Russian Academy of Sciences R&D contribution to GEOGLAM

1. Russian Academy of Sciences R&D contribution to GEOGLAM Sergey Bartalev Space Research Institute Russian Academy of Sciences GEO-XIII Plenary, 7-10 November 2016, St Petersburg, Russian Federation Side Event - Capacity Development for Stimulating Innovation inGlobal Monitoring of Agriculture: from research tooperations

2. National context of R&D activity on agricultural monitoring • Russian Statistical Agency (Agricultural Census 2006 and preparation for the Agricultural Census 2016) • Hydro-meteorological Service (crop yield forecast) • Ago-insurance and food-producing companies support (VEGA-PRO) • Science and education support (VEGA-Science)

3. Main R&D thematic areas • Land cover and land use mapping • Crop types mapping, including • Winter crops mapping (operational, national level) • Crop types mapping (experimental, regional level) • Crop status monitoring as an input to yield assessment, including e.g. • Winter kill • Summer drought

4. Conjunction of main technological components Data Analysis Methods Satellite Date Archives HPC Technologies

5. Main technological component • Multi-annual of automatic near-real-time update EO data archive, including: • MODIS Surface Reflectance MOD09 from NASA (2000 - ongoing) • Landsat data download from USGS and ESA (1989-ongoing) • Sentinel 1&2 data download from ESA • Proba-V data download from VITO • etc • Automated EO data processing chains, including: • EO data pre-processing (cloud/shadow screening, image compositing, vegetation indexes generation, data time-series reconstruction and etc) • Thematic products generation (arable lands, crop masks and etc) • Web-based Users’ Interface with data analysis tools

6. Arable lands mapping using multi-annual time series of MODIS data

7. SIGMA Activities Sites: IKI RAN, SRI, RADI, CIRAD, INTA, VITO, UCL, GEOSAS, AGHRYMET Data Management Capacity Building 7

8. VEGA-GEOGLAM Service vega.geoglam.ru VEGA-GEOGLAMis developed by the Russian Academy of Sciences’ Space Research Institute in framework of the SIGMA project to facilitate combine EO and in-situ data analysis over the JECAM test-sites

9. The MODIS coverage available in the VEGA-GEOGLAM

10. The Unified Cropland Layer at 250 m F.Waldner, S.Fritz, A.D.Gregorio, D.Plotnikov, S.Bartalev, N.Kussul, P.Gong, et al. 2016. “A Unified Cropland Layer at 250 m for Global Agriculture Monitoring.” Data 1 (1):3. doi:10.3390/data1010003.

11. SIGMA-JECAMsides for cropland mapping methods evaluation

12. SIGMA-JECAM test-sites agricultural landscapes differences Argentina Brazil China Russia Ukraine

13. Classification accuracy assessment using Pareto boundaries Argentina Brazil Ukraine Russia China

14. Russian Ministry of Sciences supported the SIGMA project cooperation • The project duration 2015-2017 • The regional focus on the EurasianEconomic Union (EEU) territory with priority major agricultural producing countries, such as Russia, Belarus and Kazakhstan (SIGMA-RBK project) • The global focus on the JECAM test sites

15. The SIGMA-RBK project main focuses • Extension of the VEGA-GEOGLAM coverage to all JECAM sites • Facilitation of Russian EO satellite data applications for agriculture monitoring • Improvement of cropland mapping products and their geographical extension with main focus to the Eurasian Economical Union countries • Regional parameterization of biophysical characteristics and crop yield retrieval methods using EO data assimilation to the crop grow models • Promotion of the web-based VEGA EO data analysis and processing tools for use by regional and global users

16. Collaboration within the SIGMA-RBK project region • VEGA-GEOGLAM data and analysis tools (http://vega.geoglam.ru/) • R&D focused on land cover and land use mapping using remote sensing data • Validation/calibration of remote sensing data derived products

17. Arable lands mapping in Kazakhstan using MODIS data time-series

18. NDVI LAI LAI QC Development of MODIS LAI @ 250m product

19. Winter crop yield prediction with WOFOST model and RS data assimilation The MODIS derived multi-annual Fcover estimates of winter crops in Tula region have been assimilated into WOFOST model in comparison to original model and official statistics (the result of FP7 MOCCCASIN project).

20. MODIS data vs WOFOST model derived LAI seasonal dynamics for different crops Sunflower Maize MODIS WOFOST MODIS WOFOST Barley Winter wheat MODIS WOFOST MODIS WOFOST

21. R&D Priorities • Development of new thematic products using moderate (Proba-V, KMSS) and high (Sentinel-2, Landsat) resolution data, including: • Land-use • Crop types • Crop yield • Bio-physical characteristics (Fcover, LAI) • Crop growth modelling with assimilation RS data derived products

22. R&D Collaboration Framework: Addressing Community Capacity • VEGA-GEOGLAM as a common technical platform for R&D on agricultural monitoring in Northern Eurasia • Facilitated access to open EO long-term data archives • Thematic products cross-comparison and validation • Models benchmarking • JECAM test-sites network support and development in Northern Eurasia region, considering R&D coordination with: • Regional research institutions • Agro-meteorological networks • Food producing companies

23. Regional workshop "Satellite Monitoring of Agricultural Lands in Northern Eurasia" October 28-31, 2013, Moscow, Russia geoglam.smislab.ru

24. Next SIGMA training and dissemination workshop is foreseeing to be organized in Moscow with support of the SIGMA-RBK project, 3rd or 4th week of May 2017

25. Thank you for your attention ! The project «Development of automated methods and information technologies for global agricultural monitoring from satellites to support GEOGLAM initiative» supported by the Ministry of Education and Science of the Russian Federation under the Contract 14.616.21.00163 Unique project ID- RFMEFI61615X0063