use of remote sensing technology in crop monitoring and ... · use of indian sar (risat) data for...

FAO/ADB Expert Meeting on Crop Monitoring for Improved Food Security, 17 February, Vientiane, Lao PDR

Use of Remote Sensing Technology in

Crop Monitoring and Assessment of

Impact of Natural Disaster

Shibendu S. Ray

Mahalanobis National Crop Forecast Centre

Department of Agriculture & Cooperation

Government of India

[email protected]

www.ncfc.gov.in


• Mahalanobis National Crop Forecast Centre was established under Ministry of Agriculture with technical and human resources support from Indian Space Research Organization. Centre was inaugurated on 23rd April, 2012.

• Named after great Indian Statistician P. C. Mahalanobis. Located in New Delhi, India.

• An initiative towards institutionalization of Remote Sensing Applications in Agriculture in the country.

• A state-of-the art RS&GIS lab

• The Mandate of the Centre is to use geospatial

technology for agricultural assessment.

• Responsible for all operational crop forecasting and drought assessment in the country using Remote Sensing Data.

• Jointly works in collaboration with 18 state agriculture departments.

Mahalanobis National Crop Forecast Centre

Email: [email protected] Web: www.ncfc.gov.in


FASAL (Forecasting Agriculture using Space, Agrometeorology and Land based observations)

Aims at providing multiple pre-harvest production forecasts of crops at National/State/ District level

National/State forecasts: • Kharif Rice (3 Forecasts, 13 states) • Rabi Rice (1, 4) • Wheat (3,6) • Winter potato (2, 4) • Rapeseed & Mustard (3,5) • Jute (1,3)

State/District forecasts: • Cotton (1,7) • Sugarcane (1,3) • Rabi Sorghum (1,2)


Salient Features for Crop Assessment using Remote Sensing

•Multi-date Microwave (SAR) for Rice & Jute and Multi-date/Single-date Optical (AWiFS/ LISS III) for other crops

Data

•Stratified Random Sampling

Approach

•All those sates, which together contribute >85% of the Crop’s area in the Country

States Covered

•Multiple, starting from 1 month of crop to pre-harvest

Periodicity

•Hierarchical for Multi-date SAR; Hybrid (combination of supervised & unsup. for multi-date optical); MXL for single date optical

Classification Approach

•FASALSoft, developed by ISRO

Software

•Agro-meteorological, progressing towards spectral & simulation

Yield Forecast

•MNCFC, State Agrl. Dept., IMD, SAC (ISRO)

Organisations Involved


Use of Multi-date Optical Data for Crop Classification


Use of Indian SAR (RISAT) Data for Rice & Jute

• Indian SAR satellite RISAT-1 was launched in April 26,

2012 and data was available for user from November,

2012.

• Crop (Rice & Jute) assessment is the major use of

RISAT-1 data (MRS mode).

• Multi (3) date data is used for crop assessment.

MRS Data Coverage in Kharif Rice


Smartphone based Ground truth Collection

5680 points covering 16 states between August to January

(2013-14)


Crop Forecast Schedule and Variation

Estimates Rice Wheat

Remote Sensing DES Remote Sensing DES

1st Aug End Sep End Jan End Feb 1st Wk.

2nd Sep End Feb 1st Wk. Feb End April Last Wk.

3rd Jan, 1st Wk. (Final) April Last Wk. Mar End (Final) July Mid

4th --- July Mid --- ----

Final --- Sep End --- Sep End

*Million Bales (180 kg)

Crop Year Area (Million Hectares) Production (Million Tonnes)

MNCFC DES % Diff. MNCFC DES % Diff. Jute (F1) 2013-14 0.72 0.76 -5.3 10.06* 10.60* -5.1

Rice (F2) 2013-14 37.32 38.71 -3.7 82.16 92.32 -11.0 Rapeseed& Mustard

(F3) 2012-13 5.53 6.34 -12.7 6.95 7.82 -11.1

Winter Potato (F2) 2012-13 1.51 NA 32.17 NA

Wheat (F3) 2012-13 29.28 29.65 -1.2 85.61 92.46 -7.4

Rabi Rice (F1) 2012-13 3.69 3.56 3.8 11.73 11.64 0.8

FASAL Forecasts

24 Forecasts in 20 Months


Remote Sensing driven Crop Cutting Experiments in Bihar

Rice Crop Map was generated using such as RISAT-1 MRS data

Resourcesat-2 AWiFS time composite NDVI during Sep 2nd FN to Oct 1st FN was extracted for rice.

Three classes (A, B, C) were defined based on frequency distribution of NDVI Values.

37 points were randomly selected in 22 districts of Bihar

Crop cutting experiment was carried out under the supervision of MNCFC.

Yield models were developed between NDVI and yield and yield map generated

Efficiency of stratification was very high.


Use of Indigenous Software

Operational use of indigenously developed software for Crop Forecasting

(FASALSoft, developed by SAC/ISRO) and Drought Assessment (NAS, developed

by NRSC/ISRO)


0 0.1 0.2 0.3 0.35 0.4 0.45 0.5 0.6 0.7 > 0.7

November 1-10 November 11-20 November 21-30 December 1-10 December 11-20

December 21-30 January 01-10 January 11-20 January 21-30 January 31-feb 10

Map of Rabi Season

Emergence Area in 6

States

Crop Emergence Progression


Drought Assessment

'National Agricultural Drought Assessment and Monitoring System (NADAMS)' project, conceptualized and developed by ISRO, Department of Space

Provides near real-time information on prevalence, severity level and persistence of agricultural drought at state/ district/sub-district level

Currently, it covers 13 states of India, which are predominantly agriculture based and prone to drought situation.

Agricultural conditions are monitored at state/district level using daily NOAA AVHRR and MODIS data for 13 states.

AWiFS (Advanced Wide Field Sensor) of Resourcesat (56 m resolution) is used for detailed assessment of agricultural drought at district and sub district level in four states

Monthly report of drought condition is provided to the all concerned agencies in Centre and State under NADAMS.

From the year 2012, the NADAMS project is being implemented by the Mahalanobis National Crop Forecast Centre (MNCFC), Ministry of Agriculture


Methodology for Agricultural Drought Assessment

NDVI/NDWI anomaly

Assessment (1)Relative dev. (2)VCI (3)In season change

Agricultural Drought Situation

Change in Crop Calendar

Lag between VI & Rainfall

Abnormal Weather Events (e.g. Floods)

Irrigation Percentage

Rainfall deviation

Sown area deviation

Ground Information

Additional Information

Multi-satellite,

Multi-date Data

VI Time Composite Drought

Warning (June, July,

August)

Normal

Watch

Alert

Drought Declaration (Sep, Oct)

Mild

Moderate

Severe


Agricultural Drought Assessment for September 2013

(No. of Districts)

State Normal Mild Moderate

Andhra Pradesh 18 3 1

Bihar 16 16 5

Chhattisgarh 13 2 0

Gujarat 25 0 0

Haryana 20 0 0

Jharkhand 12 7 3

Karnataka 25 3 2

Maharashtra 34 0 0

Madhya Pradesh 45 0 0

Odisha 30 0 0

Rajasthan 32 0 0

Tamil Nadu 26 4 0

Uttar Pradesh 61 8 0

Total 357 43 11


AVHRR based Normalized Difference Vegetation index

MODIS based Normalized Difference Water

index

Agricultural Drought Assessment - Bihar

September 2013 (upto 21st)

Seasonal Rainfall upto 30/09/13

Agricultural drought assessment –September

2013

(Source: www.imd.gov.in) September 2013

NO DATA

Legend

NO RAIN (-100%)

SCANTY ( -60% TO -99%)

DEFICIENT2 (-40% TO -59%)

DEFICIENT1 (-20% TO -39%)

NORMAL ( +19% TO -19%)

EXCESS (+20% OR MORE)

September 2011

September 2011


Odisha Rice-Flood Assessment

(Rice Map of Odisha from RISAT Data) (Flood Map of Odisha, upto 17th Oct., ISRO)

An Estimate of District wise Rice Inundation Area

(Assessment Upto 17th October, 2013)

(Rice Flooded Map of Odisha , upto 17th Oct.)


VCI (NDVI) VCI (NDWI)

TCI

• Vegetation Condition Index(NDVI) = 100*(NDVI-NDVImin)/(NDVImax-NDVImin)

• Vegetation Condition Index(NDWI) = 100*(NDVI-NDVImin)/(NDVImax-NDVImin)

• Temperature Condition Index = 100*(LSTmax - LST)/(LSTmax – LSTmin)

Vegetation & Temperature Condition Index

Agricultural Condition (January 2014)


VCI

(NDWI)

VCI > 40

20 < VCI < 40

VCI < 20

VCI > 40

20 < VCI < 40

VCI < 20

TCI

TCI > 40

20 < TCI < 40

TCI < 20

VCI

(NDVI)

Combined

District-level Crop Alert


Tasks Ahead

More Crops and More Area

Spectral Yield Mapping

Biotic & Abiotic Disaster impact on Agriculture

Agricultural Development Monitoring

Crop Insurance

Global Agricultural Monitoring

Automatization, Web-GIS, Portals…..


use of remote sensing technology in crop monitoring and ... · use of indian sar (risat) data for...

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