Russian Academy of Sciences Space Research Institute

Agricultural monitoring of using Remote Sensing: an overview

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 Sown area distribution in Russia

< 1% 1-10% 10-20% 20-40% > 40%

% of sown area by administrative according to official statistics Source: GOSKOMSTAT Main crops in Russia

32% 6% 19% 9%

18% 5%

34%

23% 54% Spring wheat Grains Spring barley Forage crops Winter wheat Industrial crops Winter rye Vegatables Other

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 VEGETATION data products: MODIS data products: S10 products MOD09GHK, MOD09GQK, MODMGGAD, (ten-days maximum NDVI composites) MOD09GST (Surface Reflectance Products) Geographical coverage: Geographical coverage: Northern Hemisphere (above 40ºN) Northern Eurasia (above 40ºN) Time frame: 1998 – ongoing Time frame: 2002 – ongoing Temporal resolution: 10 days Temporal resolution: daily Spectral bands: Main spectral bands used: i. 430 – 470 nm i. 440 – 480 nm ii. 610 – 680 nm ii. 620 – 670 nm iii. 780 – 890 nm iii. 841 – 976 nm iv. 1580 – 1750 nm iv. 1630 – 1650 nm Spatial resolution: 1.15km (nadir view) Spatial resolution: 250&500m (nadir view) MODIS receiving stations for agricultural monitoring in Russia MODIS data pre-processing steps

MODIS 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 product MOD13A3.A2006182.h19v02.004 MOD13A3.A2006182.h20v02.004

RGB: 841 - 876 nm 2105 - 2155 nm 620 - 670 nm

Improved MODIS-Terra 250&500m product Start date – 2005/07/01 End date – 2005/08/01 MODIS derived arable lands map for Russia MODIS derived arable lands map of Russia

Rostovskaya Stavropolskiy kray Comparison of MODIS derived arable lands with GLC 2000

MODIS derived map GLC2000 Comparison of MODIS derived arable lands with land-cover map

- MODIS arable lands

Land-Use map: - arable lands - non-arable lands

Tambovskaya and Penzenskaya of Russia Source: Land-cover map of USSR, 1:4 million, 1989 Comparison of MODIS derived arable lands area with official statistics Crop types classification using MODIS time-series data

PVI 0,30

winter wheat 0,25 spring barley 0,20 sunflower

0,15

0,10

0,05

0,00 2.1.2005 3.3.2005 4.3.2005 5.3.2005 6.3.2005 7.3.2005 8.3.2005 9.2.2005

April 30, 2003 May 25, 2003 10.3.2005 Time

0,3 PVI Snow Fallow land Winter crop 0,25

0,2

0,15

0,1

0,05

0

-0,05 August 30, 2003 September 23, 2003 9.7.2002 2.4.2003 2.19.2002 4.10.2002 5.30.2002 7.19.2002 12.31.2001 10.27.2002 12.16.2002 Time PVI decrease PVI increase MODIS derived crop types : Rostov , 2003 Winter crop

Sunflower

Clean fallow

– Clean fallow – Water bodies – Deciduous forest – Winter crops – Grassland – Sunflower – Built-up area Validation test sites in Rostov region Arable lands area comparison 3000 y = 1,04x - 46,9 R2 = 0,93 2500

2000

1500

1000 MODIS derived data (sq.км)

500

0 0 500 1000 1500 2000 2500 3000 official satatistics (sq.км)

Clean fallow Winter crops Sunflower Rice acreage assessment

250 1

0.9 200 0.8

150 Rice in 0.7

100 0.6 NDVI (rel.value) , 0.5

50 Ccorr Russia 0.4 0 0.3 id 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 dekad 0.2

0.1

0 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 dekad

rice acreage Calculated based on vector masks Predicted at the beginning (1000 ha) of the season based on MODIS according: 2004 2005 2006 2007 2008 vector 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 Crops affected by drought 2009

Mostly affected regions

Maximal impact on early grain crops

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 (winter wheat)

w w_y = -80,16 + 155,69 * ndv i _ar R-Square = 0,93 A

40,0 A

A

A A A A

A

30,0 ww_y

20,0

A Li near Regression wi th 95,00% Mean Prediction Interval

0,6500 0,7000 0,7500 ndvi_ar region O2 O3 J3 F1 F2 F3 M1 M2 M3 A1 A2 A3 M1 M2 M3 J1 J2 Kabardino-Balkaria 0 0 0.928095 0.990346 0 0 0 0 0 0 0 0 0 0 0 0 Karacheavo- Cherkessia 0 0 0 0 0 0 0 0 0 0.918450 0 0 0 Stavropol 0 0 0 0 0 0 0 0.819710 0 0 0 0.896070 0.971387 Krasnodar 0 0 0 0 0 0 0 0.838434 0 0 0 0 0.892490 0.942365 0.956444 Adygea 0 0 0.932812 0 0 0 0 0 0 0 0 0 0.974098 0 Rostov 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.946906 Volgograd 0 0.891109 0 0 0 0 0 0 0 0 0 0 0 0 0 Voronezh 0 0 0 0 0 0.803667 0.878573 0 0 0.997912 Belgorod 0 0 0 0 0.987377 0.995856 0 0 Orenburg 0.840599 0 0 0 0 0 0 0 0 0 0.862699 Kursk 0 0 0 0 0 0 0 0 0 0.878315 0.982881 Samara 0 0 0 0 0 0.902545 0 0 0.938675 0 0.955563 Tambov 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