Agricultural monitoring of Russia using Remote Sensing:
an overview
Russian Academy of Sciences Space Research Institute
Savin I., Bartalev S., Loupian E.
Some features of R&D at IKI Focus is on national level (entire Russia) monitoring with
application, if suitable, to sub-continental, or potentially, global coverage
Primary sources of EO data are moderate resolution satellite instruments (mainly MODIS and SPOT-VGT), while resent developments in Russia are rapidly increase the potential role of high-res. (e.g. SPOT-HRV/HRVIR) data for national monitoring
Focus on long-term time-series data analysis
Development of automatic satellite data receiving and processing chains to perform monitoring in the routine and repeatable manner
Source: GOSKOMSTAT
< 1%1-10%10-20%20-40%> 40%
% of sown area by administrative regions according to official statistics
Sown area distribution in Russia
Main crops in Russia
54%
32%9%
5%
GrainsForage cropsIndustrial cropsVegatables
34%
23%
18%
6% 19%
Spring wheatSpring barleyWinter wheatWinter ryeOther
Source: GOSKOMSTAT
Agricultural Monitoring with EO data in Russia
Development of the national agricultural monitoring system with use of EO data has been initiated by Russian Ministry of Agriculture in year 2003
Main agricultural monitoring system developing institutions:
Main Computational Center, Russian Ministry of Agriculture
Space Research Institute, Russian Academy of Sciences
Main thematic focuses of agricultural monitoring development
Arable lands area and dynamic assessment
Crop / land-use type mapping
Monitoring of impact of extreme meteorological conditions on crop growth
Crop yield forecast and assessment
Earth Observation data for agricultural monitoring of Russia
(i) Operative data
- NOAA-AVHRR
- Terra-MODIS
- SPOT-Vegetation
- SPOT-HRV/HRVIR
(ii) Historical data- Landsat-TM/ETM (1990-1995-2000)
(iii) Data under consideration for nearest future - IRS-AWIFS
- Kosmos-CX
VEGETATION and MODIS data archive at IKI
MODIS data products:MOD09GHK, MOD09GQK, MODMGGAD,
MOD09GST(Surface Reflectance Products)
Geographical coverage:Northern Eurasia (above 40ºN)Time frame: 2002 – ongoingTemporal resolution: dailyMain spectral bands used:
i. 440 – 480 nmii. 620 – 670 nmiii. 841 – 976 nmiv. 1630 – 1650 nm
Spatial resolution: 250&500m (nadir view)
VEGETATION data products:S10 products
(ten-days maximum NDVI composites)
Geographical coverage:Northern Hemisphere (above 40ºN)Time frame: 1998 – ongoingTemporal resolution: 10 daysSpectral bands:
i. 430 – 470 nmii. 610 – 680 nmiii. 780 – 890 nmiv. 1580 – 1750 nm
Spatial resolution: 1.15km (nadir view)
MODIS receiving stations for agricultural monitoring in Russia
MODIS data pre-processing stepsMODIS daily products
Snow/cloud detection
Cloud shadow detection
Best resolution selection and temporal compositing
Comparison with standard MODIS monthly data composites
Standard MODIS-Terra
MOD13A3 1km productMOD13A3.A2006182.h19v02.004
MOD13A3.A2006182.h20v02.004
Improved MODIS-Terra
250&500m productStart date – 2005/07/01
End date – 2005/08/01
RGB:841 - 876 nm
2105 - 2155 nm620 - 670 nm
MODIS derived arable lands map for Russia
MODIS derived arable lands map of Russia
Rostovskaya oblast Stavropolskiy kray
MODIS derived map GLC2000
Comparison of MODIS derived arable lands with GLC 2000
Comparison of MODIS derived arable lands with land-cover map
- MODIS arable lands
Tambovskaya and Penzenskaya oblasts of RussiaSource: Land-cover map of USSR, 1:4 million, 1989
- arable lands- non-arable lands
Land-Use map:
Comparison of MODIS derived arable lands area with official statistics
Crop types classification using MODIS time-series data
PVI increasePVI decrease
April 30, 2003 May 25, 2003
September 23, 2003August 30, 2003-0,05
0
0,05
0,1
0,15
0,2
0,25
0,3
12.3
1.2
001
2.1
9.2
002
4.1
0.2
002
5.3
0.2
002
7.1
9.2
002
9.7
.2002
10.2
7.2
002
12.1
6.2
002
2.4
.2003
Time
PVI
0,00
0,05
0,10
0,15
0,20
0,25
0,30
2.1.
2005
3.3.
2005
4.3.
2005
5.3.
2005
6.3.
2005
7.3.
2005
8.3.
2005
9.2.
2005
10.3
.200
5
Time
winter wheat
spring barley
sunflower
PVI
Snow Winter cropFallow land
MODIS derived crop types : Rostov region, 2003
– Water bodies– Deciduous forest– Grassland– Built-up area
Sunflower
Winter crop
Clean fallow
– Clean fallow– Winter crops– Sunflower
Validation test sites in Rostov region
Clean fallow SunflowerWinter crops
Arable lands area comparison
y = 1,04x - 46,9R2 = 0,93
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000official satatistics (sq.км)
MO
DIS
der
ived
dat
a (s
q.км
)
Rice acreage assessment
0
50
100
150
200
250
id 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
dekad
ND
VI
(rel
.val
ue) Rice in
Kalmykia, Russia
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Cco
rr
dekad
rice acreage(1000 ha) according:
Calculated based on vector masks Predicted at the beginning of the season based on MODIS
2004 2005 2006 2007 2008vector masks 4.9 5.4 5.8 5.8 5.4
official statistics 4.8 5.1 5.5 5.6 5.7
Crops affected by spring frost
20-24 April 2009
Mostly affected
regions
Maximal impact
on early grain
crops
Crops affected by drought 2009
Maximal impact
on later grain
and technical
crops
Crops affected by drought
July 2009
Yield assessment with NDVI time-series by administrative regions (year-analogue method)
Crops yield prediction based on regression analysis
Li near Regressi on wi th95,00% Mean Prediction Interval
0,6500 0,7000 0,7500
ndv i_ar
20,0
30,0
40,0
ww
_y A
A
AA
A
A
A
A
A
w w_y = -80,16 + 155,69 * ndv i _arR-Square = 0,93
Adygea (winter wheat)
region O2 O3 J3 F1 F2 F3 M1 M2 M3 A1 A2 A3 M1 M2 M3 J1 J2Kabardino-Balkaria 0 0 0.928095 0.990346 0 0 0 0 0 0 0 0 0 0 0 0Karacheavo-Cherkessia 0 0 0 0 0 0 0 0 0 0.918450 0 0 0Stavropol 0 0 0 0 0 0 0 0.819710 0 0 0 0.896070 0.971387Krasnodar 0 0 0 0 0 0 0 0.838434 0 0 0 0 0.892490 0.942365 0.956444Adygea 0 0 0.932812 0 0 0 0 0 0 0 0 0 0.974098 0Rostov 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.946906Volgograd 0 0.891109 0 0 0 0 0 0 0 0 0 0 0 0 0Voronezh 0 0 0 0 0 0.803667 0.878573 0 0 0.997912Belgorod 0 0 0 0 0.987377 0.995856 0 0Orenburg 0.840599 0 0 0 0 0 0 0 0 0 0.862699Kursk 0 0 0 0 0 0 0 0 0 0.878315 0.982881Samara 0 0 0 0 0 0.902545 0 0 0.938675 0 0.955563Tambov 0 0 0 0 0 0 0 0 0 0 0.932496 0 0 0 0 0 0
User access to agricultural monitoring results
www.agrocosmos.gvc.ru
Forthcoming Challenges
To extend arable lands map for entire Northern Eurasia region
To develop operational mode for crop types mapping on entire Russia level
To develop operational land-use change monitoring (e.g. land abandonment, aforestation, newly-ploughed virgin lands and etc.)
To develop operational monitoring of risk of crop damage due to insects invasions (locusts, Colorado potato beetle…)
To combine moderate and high-resolution satellite data to improve crop area estimated accuracy
To introduce a new methods of crop yield forecasting based on crop growth modeling