. Stratification covers approximately 14,500 km2 annually on the basis of Landsat imagery from 2007 and the aerial surveys: about 10,000 colour photographs scaled at 1/22,000 with GPS localization are interpreted on a 100-point grid to estimate the wheat and maize areas. Three months before harvest, crop radiometry is collected in a second aerial survey to predict yields through regression equations of yield versus vegetation indexes.

The Department of Resource Survey and Remote Sensing has joint the AGRICAB FP7 project (Tote et al., 2013) to seek technical improvements. Following a LUCAS approach (Gallego and Delincé, 2010), 10,000 points have been photo-interpreted on Google Earth, leading to 615 field visits in 2012 in Kakamega and Batere Mumias (Imata, 2013b). Rapideye 5-metre resolution images are used for crop classification, but results were discouraging as a result of the high variability of crop development. In addition, the Regional Centre for Mapping of Resources for Development (RCMRD) was associated with the

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GMFS ESA project (Gilliams et al., 2012), whereas CSE was part of the E-AGRI EC project (de Wit et al., 2013) on crop yield forecasting

Independent crop-production estimates are produced by international initiatives:

• FEWS-NET issues monthly reports, but according to Brown and Brickley (2012) they rarely refer to NDVI vegetation indexes.

• MARS issues periodic maize production forecasts (Rojas, 2009) based on analysis of CNDVI indexes of SPOT vegetation.

• The Regional Centre for Mapping of Resources for Development in Nairobi supports its 20 member states in eastern and southern Africa with problem-solving and natural resource and environmental applications, integrating remote sensing, surveying and cartography.

3.2.13. Malawi

The Ministry of Agriculture collects the agricultural statistics through its Agriculture Production Estimation Survey system (APES), composed of three parts: field crops production estimation, horticulture crops production estimation, and livestock production estimation.

Based on a farm household list frame, from February it delivers four estimates of the areas and production of the major crops; however, the current paper approach does not enable survey precision to be derived.

Malawi is a member of the Nairobi Regional Centre for Mapping of Resources for Development, and has trained 18 people in remote sensing and telecommunications between 2000 and 2005 (Rowland et al., 2007). After tests of a list frame approach to estimating crop production in 2011, the ESA GMFS project developed an area frame for the whole country in 2012 (Ceccarelli and Remotti, 2012). A grid set of 350,000 points 500 metres apart was photo-interpreted on Google Earth imagery and stratified into five land-use classes. In a field survey in Dowa and Ntchisi districts – 5,000 km2 – 74 percent of the points were reached in six weeks by 20 Ministry of Agriculture surveyors, leading to CVs for the five main crops of between 3 percent and 20 percent. SPOT imagery had an overall accuracy of 41 percent, insufficient for sound image classification.

A survey in Central region in 2013 using the national area frame covered 4,500 points. Partial results for September 2013 (Ceccarelli et al., 2013) show that in

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two districts for which 70 percent of the survey data were available, maize areas had CVs of 9 percent and 15 percent.

The World Bank financed two agricultural survey projects in 2015 for the Ministry of Agriculture. One (ITA-EFTAS 2015) consisted in a national point area frame survey; the results are not yet fully available, but technical details are given in section 6.3.1. The other project (ASTRIUM 2015) consisted in an estimation of corn acreages and yields at national and district level using SPOT imagery.

3.2.14. Mexico

The National Institute of Statistics and Geography produces the official agricultural statistics. The last agriculture census took place in 2007; it was followed in 2012 by an agriculture survey of 33 products based on 97,000 holdings (Pérez Cadena, 2013). PDA devices were used to facilitate data collection and access satellite imagery during interviews to compare crop plot polygons defined by farmers with areas plotted by GIS.

SIAP and SAGARPA run projects to integrate remote sensing technologies into agricultural statistics. With the creation of ERMEX-NG by SAGARPA and SEDENA in 2013, Mexico now has a SPOT reception station. Salvidar and Sanchez (2014) describe the main activities in agricultural statistics as:

• cropland mapping based on 600,000 images in ten years of archives; a land-use map has been created for the whole country;

• crop area estimation: in 2010, 2011 and 2012, areas under maize, wheat, sorghum, sugarcane and beans were estimated by integrating ground data with supervised image classification; results are available at the state level;

• checking that areas are sown with maize, sorghum and beans according to subsidy agreements; 5,642 fields were checked in 2014 with remote imagery;

• and area frame sampling using satellite imagery, starting in 2007.

3.2.15. Morocco

The Division of Strategy and Statistics (DSS) of the Ministry of Agriculture renewed the national area frame in 2008 on the basis of SPOT-5 imagery (DSS, 2011; Arrach et al., 2014; Ballaghi et al., 2014,): the sample is now composed of 3,000 segments of 4 to 30 ha for arable and permanent crop strata and 200 ha

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for forest and pasture strata. Using SPOT5 imagery, all provinces were stratified in ten strata; bare soil and water strata are not sampled. The strata were subdivided into square PSUs: agricultural strata have PSUs from 500 ha to 4,000 ha. Each PSU is subdivided into SSUs; the initial squares were changed to irregular polygons to cover intersections with strata boundaries, and during field visits the segments are expanded to include entire intersected crop plots. The PSUs are selected with PPS – the number of theoretical segments – and the SSUs from randomly chosen replicates. Although the DSS does not use current-year imagery to classify crops, it participated in the E-AGRI FP7 project (Mahyou et al., 2014), in which Landsat 5 and Landsat 7 were used in the 2007, 2011, 2012 and 2013 marketing years. For rain-fed crops strata in an area of 18,000 km2, cereal areas were extrapolated from the classified segments, with accuracy of the order of 95 percent.

The National Meteorological Division has adapted the JRC CGMS approach of the E-AGRI EC project for crop yield forecasts (De Wit et al., 2014). CGMS-Morocco includes the DMN, INRA and DSS institutes, which issue bulletins every two months based on meteorological and satellite information.

3.2.16. Mozambique

Agricultural statistics are the responsibility of the Department of Agriculture Statistics in the Ministry of Agriculture (Amade, 2012). The agricultural censuses in 2000 and 2010 were interspersed with agricultural surveys. The ministry’s early-warning System issues periodic bulletins on seven crop areas, yields and production. The first international project involving Mozambique was the ESA GMFS project: some training took place but little else, and collaboration in the AGRI-CAB EC project was agreed to reconcile annual list survey estimates and early-warning system forecasts. Tote et al. (2013) describe the current forecasts systems: the Ministry of Agriculture’s Trabalho de Inquérito Agrícola or TIA and Aviso Prévio, INAM, MARS, FEWS-NET, GIEWS and SADC-AMESD. With regard to agricultural statistics, AGRI-CAB and the Ministry of Agriculture implemented a point frame survey in Inharrime district – 2,149 km2 – in 2014 (AGRICAB, 2015) using a 500x500 metre grid and STAT-AGRI software for stratification, sampling, estimation, image classification and crop association analysis. The methodology adopted was that described for Senegal (see below). The 11,000 points area were stratified on Google Earth into ten land-cover classes, and resulted in a sample of 980 points. The CV obtained for cassava, the most widely grown crop covering 12,000 ha, was 11 percent. Comparison with official statistics revealed

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discrepancies because information was available only at the province level, and the AGRICAB survey was late, so many fields were erroneously allocated as fallow land.

3.2.17. Nepal

Nepal conducted its 2012 agriculture census on the basis of a list frame derived from the population census (Bashyal, 2014): farmers on 130,000 holdings selected from 5,200 PSUs were interviewed. The sampling fraction per PSU was defined as the function of the number of agro-holdings obtained in the population census. No use of area frame or imagery was used.

3.2.18. Nicaragua

An area frame was created in 1995 by USDA-NASS in collaboration with the Ministry of Agriculture and the Central Bank of Nicaragua, UNDP and USAID (Hoffman, 2014). In view of the cost of satellite imagery at that time, stratification was based on topographical maps and identified intense, moderate and low agricultural land use, urban areas and non-agricultural land. A sample of 5,600 points was randomly selected across the national territory of 130,370 km2 to give a sampling probability proportional to farmland. Observed CVs were a function of the extent of the crop areas, enabling the estimation of corn areas with a CV of 3 percent at the national level. No follow-up took place.

3.2.19. Nigeria

In 2010, Nigeria tested with USAID support an agricultural area frame survey in the 44,000 km2 Kaduna region (Hoffman, 2014). Using SPOT-5 imagery, the stratification – intense, moderate and low agricultural land use, non-agricultural and urban land and water – used an intermediate level of blocks with physical limits. A sample of 600 points was randomly drawn, with various sampling fractions per stratum. At each point, the farm was surveyed for crops, livestock and fisheries areas and production: the overall results indicated that a slightly larger sample size would be needed to produce reliable statistics with 10 percent CV for the top ten crops. Taking an average farm size of 5 ha, the sample accounted for 0.1 percent of farmland. Political instability prevented a repeat or extension of the project.

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3.2.20. Pakistan

Official crop statistics in Pakistan are generated through a village list frame survey. The parallel Agricultural Information System, started in 2005, supports the Government in integrating remotely sensed data into existing data collection, analysis and dissemination systems and promotes the combination of satellite data and improved field estimates for crop areas and yields and to monitor crop status to improve the accuracy and timeliness of agricultural statistics (Ahmad, 2014). SPOT 5-metre imagery was used to create a national area frame. After elimination of non-agricultural areas, Sindh and Punjab provinces were divided into PSUs of 1,000 ha, each of which was assigned a stratum label based on crop intensity, SSUs of 400 ha and TSUs of 30 ha; the sample size was 314. Province-level crop area estimates are derived as the sum of strata averages weighted with the number of population segments for each stratum. The survey covers wheat, rice, cotton, sugarcane and sunflowers.

The accuracy of subsequent classification of multi-date Landsat coverage was too low for the use of a regression estimator or to create a CDL (Wigton, 2012), but the current classification of bi-temporal SPOT images is promising in that regression between field and classification wheat percentages gives r2 above 0.95. Yield estimates are derived from NDVI profiles and periodic e-bulletins are published.5

CVs of 3 percent to 5 percent are obtained for major crops, and values of 7 percent to 10 percent for mixed cropping. Results are issued in April and August. The annual costs of the project, which involves 18 staff, amount to PKR30 million; the CRS departmental costs for the surveys, which involve 3,500 staff, reach PKR700 million.

3.2.21. Peru

The 2000 agricultural census was based on an area frame consisting of PSUs of 10 km2 and SSUs of 2 km2 systematically sampled with replicates (Otànez, 2004). Stratification was based on Landsat imagery and aerial photographs. Visits were made to 12,277 physical limit segments and the farmers were interviewed. Of these segments, 20 percent had to be subdivided to accommodate the large number of fields in each. A list frame of large holdings was used to create a multiple frame on the basis of which an annual survey sub-sample of 2,000 segments was designed, with a vi141ew to calibrating the

5 See: http://dwms.fao.org/~test/ais_ebulletin4_en.asp

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annual survey against the census with ratio or regression estimators. The current ENA annual production survey (INEI, 2014) appears to make no reference to the calibration approach.

3.2.22. The Philippines

The Bureau of Agricultural Statistics first tested the use of remote sensing in the 1980s (Garcia and Esquivias, 2012) in Pangasinan province. Rice and corn crops were the subject of an area frame survey of 72 segments of 25 ha using Landsat imagery, but results were inconclusive. Further tests were carried out in 2006 in preparation for the 2012 census. Quirino municipality in Isabela province was surveyed, using stratified point sampling based on Landsat and Google Earth imagery. The system was evidently practicable, but was not adopted because of the cost and irregular availability of the imagery, small field size, mixed cropping and asynchronous cropping.

The agricultural statistics currently produced by the bureau are based on a list frame, but pilot remote sensing projects continue, as described in the next two paragraphs.

The ADB Innovation Project covering the Lao DPR, the Philippines, Thailand and Viet Nam encourages statistical offices to use remote sensing to supplement data from surveys or administrative reporting systems. Operations started with estimations of rice areas and production. The user-friendly software developed by the Japan Aerospace Exploration Agency and RESTEC converts optical and radar data into rice maps and area estimates. The project also uses results from crop-cutting experiments and growth models with a view to estimating rice yields and output.

The four-year PRISM project, under the Food Staples Sufficiency Program, is supported by the Department of Agriculture, the Philippines Rice Research Institute, Sarmap and IRRI. It aims to establish a nationwide information system that will provide timely and accurate data on rice cultivation and harvests in each province, and serves as a platform for developing consistent assessments of rice production, crop health and losses due to natural disasters and epidemics. Using SAR radar imagery, PRISM is operational in seven regions, where municipalities and rice fields are selected for ground truth.

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3.2.23. The Russian Federation

Agricultural statistics are produced by the Russian Federation Statistical Service.6 For the 2006 agricultural census, the IKI Space Research Institute7 was contracted to assess the use of MODIS and Landsat imagery. In 2013, a second contract with IKI was signed to process the southern regions of Semikarakorsky district in Rostov oblast (province) and Liskinsky district in Voronezh oblast for the 2014 census, which was postponed to 2016. The deliverable was a land-use agricultural map showing arable land, hay, pastures and gardens, fallow and abandoned land at a scale of 1:50,000 based on the Landsat and MODIS archives up to 1988 and Russian satellite images of 2.1-metre resolution. Following a feasibility test at the regional level comparing census results with the land-use map, the IKI recommendation was to extend the product to the whole country and to use it during the interviews to eliminate obvious errors as to crop areas and land use. However, because of budget restrictions and the complexity of the remote sensing, the approach will not be used in the 2016 census.

The Ministry of Agriculture also has contracts with the Centre for Monitoring of Agricultural Land for remote sensing to monitor agriculture.8 Agricultural areas are not estimated, but agricultural field limits have been delineated for most of Russia, although without classification, using Ikonos and Rapideye imagery. 9 Land-cover maps have been produced, and validated through field work.

Crop yield forecasts are produced by the Hydro-Meteorological Centre. The IKI NDVI profiles available in the VEGA-Geoglam platform are part of the process.

Vega-Geoglam10 is part of the G20 Geoglam initiative and financed by the FP7 SIGMA project. It covers the whole of Eurasia and test sites in Argentina, Belgium, Brazil, Burkina Faso, China, Ethiopia, France, Madagascar, the Russian Federation, the Ukraine, Tanzania and Viet Nam. The website provides access to daily and archived satellite imagery from the Modis, Landsat and Russian satellites, land-cover maps showing arable land, winter crops and summer crops, and field-level information such as biomass. The tools provide

6 http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/en/main/ 7 http://www.iki.rssi.ru/eng/ 8 http://www.rosagroland.ru/about/ 9 http://atlas.mcx.ru/ 10 http://vega.geoglam.ru/

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for supervised classification of Landsat with Modis land-cover maps. The Russian beta version provides tools for image classification by oblast. The tools have been adapted for monitoring forest resources and contain images for all sites. From a research point of view, JRC (Fritz et al., 2006) compared the costs and accuracy of various classification methods for vegetation, Modis and Meris data for part of the Rostov oblast.

3.2.24. Rwanda

The National Institute of Statistics collaborates with the Ministry of Agriculture and Animal Resources, the Rwanda Agricultural Board and the National Agricultural Export Board. The agricultural statistics system (Manzi, 2014) consists of a programme of seasonal agricultural surveys based on multiple frame area and list sampling methods and an agricultural reporting system based on a network of experts organized at the district level.

The area frame is composed of 10,560 PSUs in the five strata sampled – the seven non-agriculture strata are not sampled – and 8,556 physical limit segments in the 540 PSUs sampled. The sampling rate varies according to the importance of crops and livestock and expected variances; 25,000 tracks – the part of a field falling within a segment – are surveyed. Segment size is 10 ha except for the rangeland stratum, where a 50 ha size was chosen. PSUs are selected in a systematic manner, with probability proportional to size (PPS). This is achieved by ordering potential SSUs by the PSUs arranged according to their proximity. By systematically selecting four sets of SSUs, a PPS selection of PSUs is achieved. The selected PSUs are then subdivided into SSUs, from which one is selected at random.

The approach uses ortho-rectified images at 2-metre resolution and colour imagery at 25 cm resolution to enable stratification and the delineation of PSUs and segments. At the national level, the precision of the 2014 estimates varied by between 5 percent and 15 percent according to crop extent.

3.2.25. Senegal

Ten-year censuses – the latest was in 2013 – are interspersed with annual list frame production surveys and a structure survey every three years, all carried out by DAPS. Of 500,000 rural households, 6,300 are surveyed (f=1.3 percent) in 900 of the country’s 9,000 districts, each equivalent to a PSU.

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Senegal is part of the AGRI-CAB project, which was implemented in an administrative unit of 2,500 km2 in collaboration with the Centre de Suivi Ecologique, which has had a satellite receiving station since 2005; DAPS is the final user (Tote et al., 2013). The area point frame was obtained by overlaying a 500x500 metre grid over the territory of interest. Stratification into six classes was derived from photo-interpretation of Google Earth imagery covering 10,000 points. The sampling fraction was 10 percent and was systematic by bloc with non-aligned replicates. Field documents were printed from the satellite imagery – Rapideye 5-metre in Senegal and Kenya, Google Earth in the other countries. In Senegal, locally recruited surveyors using hand-held devices fed a central database in real time. Although the CVs for the main crop areas were below 5 percent, the project reports relative efficiency of the stratification close to 1 as a result of the multi-purpose objective, and relative efficiency of the use of satellite imagery also close to 1 because the same crop can be at any stage from sowing to harvest at any location. In view of the US$3,000 cost of a Rapideye image and the €20 per day paid to field workers, the cost-efficiency of remote sensing is questionable.

The CSE produces bulletins based on NOAA rainfall estimates and vegetation data, and uses SPIRITS to analyse the stage of the season to identify the year most similar to the current year to forecast yields at the end of August, when vegetation stops growing, and to estimate biomass. The bulletins advise pastoralists as to movement to minimize overgrazing. Fires are monitored on the basis of MODIS data.

3.2.26. South Africa

Statistics SA produces agricultural statistics through list frame surveys covering commercial companies active in the field. Its crop area and production estimates therefore do not cover smallholder farmers. Another recurrent problem is delays in their availability.

In 2000, the Department of Agriculture, Forest and Fisheries established the Crop Estimates Committee, which is responsible for official annual estimates of crop areas and yields. Statistics SA is one of its six members. The approach used is described in Beukers (2014). Among various data sources, the committee uses data provided by the National Crop Statistics Consortium, established in 2002 and consisting of the Agricultural Research Council, GeoterraImage Ltd and SiQ Ltd.

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Its first activity was a subjective area frame survey consisting of visits or telephone contact with farmers on the basis of an area frame established through classification of Landsat 5 images. Blocks of 5x5 km were classified for stratification into classes of agriculture intensity. Samples were defined by overlaying a 225-metre square grid and randomly selecting a subset of points to vary sampling intensity by stratum. Data were collected at the point, field and farm levels, but decreasing farmers’ answer rates posed problems. Objective yield measurements were realized through a PPS sub-sample.

The Producer Independent Crop Estimates System project started in 2005. On the basis of Landsat and SPOT images, all agricultural field limits are digitized. A 22.5x22.5 metre square grid of points is overlaid and samples are randomly selected from the points falling within the digitized fields, with various sampling fractions for the strata produced from the Landsat 5 imagery. Fieldwork involves an aerial survey to identify land use and land cover. A slow-flying aircraft uses point GPS coordinates as waypoints, and the surveyor registers crop types on a hand-held device, showing field boundaries as background. If the annual Spot5 composite imagery is subsequently used to update field boundaries, Landsat 8 multi-temporal coverage is used to track changes in crop cultivation trends or sudden changes in crop rotation. The information serves to calibrate the Landsat images. Crop types are classified at the end of the summer season for the previous summer and winter. The classified areas give additional credibility to the Producer Independent Crop Estimates System results, especially when changes occur such as the current switch from sunflowers to soybeans.

3.2.27. The Sudan and South Sudan

Between 2006 and 2010, the EC-funded Sudan Institutional Capacity Programme: Food Security Information for Action established a land-cover map for the Sudan and South Sudan (Latham 2011, 2012). Based on Landsat, Spot, IRS and Aster imagery, land cover was divided into seven classes after field validation, covering covered 23.7 million ha in the Sudan and 2.8 million ha in South Sudan. Much of the work was carried out to detect deforestation; no agricultural statistics were collected.

3.2.28. Tanzania

The 2012 census, which included an agriculture module, used an enumerative area approach. With USAID support (Hoffmann, 2014), an area frame is under

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construction; it is expected to use a stratified point sampling approach, as in Nigeria.

3.2.29. Thailand

In collaboration with JAXA, GISTDA estimated rice areas in the rainy season of 2011/12 using ALOS Pulsar, Radarsat2 and THEOS data (Rakwatin, 2014; Okumara et al., 2014). A complete coverage of Thailand with 2009 ALOS 100-metre resolution ScanSar data was analysed to provide an accuracy of 70 percent in half of the 76 provinces. Tests with ALOS and THEOS on rice-area data in Khon Kaen province achieved 98 percent accuracy; in Suphan Buri, accuracy was 76 percent. The KKU crop model based on MODIS and Meteo ground-sensor information enables the estimation of yields and the derivation of rice production.

3.2.30. Tunisia

Agricultural crop statistics are derived from surveys of agricultural campaigns, cereals, farm structure and oases (SDSA, 2001). The annual agricultural campaign survey relies on an area frame composed of stratified physical limit segments and a list frame of state-owned land. Administrative units are stratified using topographical maps – aerial photographs from 1985–1989 were used for the structure survey – and the area is divided into homogeneous segments whose size decreases with the intensity of agricultural use, from 200 ha to 2 ha. Samples are selected at the segment level with equal probability in two independent selections; sampling rates are 4 percent for the arable land and permanent cropland and 2 percent for woodland and bushland. Photographs are only used to facilitate the work of field surveyors.

With regard to cereals (Medfai, 2014), the annual survey is based on 4,000 parcels in segments containing cereal fields. The National Remote Sensing Centre has launched a SCAT project in which square segments are visited and classified on Deimos and Landsat images. Relative efficiencies of between 1.8 and 3.2 have been observed. The Ministry of Agriculture commissioned CNCT to pilot wheat and barley monitoring over most of the country during the 2012/13 (Haythem, 2014). Satellite data on vegetation are also used. Monitoring bulletins issued from March to June present areas and production forecasts based on NDVI indexes.

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3.2.31. Turkey

The AGRIMONIS project (Küsek and Üsründağ, 2014), a joint venture of the Ministry of Agriculture and the Statistical Office, aims to double the value of the agriculture sector – currently US$120 billion including the food chain. Relying on a team of 1,000 technicians and a super-computer (200tflops), the system integrates satellite imagery from 50 cm resolution upward, some 1,200 robot stations, an agro-phenology grid with 1 km2 resolution and an agriculture database of 28 million parcels. The system, which is intended to serve district administrations and the private sector, from farmers to traders, will predict crop yields and has been tested for crop area estimation through classification of satellite imagery and calibration with ground survey data.

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4 The Cost-Efficiency Literature Methods of cost benefit analysis are described in Renda et al. (2013); standard approaches have been adopted by major institutions such as the European Commission (EC, 2008). However, while such an approach may be relevant when launching a new survey, as we shall see, in the field of agricultural statistics cost efficiency is rarely considered, though a chapter is devoted to survey costs in UNSTAT (2005) along with case studies to which we refer in chapter 5.

A literature review on the cost effectiveness of remote sensing for agricultural statistics produced for FAO (El Hadani and El Arrach, 2013) considers the use of remote sensing in stratification, area frame construction, calibration, estimation and forecasting, and describes experiences in FAO and in China, Brazil, the European Union, India, Italy and the United States, which use the standard relative efficiency method (Carfagna, 2001).

Other sources of information are available, but financial issues are not well documented. Tenkorang and Lowenberg-DeBoer (2008) state: “Out of the hundreds of agricultural remote sensing documents reviewed, only a few reported economic benefit estimates. Many of those documents do not provide details on how the economic benefit was estimated. Clues in the reports and the fact that the numbers are often much larger than those for detailed studies suggest that the studies not reporting details are often reporting gross benefits without deducting the associated cost. Standardizing budgeting methods and using the reported changes in yield and input application in 12 studies, remote sensing is estimated to have the potential to improve average farm profits by about US$31.74/ha Most of the studies based profit estimates on a single crop season of data. Key improvements needed for studies of the economics of remote sensing for field crops include: detailed reporting of budget assumptions, multiple year data sets in the same fields, and replication of studies of the same technology in different states.”

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Chapter 12 of Benedetti et al. (2010) deals with the accuracy, objectivity and efficiency of remote sensing for agricultural statistics, but only one paragraph is devoted to cost efficiency and the references cited were published over 15 years ago.

Some United States agencies have published analyses of the value of their imagery. The National Geospatial Advisory Committee (NGAC) paper titled “The Value Proposition for Landsat Applications – 2014 Update” notes that Landsat imagery is the most used by government agencies and non-commercial entities, and emphasizes the challenges involved in ascertaining the value of Landsat products, but also refers to the alternative replacement costs and opportunity costs as possible means to obtain a value estimate. The paper establishes that the economic value of a single year of Landsat data far exceeds the cost of building, launching and managing the satellites and sensors. The economic benefits of Landsat data for 2011 are estimated at US$1.7 billion for users in the United States and US$400 million for international users. Among the 16 applications considered, agriculture appears twice: the World Agriculture Supply and Demand Estimates used Landsat images worth US$3 million, and the NASS CDL imagery base is valued at US$4 million.

The United States Geological Survey (USGS) evaluated the economic value of imagery (i) by running partial equilibrium models to derive the additional net present value provided by imagery of corn and soya in Iowa (Forney et al., 2012; Raunikar et al. (2013) and (ii) by applying the contingent valuation method (Miller et al., 2012). The value of such information in Iowa is assessed in as US$858 million per year. The contingent valuation method involved asking 6,500 respondents if a Landsat image was worth a particular amount between US$10 and US$10,000, and according to the response the question was repeated with a value increased or decreased by 25 percent. On the basis of the average value obtained and the number of images downloaded, the value of Landsat imagery in 2011 was calculated at US$2 billion. Of the nine proposed spheres of application, agriculture (agricultural forecasting, agricultural management) was third most significant with 7 percent, after environmental sciences (48 percent) and land use-land cover (25 percent). Vadnais and Stensaas (2014) report that the USGS evaluated the national land imaging requirement by using the “value tree” method in which experts are asked to state the aims and objectives of their organizations and the associated products and services. Each product is assigned an “impact score”, which is revised to balance missing inputs. Agriculture, one of the 11 applications studied, was the sector most affected by non-availability of medium- resolution imagery.

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With regard to the social benefits of Landsat, a 2013 paper by the Space Study Board noted: “agricultural forecasting and management – the United States Department of Agriculture uses Landsat data to monitor global crop supplies and stocks to forecast shortfalls or gluts of various crops on the market. The multimillion-dollar United States agricultural commodities market relies on these crop predictions when conducting futures trading. These important functions benefit [national] food and economic security as well as security”.

Green et al. (2007) state in a White House report that 38 percent of 1,295 ASPRS survey participants considered that it would not be possible to deliver the same level of service without Landsat instruments, and quantified the economic losses caused by non-availability of Landsat imagery at US$935 million annually.

Nelson et al. (2007) studied the use of Landsat remote sensing by five USDA agencies: the RMA, NASS, the Foreign Agriculture Service (FAS) in support of the World Agriculture Outlook Board (WAOB), the Forest Service and NRCS, noting the reasons why anticipated social benefits were not delivered. Their aim was to orientate future Landsat missions to maximize the economic benefits for agricultural users. They cite ECON Incorporated (1974) in estimating that the minimum annual incremental benefits of multi-spectral scanner (MSS) data in the United States would be US$19.6 million from the wheat forecast, US$106 million in wheat and soybean consumer benefits, and US$53 million from the accurate world wheat forecast. The reasons why these figures were not achieved include poor image resolution, limited acquisition capacity, non-availability and delays, lack of cost-effective processing capacity and the cost of the products – US$4,500 for a Landsat 5 image in the 1990s. The Risk Management Agency has won insurance fraud cases on evidence provided by Landsat TM 5 and 7: for 2005, it claimed a return-on-investment ratio of 8.1, with US$34.4 million in restitutions or forfeitures and US$3.8 million in USDA satellite image archive subscription fees. Three activities involve NASS – construction of area sampling frames, crop-area estimation in ten states, and CDLs – and although no economic value is proposed, the relative efficiency of remote sensing is reported as often reaching 3; it is not used at state level, at which results are published in end June; it is however useful at county level, at which results are released early in the following year, after the harvest season. The accuracy of CDLs is reported at over 90 percent. The effects of remote sensing on the USDA world agricultural supply and demand estimates produced by the WAOB are quantified in terms of a 1 percent decline in the volatility of market prices for wheat, corn and soybeans (Isengildina et al., 2005).

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Hanuschak et al. (2001) and Hanuschak and Mueller (2002) note that crop area estimation using Landsat MSS data started in 1975 at a cost of US$750,000 for a single state, which by 1978 had decreased to US$300,000. In the 1980s, NASS was operational in eight states, and costs declined to US$150,000 per state. With the introduction of Landsat TM, NASS decided in 1988 to concentrate on research and to discontinue crop area estimation by remote sensing. Crop area estimates restarted at the end of the 1990s with CDLs, which are more informative than crop areas, at a cost of US$75,000 per state in 2000; in view of the costs, NASS used IRS as a backup rather than SPOT. Details of users of CDLs are given by Mueller and Harris (2013).

Van Leeuwen et al. (2011) focus on the FAS in considering the changes brought about by remote sensing imagery in their decision-support system for producing assessments of global crop conditions and monthly estimates of planted areas, yields and production for select commodities such as soybeans, wheat, corn, rice, cotton and oilseeds. They used NASA-JPL benchmarking to estimate changes occurring in the Decision Support System due to the assimilation of the information from the satellites. The benchmarking was carried out by defining objectives (five main objectives and nine sub-objectives), the definition of risks and mitigating measures and scores, to derive a matrix of the rate of achievement of objectives as a function of data supply, and claim that by adding costs: “This benchmarking approach could also provide insights to the US Office of Management and Budget about how investments are used and aid decision making that result in improved and independent global commodity production intelligence that equally benefits US and world societies.”

The GMES Copernicus programme examined the potential of Earth observation and Copernicus downstream services for the agriculture sector (Spacetec, 2012). It considers precision farming under the assumption of guaranteed long-term availability of free products. It quotes costs of €5/ha and gross benefits of €10/ha for farmers, which can be compared with production costs of €150 per ton for cereals. Extrapolating the €5/ha unit cost to the area that could be managed with Earth observation services, the current agricultural downstream service value of €35 million is estimated to grow to €400 million with GMES services.

A few authors discuss the costs of remote sensing applications for agricultural statistics. With regard to the former PREVS study in Brazil, Bolliger (2014) notes that the central remote sensing team had 14 technicians and 11

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statisticians, with four fieldwork coordinators, 20 supervisors and 200 enumerators. Additional costs related to topographical and land-use maps, Landsat and aerial photograph prints, Intergraph workstations and DB2 and SAS software.

Latham (2015) refers to two costs in the SUPARCO projects in Pakistan: i) the decision to start the project arose from the export of 458,000 tons of wheat in 2007 based on a false assessment of the harvest, with subsequent re-importation at a cost of US$113 million; and ii) comparison of the area frame and Crop Reporting Service approaches shows that the latter costs US$7 million and requires 3,500 staff, whereas the SUPARCO survey costs US$300,000 and needs 18 staff.

Other sources of information are the ESA and European Commission GMFS, AGRICAB and E-AGRI projects. Limited cost-benefit information is given for GMFS because it is a pilot, so costs are not final, and because such information could be useful to competitors. The GMFS project in Malawi is described in Ceccarelli and Remotti (2012), that in the Sudan by Haub and Brockmann (2012). In considering the E-AGRI project in China, Dong (2014) notes that the cost of a survey is €75 per segment and that a SPOT image costs €3,500, concluding that the achieved relative efficiency of 2.6 corresponded to 135 segments, and use of imagery in that instance was therefore cost-efficient.

The concept of relative efficiency applied to the added value of remotely sensed information is a way of examining the reduction of the size of field survey samples. We consider two publications from an extensive literature.

Carfagna (2001; 2013) formulates the concept and explains that for crop area estimation imagery can be used at the design and estimation levels.

At the stratification level, relative efficiency, expressed as variance without stratification/variance with stratification, depends on the complexity of landscape and the diversity of crops. If large non-agricultural zones can be identified in an image, allocation of a reduced sampling fraction will enable a drastic reduction of sample size. But for multi-purpose surveys, stratification will be less efficient: the large corn-, wheat- and soya-growing areas in the United States, for example, are easier to stratify than farming areas in Europe, where ten or more crops compose the arable land. In all cases, it is essential to vary sampling rates between strata to achieve efficiency.

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The second element in the cost-benefit analysis of stratification is the cost of the technology. Three opportunities allow for cost reductions:

• Whereas Landsat imagery used to be expensive, NASS now has access to free Landsat (since 2007) and Sentinel 2 (since 2015) imagery for stratification purposes. Today, images can be handled on personal computers using open-source software, and a programme that automatically stratifies manually defined physical boundary segments eliminates the need for costly human photo-interpretation (Boryan et al., 2015).

• Land-cover maps are now widely available. Teluguntla et al. (2014) identify four – Thenkabail et al. (2009), Pittman et al. (2010), Friedl et al. (2010) and Yu et al. (2013); to these we add the ESA Globecover database (Defourny et al., 2011). In the most recent map, resolution is down to 30 metres. Such data sets are far from perfect for stratification because they usually contain only one class of agriculture, but concentrating the sample on that class is an inexpensive way of reducing sampling variance.

• If a point sampling approach is adopted, stratification can take place on a grid of points defining a plane square tessellation. Given that the point is representative of the square, classification into strata can be done by photo-interpretation of Google Earth imagery (Ceccarelli and Remotti, 2012). Our experience in the LUCAS survey showed that a photo-interpreter can stratify 800 points per day, which provides an easy and cost-efficient solution.

With regard to estimation, calibration methods can be used to integrate ground survey results and image-classification results (Benedetti, 2014). The most common approach – the regression method – has been applied in US, EU and international projects for the last 25 years. Relative efficiency is a function of the crop-specific correlation between ground survey segment crop proportions and the corresponding classified image segment proportions. Relative-efficiency values between 1.5 and 3.0 are commonly reported: their cost efficiency depends on the cost of field surveys and the cost of acquiring and interpreting imagery. Giovacchini (2015) notes that in Africa crops are usually mixed, and a crop in a given area can be at any stage of development from sowing to harvest. This drastically reduces the potential cost efficiency of remotely sensed data – considering that the accuracy of classification in such projects is only 40 percent. In the AGRICAB project in Kenya and Senegal the cost of acquiring a RapidEye 5-metre resolution image was equivalent to 150

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days’ wages for a field surveyor. It is difficult to achieve cost efficiency in such circumstances, even if Landsat-8 imagery can be downloaded free; as their spatial resolution of 30 metres is in any case far too large for the field sizes encountered in African regions.

Gallego (2014) provides an exhaustive bibliography on relative efficiency analysis in estimation, and focuses on the Ukraine to quantify the relative efficiency – the extent to which satellite imagery can reduce the error of area estimates – and cost efficiency – the extent to which satellite imagery can reduce the cost of ground surveys for crop area estimation – of the MODIS, Landsat-5/TM, AWiFS, LISS-III and RapidEye systems. With Er defined as the ratio of the variance of the ground survey estimator divided by the variance of the regression estimator, cost efficiency is Er multiplied by the ratio of the cost of the ground survey divided by the sum of the ground survey and imagery costs. On an area of 78,000 km2 and with a sample rate of 2 percent – 90 square segments of 16 km2 – stratification was derived from the ESA GlobCover (Arino et al., 2008). CVs of about 25 percent were obtained for crops covering approximately 200,000 ha. With classification accuracy between 45 percent and 65 percent using decadal MODIS imagery or three dates for the other image types, all types of imagery provided comparable relative efficiency between 1.5 and 1.6. In terms of cost efficiency, if the survey cost is €167 per segment only the free-access MODIS and Landsat images enable cost efficiency of about 1.5, and all the other sources become inefficient.

Studies from other sectors also consider the cost efficiency of remote sensing for environmental monitoring. Mumby et al. (1999) review the price structure of remote sensing, and even though today’s prices are lower than they were 15 years ago, it is useful to quote from the abstract of their paper: “Satellite imagery is suitable for coarse detail habitat mapping where overall accuracies of c. 70 percent can be achieved but is inadequate for fine detail mapping, achieving c. 40 percent accuracy. Four types of cost are encountered when undertaking remote sensing: i) set-up costs, ii) field survey costs, iii) image acquisition costs and iv) the time spent on analysis of field data and processing imagery. The largest of these are set-up costs such as the acquisition of hardware and software which may comprise 48–78 percent of the total cost of the project depending on specific objectives. For coarse-detail habitat mapping with satellite imagery, the second highest cost is field survey which can account for c. 20 percent of total costs and >80 percent of total costs if a remote sensing facility already exists.”

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Lewis et al. (2013) discuss the cost-effectiveness of remote sensing approaches for mapping a tropical savannah environment. They believe that decision-making should be based on the accuracy of results and the costs involved, and identify cost components as: i) acquisition and preparation of field data; ii) acquisition and preparation of image data; and iii) image classification and accuracy assessment. Accuracy is quantified as the overall accuracy derived from a confusion matrix comparing ground measurements with image classification. In their study, results ranged from 28 percent to 67 percent, and costs – 90 percent of which pertained to fieldwork – varied by only 15 percent. Hence accuracy remained the dominant factor in the selection of approach. Satellite image classification costs were never more than 1 percent of total project costs.

De Bruin and Hunter (2003) study the cost efficiency of remote sensing for checking European subsidies. The abstract states: “A growing problem confronting users of remotely sensed imagery is whether the use of additional or different imagery to improve decision quality is actually justified by its cost. This paper discusses how to compare these competing factors so that an acceptable trade-off may be made between them. The proposed method is based on probabilistic cost-benefit analysis. The concept of “value of information” is introduced in a practical case study using remote sensing to verify farmers’ declarations for a crop subsidy program in the European Union. Application of the method requires that: i) the problem at hand can be represented by a decision tree; ii) the desirability of each decision outcome can be expressed numerically; iii) the imagery reveals information about the occurrence of events not under the decision maker’s control; and iv) the probabilities of these events and the extent to which they are detectable in remotely sensed imagery can be assessed.” An average value is fixed for all outcome possibilities by creating a multi-level decision tree in which each node has an associated probability of movement up or down. The method consists of computing the average decision value and comparing it with the cost of the technology used. As with area statistics, part of the problem is choosing between fieldwork and remote sensing: but because the issue is not covered in the paper its interest for us is limited.

In conclusion, the literature provides two promising approaches for evaluating the cost efficiency of remote sensing:

• The most attractive is the USGS approach using a cost-valuation method based on a survey of thousands of users, which enables reliable

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estimation of the value that they attribute to imagery. The population of users of remote sensing for agricultural statistics, however, is so limited that no survey can be envisaged.

• The other approach consists of evaluating cost efficiency by comparing the costs of alternative technical solutions. If the classical relative-efficiency model is adopted, conclusions can be drawn as to the suitability of different options. This will be the model used in Chapter 5 on case studies.

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5 Cost Case Studies 5.1. Haiti: Point Area Frame Sampling

5.1.1. Description of the point sampling frame

Haiti tested its first area frame survey in 1997 through a USAID project, using the classic area frame with physical segments of 100 ha. Because of the nature of the landscape and field sizes, the experience was inconclusive and did not result in any practical implementation.

From 2008 to 2014, the Centre National d’Information Geo-Spatiale (CNIGS) and the ITA and I-Mage companies implemented an EC-FED project entitled Programme d’Informations Territoriales pour le Développement Durable (PITDD) with the support of a €1.85 million FED grant (Giovacchini, 2014). The six components were:

• an agriculture and food security information system; • a land-use observation system; • management of national parks; • communications infrastructure; • land-use planning; and • watershed management.

We consider the first component. One of its initial outcomes was the Référentiel National d’Observations Ponctuelles (RENOP) database, essentially the equivalent of a land-use map. A set of 1,728,000 points on a regular square grid of 125x125 metres was photo-interpreted on 2001 IGN imagery of 50 cm resolution. Fifteen local operators spent 3,400 days between June 2008 and February 2009 on photo-interpretation using a two-level system of 24 categories. Accuracy was verified through quality controls of 3 percent, giving an average accuracy of 95 percent.

The classification system and the land cover statistics obtained at the national level are shown in Table 2.

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Table 2. Haiti, Point Area Frame Land Covers Percentages

Level 1 Level 2 %

1. Artificial

1.1. Populated urban zones 0.56

1.2. Populated rural zones 1.30

1.3. Industrial and commercial zones 0.02

1.4. Urban green, park and garden zones 0.01

1.5. Sport equipment (playgrounds) 0.01

1.6. Road network 0.28

1.7. Ports and airports 0.02

1.8. Zones under construction, waste sites 0.04

1.9. Mines and quarries 0.01

2. Agricultural 2.1. Alimentary multi-cropping 33.95

2.2. Agroforestry 16.56

3. Natural / semi-natural

3.1. Meadow or grassland 22.91

3.2. Shrub vegetation 16.60

3.3. Forest and wooded land 3.24

3.4. Mangrove areas 0.53

3.5. Trees not in Forest 0.76

4. Open space with no or

little vegetation

4.1. Sparsely vegetated areas 0.52

4.2. Bareland and rocks 0.38

4.3. River beds or recent alluvial soils 0.56

4.4. Dunes and beaches 0.03

4.5. Burned areas 0.12

5. Water area

5.1. Humid zones 0.65

5.2. Internal waters (lake, ponds, rivers) 0.79

5.3. Costal maritime waters 0.21

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Land-use maps could also be produced from RENOP (Giovacchini, 2014):

Figure 1. Haiti – RENOP Land-Use Map, 1998

Table 3: Haiti, Stratification plan

Stratum Label Land-use codes Area (km2)

0 Non-sampled 14-19, 34, 42-45, 51-53 1 012

1 Urban 11, 12, 13 529

2 Arable 21 9 409

3 Orchards 22 4 590

4 Pastures 31, 35, 41 6 704

5 Forests 32, 33 5 499

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The stratification used for the agriculture survey was based on the land-use map and comprised six strata. It should be noted that the non-sampled stratum accounts for only 4 percent of the total area, probably because of the complexity of terrain. This limited from the outset the gain generally provided by the non-sampled part of the territory because a small part of the sample was redirected on the agricultural zones.

Sample allocation followed the method described in Bethel (1985): this involved refining the sampling plan according to survey costs per stratum – the forest stratum was twice as costly as the agriculture strata, which were in turn twice as costly as the urban stratum – and targeting a 5 percent to 7 percent CV for maize, bananas and sugarcane at the district level. Sample allocation is one of the functionalities of the STAT-AGRI software developed by ITA for Haiti; the Bethel method was provided by ISTAT. Initial variances were derived from the first 2009 survey of two departments. To monitor the representativeness of the sample, 20 percent was changed each year.

For the first 2013 growing season, 84 percent of the planned sampling (see Table 4) was actually carried out.

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Table 4. Haiti – Sampling Plan for 2013 Growing Season

Department Sample

size (ha)

RENOP size

strata 1-5

Area

visited (ha)

%

visited

Ouest 2,847 262,282 2,230 78

Sud Est 2,111 112,289 1,699 80

Nord 2,055 129,027 1,731 84

Nord Est 1,882 100,028 1,743 93

Artibonite 4,331 288,555 3,636 84

Centre 2,868 212,244 2,148 75

Sud 2,448 159,007 2,147 88

Grande Anse 1,640 116,911 1,371 84

Nord Ouest 2,088 131,250 1,986 95

Nippes 1,511 75,772 1,320 87

Total 23,781 1,587,365 20,011 84

5.1.2. Description of survey components

The survey planned in the initial project comprised the components described in the following paragraphs.

Crop area phase 1 – first growing season, April–May

Once the 150 surveyors and their supervisors had been trained, the sampled survey points were visited. Land use was observed in a 3-metre to 15-metre circle round the point and classified at one of 178 levels. Where several crops were grown together, each crop percentage was estimated and recorded. When crops were grown under tree cover the forest was assigned a value of 1 and the crop(s) were assigned area percentages, giving a total value between 1 and 2.

Crop area estimation was carried out by stratum at the department level. As always in point sampling, the computations were simple because the crop area at the stratum level is simply the observed average percentage (p) for the crop multiplied by the stratum area. Aggregation of total areas at the department and national level was obtained by summing the estimated areas at the stratum and then the department level. Variances at the stratum level were computed on the basis that point-level values follow a Bernouilli distribution whose variance is p(1-p). Visits were not made to all sample points because: i) some were

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inaccessible to the surveyors; and ii) imagery showed that some were located on houses, so observation was irrelevant. This second case illustrates one element of the cost-efficiency of using remote sensing.

Another contribution of remote sensing to the survey efficiency was the use of image printouts during the field survey. They made it easy to reach the sample locations, and requiring reference to images rather than GPS coordinates limited location errors in later phases of the survey.

There were no surveys in 2010 because of the earthquake in January that year. Surveys were completed in 2009 in two departments, in 2011 in six departments, and in 2012, 2013 and 2014 in ten departments; it is not known if the phase 2 survey was completed in in 2014.

Crop area, phase 2 – second growing season, November–December

Because Haiti has two crop-growing seasons, the survey was repeated to obtain total annual crop areas.

Yield and production survey

The plan was to estimate the yields of maize, rice, bananas, yams and beans, but lack of available staff at the Ministry of Agriculture limited the work to writing survey manuals, training in plot selection and crop cutting and weighing, and defining the 10 percent sample of points containing maize and rice in Artibonite and Sud. No field survey was carried out. For maize, double-phase sampling by regressing a limited number of fields (k) with precise yield measurements on a larger sample (n) with stalks counts was envisaged.

Survey of holdings

The holding frame was based on the RENOP grid. Points were aggregated in groups of four to define a square segment of 1.56 ha. Stratification involved characterizing the segment according to land use at the four corner points. If no points fell into stratum 2, 3 or 4 of the crop-area frame, the square was classed as part of a non-agriculture stratum; if the segment had 1, 2, 3 or 4 agricultural corners, it was allocated to stratum 1, 2, 3 or 4 according to the intensity of agriculture.

To achieve a sample size of 700 holdings, and considering that the average size according to RGA-MARNDR 2009 was approximately 0.74 ha, the sample size

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was fixed at 350. Again, staffing limitations prevented the completion of this activity.

5.1.3. Phase 1 crop area results, 2013

Table 5 is extracted from the EC-FED report (Giovacchini, 2014): it shows the estimation for the main crop areas in hectares in the first growing season of 2013 and the 2009 RGA total areas for both growing seasons (MARNDR, 2009).

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Table 5. Estimated crop areas, 2013 first growing season

Maize

Bananas

Beans

Rice

Department Area CV Area CV Area CV Area CV

Ouest 30,732 7 11,263 10 5,147 17

Sud Est 23,910 6 5,042 12 1,090 28 6 313

Nord 11,317 10 20,464 7 5,645 13 931 34

Nord Est 6,088 11 1,941 19 2,954 17 1,840 22

Artibonite 28,120 6 11,453 9 9,897 11 8,893 9

Centre 51,371 5 17,019 9 4,713 19 888 42

Sud 29,078 6 6,105 13 2,664 19 2,914 18

Grande Anse 9,702 11 7,493 12 1,521 28 789 42

Nord Ouest 12,658 8 10,110 9 516 40 37 140

Nippes 11,559 8 4,399 13 2,432 18 62 99

Total 214,540 2 95,293 3 36,582 6 16,364 7

RGA 2009 393,095 NA 97,537 NA 247,067 NA 75,861 NA

Sorghum

Gungo peas Sugarcane Unused land

Department Area CV Area CV Area CV Area CV

Ouest 7,044 15 18,974 9 6,652 13 70,324 4

Sud Est 1 11 5.6 12 779 33 25,204 6

Nord 321 56 5,088 14 12,399 9 26,945 7

Nord Est 240 61 3,657 16 2,278 19 15,587 7

Artibonite 10,367 12 12,421 10 18,012 7 64,415 4

Centre 8,931 13 18,247 9 13,659 11 24,976 8

Sud 4,144 18 7,622 13 1,418 29 34,499 6

Grande Anse 3,399 20 3,471 20 32,660 6

Nord Ouest 840 32 7,366 12 946 30 16,058 8

Nippes 624 39 4,346 14 1,967 20 8,133 10

Total 39,190 6 86,725 4 61,586 4 318,807 2

RGA 126,775 NA 108,633 NA NA NA NA NA

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These results show that with the selected sample size the CV of the major crops is below 5 percent when a crop area is greater than 50,000 ha, and below 10 percent when a crop area is greater than 10,000 ha.

The estimated crop areas do not match the RGA 2009 results. This is because they refer to different years and different growing seasons; the large discrepancies reflect methodological differences in the two approaches.

The graphs in Figure 2 show the variance of crop areas rescaled according to the density of points per km2, which enables comparison of the efficiency of the sampling plan with similar point surveys. The survey in Haiti appears to be twice as accurate as TerUti 2014 – the slope was of 0.10 as opposed to 0.20 –, possibly as a result of stratification and the fact that the points in TerUti are clustered. Because details of crops and pastures are not publicly available, the TerUti computations rely on the urban, bare-soil and wooded land classes.

Figure 2. Comparison of the standardized sampling variances:

a) Haiti survey b) French TerUti survey

5.1.4. Stratification Efficiency

Stratification is carried out at the stratum level through photo-interpretation and at the department level by administrative splitting, but to evaluate the cost efficiency of imagery for stratification we must work at the department level, and subsequently aggregate efficiency at the national level.

Following Cochran (1977), the variance of the simple random sampling plan can be derived from a stratified sample by splitting the total variance into two;

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the in-stratum variance and between-strata variance. At the department level, this gives us:

_ s s _ _

V(Ysrs) =((1-f)/n)*(1/(N-1))* { Σ (Nh-1)* S2h + Σ Nh (Yh – Y)2} h=1 h=1 where: N is the total population size;

n is the total sample size; f is the total sampling fraction n/N; s is the number of strata; Nh is the population size for stratum h; S2

h is the variance within stratum h; _ Yh is the average of stratum h; and _ Y is the department average.

As usual, relative efficiency is expressed as the ratio of variances:

Er= Vsrs/Vstr

It should be noted that relative efficiency is the same whether computed on the average or on the total.

An initial step is to look at the sample allocation by stratum and by department, shown in Table 6.

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Table 6. Sample Allocation by Stratum

Stratum Label Sample size

Sampling fraction

0 Non-sampled 0 0

1 Urban 303 0.008

2 Arable 10 655 0.017

3 Orchards 3 942 0.018

4 Pastures 3 107 0.008

5 Forests 2 058 0.006

20 065 0.012

Stratification efficiency evidently comes from (i) the splitting of total variance into in-stratum and between-strata, and (ii) the varying sampling fraction among strata: “non-sampled” is zero, and the values for “arable” and “orchards” are double those of the other strata.

The variance gains at the department and national levels for the selected crops and land uses are shown in Table 7.

Table 7. Variance Gains at the Department and National Levels

Dept 1 Dept 2 Dept 3 Dept 4 Dept 5 Dept 6 Dept 7 Dept 8 Dept 9 Dept 10 Haiti

Dry forest 0.86 0.72 0.30 0.79 0.53 0.77 0.67 0.76 0.74 0.78 0.67

Savannah 0.87 0.79 0.56 0.80 0.62 0.76 0.69 0.95 0.79 0.80 0.73

Maize 1.31 1.21 0.91 1.07 1.05 0.97 1.04 1.09 1.15 1.11 1.09

Rice NaN 1.75 1.06 0.95 1.77 1.00 1.22 0.91 1.33 1.43 1.38

Sorghum 1.10 1.28 1.14 0.97 0.81 0.92 0.84 NaN 1.15 0.92 0.97

Bananas 1.59 1.34 1.01 1.23 1.22 1.00 1.13 1.21 1.29 1.11 1.18

Manioc 1.15 1.22 0.98 1.05 0.98 0.96 0.86 0.86 1.00 1.00 0.99

Potatoes 1.54 1.08 1.09 0.97 1.45 0.92 0.92 0.86 0.99 1.07 1.09

Beans 1.25 1.17 1.13 1.00 1.02 0.92 1.20 1.07 1.24 1.04 1.08

Gungo peas 1.19 1.14 1.11 0.96 0.97 0.89 0.81 0.98 1.00 1.04 1.02

Citrus NaN 1.32 1.08 NaN 1.22 NaN NaN 1.01 0.64 1.26 1.02

Coffee 1.64 1.24 1.18 1.25 1.21 1.11 1.18 1.39 1.59 1.04 1.24

Sugarcane 1.70 1.25 1.11 1.09 1.35 0.93 0.98 0.96 1.19 1.06 1.17

Fallow 1.09 1.07 0.75 1.04 0.87 0.94 0.89 0.97 1.00 0.95 0.97

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Various conclusions can be drawn:

• Departments 3, 6 and 8 seem to have less stratification efficiency, and department 6 has only one land use with an efficiency above 1, which suggests that photo-interpretation was unreliable or that land uses changed drastically between 2001 and 2013.

• Of the land uses studied, bananas, beans, coffee, maize, rice and sugarcane show efficiency gains between 8 percent and 38 percent at the national level.

• As expected, non-agricultural land use suffers from the stratification and shows efficiency less than 1.

• At the department level, stratification efficiencies are between 1.5 and 1.7.

5.1.5. Cost aspects

The cost structure of the survey has two elements: i) the costs met locally by CNIGS; and ii) the costs met by the EC contractor. The crop-area survey cost components were offices, staff, computer hardware and software, photo-interpretation, sample selection, implementation and interpretation of results.

Offices were provided at no cost by CNIGS, which also provided local project-management staff. The costs of expert staff were covered by the EC grant. Over the five years, RENOP and survey activities cost about US$400,000 of the EC grant.

Hardware – 15 personal computers and 100 GPS sets – was made available at no cost, as they were available from earlier cooperation projects. The only software needed were the DET database, which was available from an earlier GMFS project and adapted for the Haiti survey by ITA, and the STAT-AGRI software for sample extraction and estimation, the costs of which were included in the US$400,000.

Fifteen local photo-interpreters were needed for eight months, at a total cost of US$24,000. The cost of the 3 percent quality control by ITA experts was included in the project cost. It should be noted that the survey did not require the photo-interpretation of such a dense set of points: a 500-metre grid would have been sufficient and would have required 16 times fewer staff.

Local management staff received specific training, but sample selection and results interpretation were carried out locally each year by ITA experts; the

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costs were thus covered by the EC grant. Field visits were carried out by a private contractor paid by CNIGS: in the two phases, visits to the 20,000 sample points cost US$150,000 annually (approximately US$3-4 per point and visit).

The annual cost of the survey from 2011 to 2014 was therefore US$256,000 – ITA inputs were US$100,000, LU stratification cost US$6,000 and the field survey cost US$150,000.

Setting aside the fact that land-use photo-interpretation was used for additional purposes beyond the crop-area survey – costs could otherwise have been divided by 16 – it is evident that imagery-based stratification incurred a cost increase of 2 percent. But it provided a decrease in variance of between 8 percent and 38 percent for major crops, which illustrates the cost efficiency of remote sensing for stratification in Haiti.

The USDA financed new aerial coverage in 2014, so that the imagery could be updated and orthorectified at a cost of US$5/km2 – US$135,000 for the whole country – and photo-interpretation could be repeated on a 500-metre grid at a cost of US$1,500. The efficiency of the stratification would certainly be enhanced.

5.2. Morocco: area frame sampling

5.2.1. Description of the area sampling frame

Morocco has used a multiple-frame sampling approach since 1978 to collect agricultural statistical information. Surveys cover crop areas and yields on 10 million hectares, 30 million heads of livestock, market prices for 40 products at central markets and souks, and agricultural holdings. The area frame covers the 40 western provinces; its 3,000 segments contain 95 percent of the national sample of holdings. The list frame covers the eastern and southern areas, where agriculture is more marginal, and is largely based on administrative registers. For crops under contract such as sugar-beet, administrative data replace the area-frame survey results because they are considered to be much more accurate.

In 2008 the Ministry of Agriculture started to renew the area frame, which was originally based on a stratification extracted from existing 1/50,000 topographical maps. The main change is the creation of 1/25,000 land-use maps

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derived from SPOT 2.5 metre resolution imagery covering ten classes: rain-fed arable land, irrigated arable land, orchards, forest, grazing land, large urban and small urban areas, villages, bare land and water bodies.

From 2011 to 2014 an automated GIS stratification tool supported the updating of cereal survey samples over 24 of the 40 area-frame provinces (thus covering an area of 66,900 km2). Samples are selected independently for each crop survey. Sample sizes are initially computed at the province and stratum levels with the NASS ALLOCATE software, which uses Neyman optimal allocation based on constant costs and a priori variances taken from the preceding annual surveys. Hitherto, 1,035 segments have been allocated in the three main cereal strata: 10 arable rain-fed, covering 52 percent of the area, 20 arable irrigated covering 8 percent of the area and 80 large douars covering 1 percent of the area, which are important for counting livestock.

The GIS tool enables the extraction of segments at the stratum level. First, rectangular 600 ha primary sampling units are defined for strata 10 and 20. Second, PSUs are subdivided in segments of 30 ha for stratum 10 and 20 and 4 ha for stratum 80. A single segment is selected from each PSU; this stage will soon disappear because it is a technical constraint inherited from the past. The sample per strata (nh) is then split into artificial strata (kh, function of slope) and systematic replicates (rh) with nh = kh rh.

5.2.2. Main results of the 2013 cereal survey

Micro-data were accessed for the Chaouia–Ouardiga, Doukala–Abda, Gharb– Chrarda–Beni Hssen, Grand Casablanca and Rabat–Sale–Zemmour–Zaer regions, which have 18 provinces and a total administrative area of 49,000 km2. The data consisted at the tract level of the crop code and the tract area, which extended beyond the segment. We decided to work at the level of the 810 segments on the percentages of the extended segment area computed as the ratio of the extended crop areas and the extended segment area.

The main results – estimated areas in hectares and CVs – are shown in Table 8 at the regional level for wheat, durum wheat and barley, and at the global level for the ten most important crops.

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Table 8. Morocco Estimated Crop Areas (ha)

Regions Three cereals (ha) CV

Chaouia–Ouardiga 798,190 2.1

Doukala–Abda 590,860 4.0

Gharb– Chrarda–Beni Hassan 298,970 3.3

Grand Casablanca 30,260 9.8

Rabat–Sale–Zemmour–Zaer 231,360 6.3

Total 1,949,640 1.8

Ten most important crops

Area (ha) CV

Total three cereals area 1,949,640 1.8

Wheat 917,971 3.5

Fallow land 649,352 4.1

Barley 647,315 4.1

Durum wheat 384,337 6.3

Pasture 122,668 16.3

Corn 108,831 12.1

Waste land 86,342 10.4

Beans 68,771 11.5

Berseem clover 36,903 12.6

Chickpeas 35,207 13.7

Lentils 31,173 15.7

5.2.3. Stratification efficiency

This section looks at the efficiency of the stratification based on the land-cover maps derived from SPOT imagery. Relative efficiency is derived through the method set out in Cochran (1977).

For the total cereals area –wheat, durum wheat and barley – it can be shown that relative efficiency varies from province to province between 1.4 and 14. As in Figure 5, an important explanatory variable of the reduction in variance is the percentage of province area excluded from the sampled area. This arises from the land-use maps and the identification of areas where there is a low probability of cereals being grown. This gain results from the concentration of the limited sample available in the areas where cropping is intense.

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Figure 3. Stratification Efficiency versus proportion of Non-Sampled Area in a Province

For the most important crops, it is evident that optimum relative efficiency is obtained for the total cereals area (see Table 9). This is positive because these are the main crops of interest for the survey. For pasture and corn, no gain of efficiency is observed.

Table 9. Morocco, stratification relative efficiencies at national level

As shown in Table 10, the estimation of crop areas at the provincial level benefits more from stratification, which is of interest because local estimates

Crop Relative efficiency

Total three cereals 3.0

Wheat 2.1

Fallow land 1.9

Barley 1.6

Durum wheat 1.3

Pasture 1.0

Corn 1.0

Waste land 1.8

Beans 1.4

Berseem clover 1.3

y = 11.546x + 0.6378R² = 0.5742

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0.00 0.20 0.40 0.60 0.80 1.00

Rela

tive

effic

ienc

y

% of non sampled area in the province

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are always needed and usually suffer from greater variance because sample sizes are smaller. Cases of relative efficiency above 3 are not exceptional. The case of waste land in Kouribga – Er = 40 – appears abnormal: it is caused by the fact that the 8 segments of 4 ha of stratum 80 (large douars) show a percentage of waste land of 80 percent, whereas stratum 10 (rain-fed arable land) shows a percentage of 1 percent.

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Table 10. Morocco, stratification relative efficiencies at province level

Region Province Total Soft Fallow Barley Durum Pasture Corn Waste Beans Clover

3 Cereals Wheat Land Wheat Land Land

Chaouia –

Ouardigha

Benslimane 4.4 2.5 2.4 2 1.8 1.6 1.6 6.7 1.8 NaN

Berrechid 2 1.2 1.2 1 1.1 1 NaN 2.4 1 NaN

Khourbiga 6.3 1.8 2.1 3.4 1.7 1.5 NaN 40.6 NaN NaN

Settat 2.9 1.7 1.2 1.3 1.7 0.8 NaN 1.2 1.4 NaN

Doukkala –

Abda

El Jadida 1.7 1.3 1.7 1.3 1.2 0.9 1.1 5.6 1.3 2.1

SAFI 1.4 1 1 1 1 0.9 0.9 2.6 0.9 8.6

Sidi Bennour 1.6 1.1 1.2 1 1.1 NaN 0.9 3.2 1.6 3.1

Youssoufia 1.4 0.9 0.9 1 0.9 0.84 NaN 1.5 NaN NaN

Ghrab -

Chrarda-

Beni Hssen

Kenitra 3.2 4 1.5 0.9 1.9 0.8 1.7 1.2 1.9 1.5

Sidi Kacem 4.5 4.6 1.6 0.8 1.4 0.9 NaN 1.6 1.4 0.9

Sidi Silmane 13 10.1 2 2.2 2.3 2 NaN 2.8 2 2.1

Grand

Casablanca

Mediouna 2.9 1.5 4.8 3 1.5 NaN 1.5 1.9 1.5 NaN

Mohammedia 4.2 2.9 2.4 3 1.6 1.6 1.7 1.6 1.5 1.5

Nouacer 2.9 1.5 1.6 1.7 1.4 1.3 1.5 1.8 1.3 NaN

Rabat –

Sale-

Zemmour

Zaer

Khemiset 4.5 3.2 3.1 2.5 2.1 2.1 2.1 2.2 2.1 NaN

Rabat 10.2 10.2 10.2 10.2 NaN 10.2 NaN NaN 10.2 NaN

Sale 6.6 4.1 8.5 6 2.8 2.9 3.5 2.7 2.8 NaN

Skhirate Temara 7.1 4.6 3.8 3.8 2.4 2.5 2.7 2.4 NaN NaN

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5.2.4. Cost aspects

The first cost component of the survey was the 1/25,000 land-use maps, each of which covered 16,000 ha and cost US$1,100; of this, 50 percent was imagery costs, 35 percent field validation and 15 percent photo-interpretation. The total for the 24 provinces covering 66,900 km2 was US$460,000.

The second cost component was sample extraction with the free NASS ALLOCATE software and the GIS mapping tool, which cost US$20,000 to buy. These helped to reduce the time required for each region from ten months in the first survey to about one week.

The third component was field visits to the selected segments to complete the first survey and identify the holdings linked to each segment. This work was contracted to the private sector and cost an average of US$225 per segment, to which the US$140 borne by the Ministry of Agriculture must be added. In subsequent years survey costs amounted to US$125 per segment.

The estimated cost of the cereals survey in the first year was therefore US$378,000 for the 24 provinces. The annual cost in the subsequent nine years amounted to US$130,000. The average cost for the ten-year survey was hence US$155,000. Stratification therefore increased annual costs by 30 percent.

Considering the reduction in variance at the global level described in Section 5.2.3, it is evident that except for pasture land and corn the relative efficiency of stratification was greater than in 1.3, and reached a value of 3 for the three major cereals. At the provincial level, Er values above 3 were not exceptional. We can therefore conclude that the stratification of the cereal survey in Morocco was cost-efficient.

5.3. China: Area Frame Sampling and Regression Analysis

Several entities produce agricultural statistics in China (Zhang, 2015). Some are official: the National Bureau of Statistics (NBS), the Ministry of Agriculture, the State Administration of Grain and the China Meteorological Office. Other non-official sources include the Institute of Remote Sensing and Digital Earth Imagery and the Chinese Academy of Sciences. Of these, the NBS is the only one delivering official statistics and also using remote sensing for area frames, stratification and image classification for regression.

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Figure 4. Administrative Divisions of the People’s Republic of China

Source: http://en.wikipedia.org/wiki/Administrative_divisions_of_China

5.3.1. Description of the area sampling frame

Increasing economic migration from rural areas to towns was the main reason for replacing the former list frame with an area frame. When rural residents leave, their land is redistributed among the other rural workers, which leads to land fragmentation and rapid depreciation of the list frame. Using an area frame is thus a way to stabilize the sampling frame to be used over a ten-year period.

The NBS uses area frames in seven of the 33 provinces of China (Yu, 2013); area frames for three additional provinces planned for 2015 are not yet operational. The administrative area covered by the ten provinces is 1,652,083 km2, 17 percent of the national land area. Table 11 shows the year of area frame creation, the year of the imagery used and the year of the first area frame survey in these provinces.

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Table 11. Area Frame Surveys in China

Province Km2 Location Image year for

census update

First survey

year

Counties

with area frames

Anhui 139 400 East-central 2013 2014 63

Hebei 188 434 East-central 2014 2016

Henan 167 000 East-central 2012 2013 43

Hubei 185 900 East-central 2012 2013 68

Hunan 211 855 South-east 2014 2016

Jiangsu 102 600 East-central 2012 2013 75

Jiangxi 166 894 South-east 2014 2016

Jilin 187 400 North-east 2013 2014 44

Liaoning 145 900 North-east 2012 2013 43

Shandong 156 700 East-central 2014 2015 110

Creation of the area frames takes advantage of existing lists of small-scale farmers and detailed land-use maps at the individual plot level. Using ArcGIS and ENVI, village administrative limits and land use maps are merged within the GIS tool, after which remotely sensed imagery is used to update the land-use maps by photo-interpretation and automatic classification.

On the basis of previous surveys, counties of marginal agricultural importance are excluded from the frame, but the objective of surveying at least 90 percent of the total crop area is maintained. Stratification is then carried out at the village level that defines PSUs. The number of villages in each county ranges from 100 to 500, each with an area of between 1 km2 and 5 km2.

Using the total crop areas at the village level classified by remote sensing, between two and seven strata are defined and the PSUs are classified into them. The sample size for villages per county is kept to between 15 and 20; sample size per stratum follows rules akin to the Neyman allocation method involving the function of strata size and variability. The stratum with the least agriculture is excluded from the surveyed population. The PSUs are selected with probability proportional to size, the auxiliary information being cropping intensity.

Finally, five secondary sampling units, equivalent to segments, are selected from each sampled village. A 2 ha or 5 ha grid is overlaid on the village limits,

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and five grid elements are randomly selected. These segments are adjusted in two ways: the non-arable area obtained from remote-sensing classification is excluded from the segment, and field plots intersected by the segment are entirely included to give irregular physical segments of random size.

This process results in a stratified two-stage sampling design with PPS selection at the first stage and random selection at the second stage. The sampling fraction is of the order of 0.2 percent.

Table 12 shows the official 2013 statistics for the area frame provinces.

Table 12. China: Official Statistics for 2013 in Area Frame Provinces

Province Wheat

1000ha

Total/rice

1000ha

Corn

1000ha

Rapeseed

1000ha

Anhui 2432.9 2214.1 845.1 568.1

Henan 5366.7 641.3 3203.3 371.3

Hubei 1094.8 2101.2 573.5 1226.3

Jiangsu 2146.9 2265.7 426.6 413.9

Jilin 0 726.7 3499.1 0

Liaoning 5.6 649.2 2245.6 0.6

Shandong 3673.3 123.1 3060.7 9.5

Source: China rural statistical yearbook (2014).

5.3.2. Main results in the province of Anhui

Confidentiality restrictions prevent access to survey results, even when aggregated at the stratum level per county. But access to the survey design data was granted, which made it possible to work on the expected relative efficiency of the design and to estimate sampling variance. The estimator of variance in two-stage sampling with PPS in the first stage is usually obtained by approximation, using the Taylor series development or re-sampling methods such as bootstrapping or jack-knifing. Working at the design level enabled 100 sampling runs, which made it possible to obtain a variance component at the PSU level. The second-stage variance component is usually marginal at 5 percent to 10 percent of total variance, so the relative efficiencies computed later can be taken as representative of the true values.

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The crop areas obtained at the design level are in line with the 2013 survey results shown above, and thus give confidence in the derived CVs (see Table 13.). They show that because of the stratification and PPS, CVs below 5 percent are obtained at the province level for crop extents above 100,000.

Table 13. – China, design level crop areas in Anhui province

Crop Area (ha) CV

Wheat 2.231.460 1.3

Early Rice 102.790 5.4

Middle Rice 1.895.270 0.9

Late Rice 870 116.7

Corn 1.021.100 3.0

Rapeseed 205.050 4.6

Expected estimated crop areas at the county level and their CVs are given in Table 15 on page 80. At the county level, with 20 PSUs and 100 SSUs, CVs below 10 percent are often obtained, but for a fixed crop extent a large range of CVs values is observed.

Figures 5 and 6 show the relationship between the logarithm of the crop area and the expected CV for wheat and middle rice.

Figure 5. – China, relation between wheat CVs and areas at county level in Anhui province

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Figure 6. – China, relation between middle rice CVs and areas at county level in Anhui province

5.3.3. Stratification efficiency

Using the Cochran (1977) formula mentioned earlier, relative efficiency was computed by adding the “between strata” variance to the “within strata” variance observed for the stratified sample, and calculating the ratio between the non-stratified and stratified variances at the county and province levels. We computed the efficiencies in two ways: i) including the non-sampled stratum; and ii) excluding the non-sampled stratum. And given that the PPS sampling could invalidate the use of the Cochran Er formulae, we created a set of 100 sampling simulations, keeping the PPS selection of PSUs but without stratification.

Table 14 compares the efficiencies obtained by both methods for the six crops in Anhui province.

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Table 14. Comparison of Er Calculation Methods

Comparison of Er calculation methods

Wheat Early Rice

Middle Rice Late Rice

Corn Rapeseed

Cochran with non- surveyed stratum

3.6 1.4 7.7 0.3 1.6 1.1

Cochran without surveyed stratum

1.3 0.8 2.2 0.3 0.9 0.7

Monte Carlo 1.4 1.0 2.8 0.9 1.1 1.0

Following Cochran’s approach, it is clear that at the province level efficiency reaches a value of 7.7 for middle-season rice, 3.6 for winter wheat and 1.6 for corn. Rapeseed, also a winter crop, and early and late rice show poor efficiencies. As with the preceding case studies, a large part of the efficiency results from not sampling the low intensity stratum of the villages. The efficiency computed for the sampled strata only reduces efficiency to 1.2 for winter wheat and 2.2 for middle-season rice, and to less than 1 for the other crops. In Monte Carlo simulations, efficiency is slightly less.

In what follows we will rely on the efficiency obtained through the Monte Carlo simulations. Efficiencies at the county level are shown in Table 15 for wheat and middle-season rice only because the Er is nearly equal to 1 for the other crops. At the county level, it is evident efficiency varies considerably, mainly as a function of the extent of the non-sampled strata and of the difference in average and variances among strata.

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Table 15. China: Efficiencies at the County Level

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5.3.4. Costs: Area Frame and Stratification with Remote Sensing

The costs associated with the area frame survey can be subdivided into the cost of constructing the area frame, including stratification, the cost of carrying out the survey and the cost of analysing the results. One part of the costs is the fees for the work sub-contracted to commercial companies and to the counties, provinces and headquarters; another is the NBS charges.

The NBS considers that the area frame approach is more expensive than the list frame in terms of both frame construction and the field survey. This is because of the technology used – satellite images, digital land-use maps, workstations, GIS software and PDAs – and because the sample was more compact in the list frame, which was based on the hamlet level, than in the area frame, which was based on the village level. The NBS prefers the area frame approach because of its robustness.

For each province, the two main cost components can be summarized as: i) RMB500,000 for sub-contracted work on creating the area frame, which is expected to last for ten years; and ii) RMB50,000 per county for the annual field survey; for Anhui province, the total is therefore RMB 3 million.

The sub-contracted costs cover image acquisition on three dates at 200 GB per province, and classification. Using land-use maps limits updating, frame setting and the sample extraction, which requires up to 20 persons full time for two months.

The NBS costs relate to: i) central organization: two staff for two months, hardware and software costing RMB500,000 for 20 stations with ArcGis and ENVI centrally, plus provincial stations; ii) province-level work: printing maps for 150 counties and interpreting survey results; and iii) county-level work: 3 field visits by two enumerators per village with field data entry into electronic systems.

In summary, area frame creation and stratification cost an estimated RMB1 million, compared with the field survey annual cost of RMB3 million. Assuming that the area frame will be used for ten years, the additional cost of the area frame and stratification are 3 percent.

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Considering the relative efficiency of stratification and the cost efficiency of remote sensing for area frame creation and stratification, the approach is clearly cost-efficient.

5.3.5. Using Remote Sensing to improve crop-area estimation

The use of satellite imagery to improve agricultural statistics through the regression method is still experimental at NBS. In 2014, two provinces were fully covered: Liaoning – 145,900 km2 – and Xinjiang – 1,664,897 km2; most of Zhejiang province was also surveyed. The province of Anhui – 139,400 km2 – is being covered in 2015; the survey of winter wheat is already complete.

The following discussion of methods is based on Xinjiang province, where cotton is the main crop of interest. The 2013 crop areas were: i) wheat – 1.1 million ha; ii) rice – 67,000 ha; iii) corn – 900,000 ha; iv) rapeseed – 44,000 ha; and v) cotton – 1.7 million ha.

The first step was to overlay a 3x3 km grid on the province. The grid cells would form the PSUs. Because much of the province is mountain and desert, a large part of the administrative area was excluded from the frame, reducing the population target area to 200,000 km2. This resulted in a population of 22,000 PSUs, from which a sample of 500 was to be selected. The PSUs were then stratified into 14 classes such as dryland, wetland and cotton cropping on the basis of land-use maps and image coverage from 2013. PSUs were selected at the county level, with PPS as a function of total arable land.

The second stage of the sampling consisted of random selection of three segments as SSUs of 300x300 metres, totalling 9 ha. Total province sampling size amounted to 1,500 segments.

Using the current year’s ground survey, satellite coverage from three dates was classified into the main crop classes, aiming at 75 percent accuracy on training set. Some 1,000 free-access GF-1 or ZY-3 scenes enabled coverage of 70 percent of the area; the missing part required the purchase of expensive commercial imagery such as Ikonos and Worldview2 covering 1,000 km2 each. Some of the target area could not be imaged, but there was imagery for all 500 sampled grids.

This exercise enabled three types of estimate: i) direct expansion of the results of the 1,500 segments of the ground survey; ii) direct expansion of the classification results for the 500 sampled grid cells, the PSUs; and

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iii) regression estimation merging the ground survey and classification results at the segment level; 840 segments contained cotton; this was done on 40 classified GF-1 images and 20 Landsat 8 images.

For reasons of confidentiality, access to detailed results was denied. The difference between the first two approaches was understood to be of the order of 5 percent, which would have been an excellent result. Regression analysis was applied at the province level without subdivision based on image acquisition dates or strata. The coefficient of determination – R2 – for cotton was announced as about 0.66, which again appeared excellent for such an imprecise approach.

5.3.6. Cost aspects of Using Remote Sensing to improve crop-area estimation

As with the area frame exercise, the work was shared between NBS and a contracted commercial company. All the subcontracted technical work was carried out at NBS premises at a cost of RMB700,000 for the province.

Internal NBS costs were mainly the ground survey, on which 200 surveyors with their drivers worked for two weeks in each survey period, covering an aggregate of 20,000 km. Salaries were RMB150 per day for each of the surveyors; drivers with private car were paid RMB250 per day, and fuel costs amounted to RMB100 per day for each team. Data were entered on hand-held computers. Each segment had averagely 15 to 20 fields. The overall cost of the ground survey was approximately RMB1 million for the province.

5.4. India: Area Frame Sampling and Pixel Counting

The Directorate of Economics and Statistics of the Ministry of Agriculture produces agricultural statistics in India based on information from sources such as the -Forecasting Agricultural output using Space, Agro-meteorology and Land-based observations initiative (FASAL), of which the Mahalanobis National Crop Forecast Centre (MNCFC, created in 2012), is the operational arm. MNCFC’s mandate covers crop forecasts, monitoring of extreme weather, horticulture assessment and crop insurance. But its outputs in terms of crop areas and production cannot be distributed because they are unofficial.

Crop forecasts are based on conventional surveys and remote sensing. Pre-season and early-season forecasts are based on econometric and agro-meteorology models; mid-season, state-level and district-level pre-harvest

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forecasts rely on remotely sensed information. The MNCFC issues 18 annual forecasts based on remote sensing (see Table 16) for crop areas and productions for the eight main crops – Kharif rice, wheat, cotton, sugarcane, jute, rapeseed and mustard, Rabi rice and Rabi sorghum – on 102 million ha of the 120 million ha of cropland in India.

Table 16. India Annual Forecasts based on Remote Sensing

Crop States Forecasts Date Level Area Yield Area Mha Off. Stats

Jute

3 F1 July 18 2014 District SAR Agromet 0.78

Kharif Rice 13 F1 Sep. 05 2014 State SAR Agromet 33.9

13 F2 Oct. 01 2014 District SAR Agromet

14 F3 Feb. 03 2015 District SAR

RS + Agromet

+ crop Cutting

Sugarcane 5 F1 Aug. 08 2014 State Awifs Agromet 3.86

5 F2 Dec. 17 2014 District Liss3+L8

Agromet

Cotton 8 F1 Nov. 07 2014 State Awifs Agromet 12.2

8 F2 Dec 17 2014 District Liss3+L8

Agromet

Rapeseed &

Mustard

5 F1 Dec. 31 2014 State Awifs Agromet

5 F2 Feb. 03 2015 State Awifs Agromet 5.48

6 F3 Mar. 05 2015 District Liss3+L8

RS spectral mode + Agromet

Rabi Sorghum

2 F1 Feb. 03. 2015 District Liss3

+L8 Agromet 3.67

Wheat 6 F1 Feb. 09 2015 State Awifs Agromet 36.91

6 F2 Mar. 05. 2015 State Awifs Agromet

8 F3 Apr. 10 2015 District Liss3+L8

RS spectral mode + Agromet

Rabi Rice 5 F1 Apr. 01 2015 District Risat Agromet 3.41

Potato 5 F1 Feb 06 2015 State

Awifs Agromet 1.37

5 F2 Mar. 05 2015 District Liss3+L8 Agromet

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MNCFC forecasts up to the district level aim to cover at least 90 percent of national production of the eight major crops, so only 19 of the 29 states are included in the analysis. MNCFC collaborates with 90 national and state entities:

• Space Application Centre-Indian Space Research Organization (SAC-ISRO) for research and development, with objectives fixed at six-monthly meetings; new approaches are tested by SAC for four years, after which MNCFC staff run it for two years under SAC supervision.

Estimation of crop areas is based on:

• ground truth collected by 19 state agricultural departments; • MNCFC state-level area estimations based on remote sensing; and • district-level area estimations from state remote-sensing centres; image

analysis is carried out at MNCFC by state experts.

Operational estimation of yield and production is based on:

• Agromet and simulation models run by the Indian Meteorological Department in collaboration with 46 universities; and

• econometric models run by the Institute of Economic Growth.

5.4.1. Crop area estimation: methods and results

The 29 states of India cover 3.29 million ha. The 2012 MNCFC sampling plan based on 19 states was revised in 2014 and now consists of 5x5 km segments following tests by SAC-ISRO. Initially, classified remotely sensed images provided crop maps that provided the percentage of the eight major crops in all segments, which in turn enabled the creation of between two and four strata with crop-specific class limits at the state level.

A crop-specific sample of segments is then randomly selected at the district level to ensure representativeness, with a 15 percent to 20 percent sampling fraction per stratum except for strata with less than 5 percent for important crops and 2 percent for minor crops, which are not sampled. The national sample size is of the order of 25,000.

For classification, ground truth is collected for 30 percent of the sample – 8,000 points, one field per segment. Of these fields, 6,000 are surveyed using

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smartphones for photography, GPS recording and digital data entry; real-time results can be monitored at the ISRO Bhuvan website: http://bhuvan.nrsc.gov.in/bhuvan_links.php Information on planting and harvest dates, areas, crop development and diseases is also acquired.

For rice and jute, RISAT 1 images – 18-metre ground sampling distance (GSD) C band, like Radarsat – from three dates are selected to detect planted areas in the four cycles: 13–25 July, 15 July–15 August, 2 August–15 September and 27 August–26 September. On the basis of the ground-truth data, back-scattering model limits are established at the district level by computing means and standard deviation for fields containing at least 7 pixels. Rice and jute estimates are derived from the 25,000 classified segments of the sample.

For the eight states with archived results – there are no district-level archives –the CV and relative efficiencies of rice estimates are given in Table 17. For rice areas between 1 and 3 million ha, the coefficients of variation are between 1 percent and 3 percent; there is, as expected, a clear relationship between CV and crop extent at a fixed sampling rate.

The efficiency of stratification based on radar imagery ranges from 1.3 to 3.2 – an excellent result in view of the “noise” encountered.

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Table 17. Rice Sampling Plan, CV and Stratification Efficiency

Rice Sampling Plan, Coefficient of Variation and Stratification Efficiency

State CV (%)

Effective Degrees of Freedom

Efficiency/ Gain

Population Samples Population Total

Sample Total

Sampling Fraction

A B C D a b c d N n %

Andhra Pradesh 2.30 267 3.22 316 581 736 1201 49 91 114 182 2834 436 15%

Bihar 1.41 644 1.30 649 989 1091 784 134 183 206 159 3510 682 19%

Jharkhand 1.92 350 1.30 233 552 747 872 47 93 130 144 2404 414 17%

Karnataka 3.34 269 1.66 195 521 741 1350 41 91 123 204 2807 459 16%

Odisha 1.53 709 1.38 774 1192 1346 1486 126 194 222 246 4798 788 16%

Chhattisgarh 1.61 529 1.46 711 979 1042 1347 114 157 163 208 4079 642 16%

UP 1.23 1230 1.31 1332 2218 2154 1826 226 364 365 312 7530 1267 17%

West Bengal 1.15 469 2.17 603 907 900 676 98 139 143 115 3086 495 16%

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Figure 7. CV as a Function of Rice Crop Area, by State

For all other crops, AWIFS and LISS-3 images are used and area estimates are based on the classification results at the population level. For technical progress allowing, no sampling has been carried out since 2014.

At the start of the crop season, the 10-day NDVI temporal series from the two AWIFS instruments – these cover a 700 km swath, repeated every five days – are clustered; pixels with the crop pattern observed in the ground-truth data set are counted in the relevant crop area. For the last pre-harvest estimate, one LISS-3 image is classified by maximum likelihood: this is done crop-by-crop, but where wheat is involved, pixels already classified as mustard or potatoes are masked.

Because there are no archives, information on the CVs and stratification efficiencies could not be obtained. The MNCFC, however, re-ran classification procedures so that matrices could be provided for two states – an easy one and a difficult one. Even though the matrices are over-optimistic because they are based on the training sets, it is evident that in the Punjab example excellent

(Kha)

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96 percent overall accuracy is obtained, whereas in Gujarat the early image date for wheat and mustard development limited accuracy to 72 percent.

To quantify possible bias implied by the classification errors, we looked at errors of omission and commission: in Punjab, the bias is 1.2 percent for cotton and minus 1.2 percent for paddy rice; in Gujarat; the bias is minus 10.9 percent for wheat and 2.9 percent for mustard (see Table 18).

Table 18.

Punjabe State

LISS III Data (24 Sept 2014)

Classes Cluster Bean

Plantation Cotton Paddy Fallow Mixed Crop

Row Total

User Accuracy

Cluster Bean

13 0 0 0 5 0 18 72.22

Plantation 0 15 0 0 0 0 15 100.00

Cotton 0 0 78 2 0 0 80 97.50

Paddy 0 0 3 81 0 0 84 96.43

Fallow 1 0 0 0 49 0 50 98.00

Mixed Crop

0 0 0 0 0 26 26 100.00

Column Total 14 15 81 83 54 26 273

Producer Accuracy 92.86 100.00 96.30 97.59 100.00

Overall Accuracy 95.97

Punjab is an agriculturally dominant area, During 24 Sept, Rice will be senescence phase and cotton will have very good vegetative growth.

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Gujarat State

Landsat Data (31 Dec 2014)

Classes Potato Other Crop Wheat Mustard Row Total

User Accuracy

(%)

Potato 88 3 14 8 113 77.88

Other Crop 0 138 2 3 143 96.50

Wheat 9 11 46 15 81 56.79

Mustard 12 5 6 40 63 63.49

Column Total 109 193 68 66 436

User’s Accuracy

80.73 71.50 67.65 60.61

Overall Accuracy 71.56

The Lower Accuracy for Wheat is due to the fact that, the crop is at very early stage in December and hence difficult to classify. Similary the optimum date for Mustard is Mid – January.

5.4.2. Method for crop yield estimation

Agromet models

After adjustment at the district level of the trend for each crop yield in a 25-year time series, annual deviations are computed. Next, correlations with annual yield deviations are obtained for meteorological variables for 14-day periods such as Tmax, Tmean, sunshine hours, soil humidity and rainfall. For each meteorological variable, a weighted variable is defined as the sum of the preceding fortnight’s value weighted by the observed correlations, and a stepwise regression is adjusted to predict crop yield at that date.

Crop simulation models (rice and wheat)

In the last three years, the DSSAT model has been calibrated for several varieties. The model gives crop yields based on soil, meteorological and remote sensing information from systems such as AWIFS NDVI and RISAT: sowing date for wheat or transplantation date for rice, variety, phenology, leaf-area index, biomass and yield. This model is used for final forecasting; it is adjusted at the district level.

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Spectral models based on remote sensing

For rice, a physical model (Choudhury et al., 2007) is adjusted in relation to biomass from the back-scattering in RISAT imagery, with crop age taken into consideration. Reference tables established by ISRO on the basis of the crop harvest index are used to convert biomass into yield.

For wheat and mustard, a Monteith empirical model is used to relate crop biomass to received light. Using the AWIFS crop masks and working at 1 km per pixel, the model is fed with EPAR and land surface temperature from MODIS, received sunlight from Insat and crop mask/sowing mask from AWIFS. Current year harvest indexes relating yield to biomass are derived from ground values.

Crop cutting experiments

For rice and wheat, the sample of 2,000 crop cutting experiments is optimized on the basis of remote sensing to derive state-level yield estimates. After the final classification – in September for rice and in March for wheat – the current year crop masks are used and subdivided in four classes of max seasonal NDVI, with a random selection of points taken per stratum. In 2015, ten samples will be taken per district.

Current research targets the use of Agromet models for more crops, and the use of radar for maize and cotton areas with a view to obtain earlier forecasts in rainy seasons, when optical data are scarce.

5.4.3. Costs and resources

The MNCFC has an annual budget of US$1.7 million for offices, salaries, field surveys, imagery, hardware and software. Its 31 staff members come from ISRO, ISS, DAC and contractors. There are 19 workstations under four ARCGIS licences, 13 Erdas Imagine licences, 5 Geometica licences and two Statistica licenses. They work with a server providing 16 TB of disk space.

Ground truth is collected every year by DAC in 8,000 field visits costing US$15 each and 2,000 crop-cutting visits costing US$25, giving a total cost of US$170,000, 10 percent of the budget. Salary costs are supported by the DAC. Collection requires two staff days per district for ten field visits, with one hour’s travel between visits on average.

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Satellite imagery is provided by ISRO at preferential prices below those applied to clients outside India; the annual budget is US$275,000. The products are:

LISS-3 ortho-rectified images at US$233 each, or US$111 for a basic product;

AWIFS ortho-rectified images at US$500 each, or US$250 for a basic product;

weekly country-wide NDVI AWIFS mosaics costing US$1,000; and

RISAT radar images at US$190 per scene, well below the US$2,600 charged for RADARSAT until 2012.

The time needed to issue a crop area bulletin, including image downloading, registration and analysis, is estimated as eight to nine working days per state. A final bulletin for rice requires 120 staff days. It is impossible to estimate the staff needed for a crop-yield bulletin because 46 state remote sensing institutes are involved.

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6 What’s next 6.1. Imagery access: the 2015 situation

In theory, access to satellite imagery is no longer a problem. Although some sensor series have been modified and renamed – AVHRR was replaced by METOP, VEGETATION by PROBA V and ENVISAT/ERS by SENTINEL-1 – numerous sensors are currently available at various levels of spatial and spectral resolution. According to a report by the United Nation Office for Outer Space Affairs in January 2014, there were 3,921 satellites in orbit; of the 1,167 that are active, 192 are Earth observation satellites. Belward and Skoien (2015) give a full review of the situation at the end of 2013, and more recent information can be obtained at:

• https://eoportal.org/web/eoportal/satellite-missions • http://www.wmo-sat.info/oscar/satellites • http://database.eohandbook.com/database/missiontable.aspx

Current problems largely relate to acquisition capacity. The oldest sensors are limited by: i) their recording and downloading capacities – Cosmoskynet is an example; ii) service prices even for non-priority products; very high resolution satellites often charge more than US$20 per km2; and iii) non-availability for non-national projects: examples include Chinese satellites such as GF-1 launched in 2013 covering a 46 km swath with a 2/8 metre P/MS sensor and a 16 metre WFV sensor and GF-2 launched in 2014 with sub-metric resolution only.

For low-resolution imagery – above 100 metre ground sampling distance – various free-access services are provided by geo-stationary and geo-synchronized satellites:

• The geo-stationary Meteosat-10 second-generation satellite EUMETSAT, launched in 2012, provides images every 15 minutes of all of Europe and Africa; Asia is covered by MTSAT and America by GOES; 11 channels provide a 3 km GSD in visible and infrared light, and one channel provides a high-resolution 1 km signal in visible light.

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• The NASA soil moisture active/passive spacecraft launched in January 2015 started delivering 9 km resolution soil moisture information to a depth of 5 cm through a passive radiometer merged with an SAR L-band. Due to radar failure in July 2015, the current resolution is limited to 40 km.

• The geo-synchronized EUMETSAT METOP A launched in 2006 and B launched in 2012 continue the service of the former NOAA-AVHRR-3 sensor, providing six channels daily in the visible and infrared bands at a minimum 1.1 km resolution.

Since 2000 and 2002, MODIS imagery is provided by the Terra and Aqua satellites. Of 36 channels, the best for agriculture are the two bands imaged at 250 metres in visible light and the five bands imaged at 500 metres in the infra-red; it covers a 2,330 km swath every one or two days.

MERIS is a non-operational sensor on the ESA ENVISAT platform launched in 2002. It delivered a 300 metre resolution signal in 15 spectral bands in visible and infrared light covering a swath of 1.15 km every three days; it enabled the development of the ESA GLOBECOVER in 2009. When launched in early 2016, Sentinel 3 will provide MERIS-like imagery with a 1 km GSD over water and 300 metres over land.

Data from the ESA PROBA-V launched in 2013 continue the vegetation instrument data set, and should be seen as a gap-filler for Sentinel-3. Since March 2015, imagery at 100 metre GSD – free if older than a month – has been available at VITO in the visible and infrared range covering a 2.15 km swath daily. The Earth’s land is covered daily at 350 metres resolution.

For high-resolution imagery above 5 metres GSD, some imagery is free – but most is still very costly.

Landsat 8, launched in 2013, was the first free worldwide provider of high-resolution optical data. Geocoded products with a resolution of 15 metres in panchromatic and 30 metres in eight MS bands are available within 24 hours. Its 170 km swath makes it an optimum data source for agricultural statistics.

Sentinel 1 radar imagery in the C band has been available free since 2014. Operating in four modes and with variable polarimetry, its main purpose is disaster monitoring; it should therefore be seen as an auxiliary source of information for agriculture, particularly in regions with persistent cloud

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coverage. Imagery can be resampled at 20 metre resolution; its swath is 250 km.

Resourcesat-2 (ISRO, IRS-P6), launched in 2011, has three instruments:

• LISS-4 with 5 metre resolution from three bands in the visible and near-infrared range; a 70x70 km image costs US$2,750;

• LISS-3 with 23 metre resolution from four bands in the visible, near-infrared and short-wave infrared; a 170x170 km image costs US$2,750; and

• AWIFS with 56 metre resolution from four bands in the visible, near-infrared and short-wave infrared; a 350x350 km image costs US$850.

The four CBERS-2B satellites launched in 2010 and 2014 under a Brazilian/Chinese cooperation deliver imagery with various characteristics: panchromatic at 5 metres GSD in a 60 km swath, and MS 4 bands in the visible and near-infrared at 10.2 metre and 64 metre GSD in swaths of 60 km, 120 km and 866 km. Imagery is free in the INPE reception area; beyond Central and South America other reception stations cover most of Africa, Russia and China. It is not easy to order the images.

Spot 7, launched in 2014, has the same characteristics as its predecessor SPOT6, which is still operational. It offers a panchromatic band at 2.2 metre resolution and four MS bands at 8.8 metres. A 60x60 km image costs about US$5,000, giving a price of US$1.5 per km2 for non-processed archived products. Geocoded programmed XS products cost aboutUS$3.50 per km2.

The five Rapideye satellites have served the agriculture and agricultural insurance sectors since 2008. Archived images from four multispectral bands in the visible and near-infrared range covering a swath of 77 km can be ordered for about US$1 per km2.

The second-generation DMC series launched in 2009 – UK-DMC 2 and DEIMOS 1 – provides three bands in the visible and near-infrared range at 22 metre GSD in a 650 km swath that can be up to 1,800 km long. The system has been used operationally by the European Commission and the United States. Archive imagery costs 9 US cents per km2. In 2011, NigeriaSat 2 and NigeriaSat X also started to provide panchromatic imagery at 2.5 metre resolution and MS imagery at 5 metres resolution in a 22 km swath.

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The Airbus DS FORMOSAT-2 has been delivering 2 metre panchromatic and 8 metre multispectral imagery in a swath of 24 km since 2004. Products cost US$4.5 per km2.

Thailand financed the 2008 launch of THEOS, which has a 2 metre panchromatic band and three multispectral bands at 15 metre resolution. Data are not generally accessible outside the Geo-Informatics and Space Technology Development Agency.

Aster is an old US/Japanese instrument on the Terra satellite launched in 1995. With a 15 metre GSD and a swath of 60 km, it delivers four bands in the visible and near-infrared range at 90 US$ per scene; new acquisitions cost US$350.

Canada’s Radarsat, launched in 2007, is a taskable C-band SAR providing ascending and descending imagery. The costs per image is about US$3,600 for resolutions from 8 metres to 100 metres and swaths from 50 km to 500 km.

RISAT 1 and 2 launched in 2012 and 2014 provide C-band multi-polarimetric and multi-resolution imagery in a swath of 115 km in 25x8 MRS resolution at a cost of US$190 for Indian users. Worldwide imagery is available from Tromsö station.

ALOS-2 launched in 2014 is unlike ALOS-1 in that it does not provide optical images – only SAR L-band imaging at a GSD of between 3 metres and 100 metres. One of its objectives is the monitoring of rice growing in Asia.

Other radar imagery is available, but it has limited applicability to agricultural statistics because of the image bands, re-sampling and prices. The Cosmoskynet system of four satellites is dedicated to interferometry; the same holds for Terrasar X.

For very high resolution of less than 5 metres GSD, most of the available imagery is so costly that it is unlikely to be accessible for agricultural statistics. The available systems are listed below.

Google Earth provides free access to very high resolution aerial and satellite imagery for most of the world. Its use in agricultural statistics has its limitations due to changes in acquisition dates, geometry or radiometry, but Google Earth material can be useful for printing field survey documents or stratifying land cover. All printed images must show a visible Google copyright logo.

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Bing provides Microsoft aerial photographs on the Bing Maps application. Limited use can be made of it without a licence, so usage for agricultural statistics may be allowed.

Digital Globe launched the Quickbird-2 satellite in 2001 and the WorldView 1, 2 and 3 satellites in 2007, 2009 and 2014. Exceptional spatial resolution is offered with 31 cm GSD in eight bands in the visible and near infra-red range. Prices reflect the quality of the service; archive imagery costs US$20 per km2.

The Korean KOMPSAT-3 launched in 2012 delivers panchromatic and MS 4 bands in the visible and near infra-red range at GSDs of 0.7 metres and 2.8 metres. For standard tasks, the cost is US$16 per km2 from GISTDA; archive products cost US$8 per km2 from Apollomapping. KOMPSAT-5 launched in 2013 is an X-band SAR satellite with 1 metre to 20 metre re-sampling.

The Airbus DS Pleiade 1A and 1B launched in 2013 provide panchromatic 0.7 metre and MS 2.8 metre imagery in the red/green/blue and near-infrared ranges. They can be tasked to cover strips of up to 150x40 km. The costs of archive products is about US$13 per km2.

Images from Ikonos 2, launched in 1999, in the red/green/blue and near-infrared ranges at 82 cm and from Geoeye-1, launched in 2008, in the same ranges at 41 cm can be ordered from e-GEOS or Apollomapping. Prices vary according to the product; archive imagery ranges from US$15 to US$40 per km2.

EROS B, launched in 2006, was an ImageSat satellite delivering 70 cm panchromatic imagery in a 7 km swath at a cost of US$7.5 per km2, available from Apollomapping.

The SkySat 1 and 2 satellites, now owned by Google, were launched in 2013 and 2014 as the first components of a 24-satellite system for daily passes with panchromatic 90 cm and MS 2.0 metre imagery in the red/green/blue and near-infrared ranges in an 8 km swath. They can be tasked to provide videos. New and archive imagery costs US$10 per km2; videos cost US$5,000 for 30 seconds.

Planet Labs USA launched its first three satellites in 2013. In February 2014 and July 2015, the Flock1 and 2 constellation (41 doves, 3-5m MS resolution, no infrared band. See https://www.planet.com/flock1/) was deployed from the

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International Space Station. The plan is to increase the number of doves to 131 in 2015. In September 2015, Planet Labs announced a US$60 million grant of imagery for the benefit of the UN agencies.

6.2. The immediate future of imagery and software

The main development in the use of remote sensing for agriculture applications will be the services of the Copernicus programme provided by the Sentinel system. Sentinel-2, launched in June 2015 to monitor vegetation, provides images in 13 bands in the visible, near-infrared and shortwave infrared ranges with a GSD of 10 metres in four bands with a ten-day repeat cycle at equator; the swath is 290 km. Its systematic land-acquisition mode will guarantee image acquisition except where there is cloud cover.

In addition to real-time delivery of five standard products, an open-source portable toolbox documented, tested and validated on the 12 JECAM sites will provide cloud-free surface reflectance composites, dynamic cropland masks, cultivated crop-type maps and area estimates, and vegetation status.

In the context of its climate change initiative, ESA has identified five essential climate variables for mapping: land cover, fires, ice sheets, glaciers and soil moisture. For Africa, the intention is to deliver monthly 10–20 metre land-cover maps to monitor vegetation phenology. Future services will integrate Sentinel-2 daily 10 metre 5 gigabytes images, Landsat-8 30 metre images, PROBA-V 100 metre images and Sentinel-3 300 metre imagery. Archive land cover imagery and a toolbox are freely downloadable (see: http://maps.elie.ucl.ac.be/CCI/viewer). Project progress can be followed at http://www.esa-landcover-cci.org/?q=node/126

The other major change will come from Google. Six additional SkySat satellites will be launched in 2016, providing vastly enhanced image acquisition capability, though less than that of the planned constellation of 23 satellites.

Another interesting development is the Earth Engine application, which enables real-time data access to the Landsat 8 and Sentinel 1 and 2 satellites. A user can work on a personal computer to access pre-processed data on radiometry and geometry, apply the analysis routines and integrate the outcome with images or GIS data sets. It is currently limited to the scientific community, but professional licences can be expected in the near future; prices will reflect the Google business model.

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It is also worth mentioning that in 2015, NOAA launched its Big Data Alliance initiative, selecting five partners (Amazon, IBM, Google, Microsoft and the Open Cloud Consortium) for archiving, distribution, and enhancement of its 20TB daily acquisition; opening access to these would give rise to a business growth of US$3 trillion (https://data-alliance.noaa.gov)

Additional commercial optical satellite launches have been announced.

Three additional DMC-3 satellites will service users of remote sensing from 2015. The Twenty First Century Aerospace Technology Company of China has bought all the imagery for a seven-year period. With daily revisits, DMC-3 will provide panchromatic 0.50 GSD and MS 3 metre imagery, which will be a major improvement over the Beijing-1 satellite imagery available since 2005.

The United States/Argentina Satellogic group launched three MS satellites in 2013 and 2014: CubeBug1 and 2 and BugSat-1 provide 2 metre resolution in current tests, and the company plans to launch 15 more 1 metre MS satellites in 2015 with a two-hour revisiting period anywhere on the globe. The idea is to sell more than raw imagery, and the company envisages increasing the number of satellites to a possible 300.

An ALOS-3 optical satellite to be launched for JAXA in 2016 will have a panchromatic band of 0.8 metre GSD and a swath of 50 km, and four MS bands of 5 metre GSD and a swath of 90 km.

6.3. The use of drones for field surveys: ITA, NBS and VITO

6.3.1. ITA in Malawi

In a World Bank project, in 2015 Malawi has been testing a point area frame for its 118,500 km2 area. This context created, for the contractor ITA, an opportunity to examine the use of drones for agricultural surveys in an African country.

The Ministry of Agriculture needs crop estimates in February, April and June. An early area survey of maize in March covered 300 clusters of 16 points on a local 250 metre grid defining a 1 km2 segment in which fields were measured with GPS.

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A full crop area survey consisted of 1,500 clusters and 25,000 points targeting a CV for maize of approximately 5 percent. Points were clustered because of the time and cost involved for accessing 25,000 individual points and the low spatial correlation between points distant 250 metres apart.

A final production estimate was based on 900 farm interviews; it covered maize and some other crops. The fieldwork was done by the Ministry of Agriculture.

ITA tested the feasibility and accuracy of using drones to acquire images because the survey involved hiring and running 25 vehicles for 50 days at a cost of US$250,000. A FLYGEO drone was used by the Brindisi-based STF company on 20 of the 1 km2 clusters of 16 points. It was equipped with a Sony Alpha 6000 camera providing 2 cm GSD colour images and cost US$20,000; it had a wingspan of 2 metres, length of 75 cm and weight of 1.8 kg and could fly for three hours at a distance of 3 km guided by GPS with pre-set waypoints; altitude was selected on the basis of the Google 3D digital elevation model.

Customs restrictions meant that the drone had to be imported unofficially, and no flight authorization could be obtained. Six days and 500 km of road travel were necessary to cover the 20 segments compared with 15 minutes of flight per cluster. Photographs covering areas of 117x78 metres required 200 gigabytes of post-processed data using APS and Photoscan software.

The main conclusions are:

• Even though the imagery is impressive, the mixture of crops may have been too great for the imagery used: 30 percent of maize fields are mixed with pigeon peas, vegetables, cow peas, sorghum, ground peas, soya, cassava, tobacco and sweet potatoes; even with 2 cm resolution, the secondary crops could not be identified.

• The drone had to be imported under cover in separate pieces and flight authorization could not be given, so it could not be used for official surveys.

• The time advantage of using the drone to cover three clusters per day was not great in that the field survey covered 1.5 clusters per day.

6.3.2. NBS in China

In Inhai province in 2014, NBS obtained 390 images with a drone equipped with a Sony Nex-7 camera with a frame of 6,000x4,000 pixels. The TH-D Technology Company provided the service. The drone flew for 40 minutes to

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cover a segment of 2x2 km and provide a mosaic of 300x200 metre images. GSD was 5 cm. The August 2014 image below shows corn and peanuts.

6.3.3. Drones at VITO Belgium

In recent years, VITO has begun research on and to examine applications of drone technology.

• In compliance with the legal limitations restricting the use of drones in the European airspace, VITO developed the first drone management platform that allows commercial drone operators and air traffic controllers to make use of this airspace.

• Initially dedicated to the monitoring of pipelines, their use of drones expanded to more diversified applications: monitoring of pest spreading in orchards for administrative bodies, monitoring of field trials (breeding and phytosanitary experiments) for the private sector.

• In light of the constraints arising cloud coverage on satellite images acquisition, VITO experimented with large-scale mapping and crop conditions monitoring in South America (specifically, Peru).

The main types of drones experimented with by VITO are listed in the Table below. It should be noted that at least the “SenseFly eBee” fits into an ordinary commercial airplane’s overhead locker, which facilitates its international

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transport. VITO’s experience appears to confirm the robustness of the drones and payloads tested.

Name Manufacturer Payload

Draganfly X6

Draganfly Innovations Inc., 2108 St. George Avenue, Saskatoon, SK S7M0K7, Canada

Panasonic LX3

Aerialtronics AT8

Aerialtronics, Dr. Lelykade 20, 2583CM Den Haag, Netherlands

Sony Nex 6

SenseFly eBee

Sensefly, Route de Genève 38, (Z.I. Châtelard Sud)

1033 Cheseaux-Lausanne, Switzerland

Canon IXUS

RE camera MultiSPEC

Aerialtronics ATX8

Aerialtronics, Dr. Lelykade 20, 2583CM Den Haag,

Netherlands Sony Nex 6

Panasonic GH4 + gimbal

ET-Air Cruiser

ET-Air, Stangelandsv. 190b, N-4354 Voll, Norway

Multiplex Mentor & Cularis

Aerobertics, Maalse Steenweg 367, 8310 St-Kruis, Brugge

UAV Navigation Oculus

UAV Navigation, Avda Pirineos 7, B11, 28703, San Sebastian de los

Reyes, Spain

OTUS 170 Gimbal

Explorer

Aerobertics, Maalse Steenweg 367, 8310 St-Kruis, Brugge

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