Agricultural monitoring of Russia using Remote Sensing: an overview

Transcription

1 Russian Academy of Sciences Space Research Institute Agricultural monitoring of Russia using Remote Sensing: an overview Savin I., Bartalev S., Loupian E.

2 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

3 Sown area distribution in Russia < 1% 1-10% 10-20% 20-40% > 40% % of sown area by administrative regions according to official statistics Source: GOSKOMSTAT

4 Main crops in Russia 32% 9% 6% 19% 5% 18% 34% 54% Grains Forage crops Industrial crops Vegatables 23% Spring wheat Spring barley Winter wheat Winter rye Other Source: GOSKOMSTAT

5 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

6 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

7 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 ( ) (iii) Data under consideration for nearest future - IRS-AWIFS - Kosmos-CX

8 VEGETATION and MODIS data archive at IKI VEGETATION data products: S10 products (ten-days maximum NDVI composites) MODIS data products: MOD09GHK, MOD09GQK, MODMGGAD, MOD09GST (Surface Reflectance Products) Geographical coverage: Northern Hemisphere (above 40ºN) Time frame: 1998 ongoing Temporal resolution: 10 days Spectral bands: Geographical coverage: Northern Eurasia (above 40ºN) Time frame: 2002 ongoing Temporal resolution: daily Main spectral bands used: i nm i nm ii nm ii nm iii nm iii nm iv nm iv nm Spatial resolution: 1.15km (nadir view) Spatial resolution: 250&500m (nadir view)

9 MODIS receiving stations for agricultural monitoring in Russia

10 MODIS data pre-processing steps MODIS daily products Snow/cloud detection Cloud shadow detection Best resolution selection and temporal compositing

11 Comparison with standard MODIS monthly data composites Standard MODIS-Terra MOD13A3 1km product MOD13A3.A h19v MOD13A3.A h20v RGB: nm nm nm Improved MODIS-Terra 250&500m product Start date 2005/07/01 End date 2005/08/01

12 MODIS derived arable lands map for Russia

13 MODIS derived arable lands map of Russia Rostovskaya oblast Stavropolskiy kray

14 Comparison of MODIS derived arable lands with GLC 2000 MODIS derived map GLC2000

15 Comparison of MODIS derived arable lands with land-cover map - MODIS arable lands Land-Use map: - arable lands - non-arable lands Tambovskaya and Penzenskaya oblasts of Russia Source: Land-cover map of USSR, 1:4 million, 1989

16 Comparison of MODIS derived arable lands area with official statistics

17 Crop types classification using MODIS time-series data PVI 0,30 0,25 0,20 winter wheat spring barley sunflower 0,15 0,10 0,05 0,00 April 30, 2003 May 25, Time 0,3 PVI 0,25 Snow Fallow land Winter crop 0,2 0,15 0,1 0,05 0 August 30, 2003 September 23, , PVI decrease PVI increase Time

18 MODIS derived crop types : Rostov region, 2003 Winter crop Sunflower Clean fallow Clean fallow Winter crops Sunflower Water bodies Deciduous forest Grassland Built-up area

19 Validation test sites in Rostov region 3000 Arable lands area comparison 2500 y = 1,04x - 46,9 R 2 = 0,93 MODIS derived data (sq.км) official satatistics (sq.км) Clean fallow Winter crops Sunflower

20 Rice acreage assessment NDVI (rel.value) Rice in Kalmykia, Russia Ccorr id dekad dekad rice acreage (1000 ha) according: Calculated based on vector masks Predicted at the beginning of the season based on MODIS vector masks official statistics

21 Crops affected by spring frost April 2009

22 Crops affected by drought 2009 Mostly affected regions Maximal impact on early grain crops Maximal impact on later grain and technical crops

23 July 2009 Crops affected by drought

24 Yield assessment with NDVI time-series by administrative regions (year-analogue method)

25 ww_y Crops yield prediction based on regression analysis Adygea (winter wheat) w w_y = -80, ,69 * ndv i _ar R-Square = 0,93 A 40,0 A A A A A A 30,0 A 20,0 A Li near Regressi on 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 Karacheavo- Cherkessia Stavropol Krasnodar Adygea Rostov Volgograd Voronezh Belgorod Orenburg Kursk Samara Tambov

26 User access to agricultural monitoring results

27 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