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 in Global Monitoring of Agriculture: from research to operations

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
Satellite Date Archives
Data Analysis Methods
HPC Technologies

5. Main technological component
Multi-annual of automatic near-real-time update EO data archive, including:
MODIS Surface Reflectance MOD09 from NASA ( 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 Land cover & crop land assessment
Agricultural Productivity
Env. Impact Assessment of Land use change
Sites: IKI RAN, SRI, RADI, CIRAD, INTA, VITO, UCL, GEOSAS, AGHRYMET
Data Management
Capacity Building 7

8. VEGA-GEOGLAM Service vega.geoglam.ru
VEGA-GEOGLAM is 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 “A Unified Cropland Layer at 250 m for Global Agriculture Monitoring.” Data 1 (1):3. doi: /data

11. SIGMA-JECAM sides 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
China
Russia

14. Russian Ministry of Sciences supported the SIGMA project cooperation
The project duration
The regional focus on the Eurasian Economic 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 (
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. Development of MODIS LAI @ 250m product
NDVI
LAI
LAI QC 18

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). 19

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
Unique project ID - RFMEFI61615X0063