Earth Observations based agricultural monitoring over EEU countries: concept and first results of the SIGMA RBK project

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1 Earth Observations based agricultural monitoring over EEU countries: concept and first results of the SIGMA RBK project Bartalev Sergey, Loupian Evgeny, Plotnikov Dmitry and Tolpin Vladimir Space Research Institute Russian Academy of Sciences IAAE Inter Conference Symposium Agricultural Transitions along the Silk Road Restructuring, Resources and Trade in the Central Asia Region, 4 6 April 2016, Almaty, Kazakhstan

2 Recent volatility of Agricultural Prices Monthly Wheat Prices ($/Metric Ton) Source: World Bank Wheat 2008 Price hikes Droughts: Australia & Ukraine 2010/11 Price hikes Drought: Russia 1971/2 s price hike Drought: Russia Landsat 1 Launched (1972) Nominal wheat price in US $/metric Ton Becker-Reshef

3 GEOGLAM Objectives To strengthen the international community s capacity to produce & disseminate relevant, timely and accurate information and forecasts on agricultural production at national, regional and global scales, through reinforced use of Earth Observations GEOGLAM is a «coordination programme», aiming at - supporting, strengthening and articulating existing efforts - developing capacities and awareness at national and global level - disseminating information

4 SIGMA Activities Sites: IKI RAN, SRI, RADI, CIRAD, INTA, VITO, UCL, GEOSAS, AGHRYMET Data Management Capacity Building 4

5 SIGMA Facts Funded By The European Commission Start 1 November March 2017 Agriculture AND Environment 22 partners, 17 countries VITO, CIRAD, JRC, IIASA, Alterra, RADI, NMSC, DEIMOS, GeoSAS, RCMRD, Aghrymet, RCMRD, Sarvision, Sarmap, INTA, Geoville, UCL, EFTAS, FAO, ITC, GISAT, IKI, SRI Argentina, Ukraine, China, Russia, Africa, USA, Brazil, Vietnam, Belgium, A Major European contribution to GEOGLAM Coordinated by VITO

6 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

7 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

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 Main features of the VEGA GEOGLAM (I) Multi annual EO data archive (daily update): MODIS Surface Reflectance product (2000 ongoing) PROBA V (2014 ongoing) Landsat TM/ETM+/OLI (2001 ongoing) Sentinel 2 (2016 ongoing) (II) EO data pre processing: cloud screening, cloud free image compositing, VI time series reconstruction (III) Web based User data analysis tools Multi spectral and multi temporal colour composition NDVI and other VI temporal profiles extraction Supervised and unsupervised image classification

10 In situ and auxiliary data integrated in the VEGA GEOGLAM (I) In situ data for users defined polygons (fields): Land use types Crop types Biophysical characteristics (LAI, FCover, biomass and etc) Yield Phenological stages (crops calendar) (II) Meteorological data: NCEPdata (2000 ongoing, 4 times per day update) (III) Thematic maps Land cover Soil types etc

11 The MODIS coverage available in the VEGA GEOGLAM

12 The VEGA GEOGLAM: Proba V daily data. An example for September 15, 2015

13 The VEGA GEOGLAM: Landsat coverage The Landsat data coverage for the period Jan 1- March 27, 2016, The Landsat data archive contains data for the period since year 2000 with daily update.

14 The VEGA GEOGLAM: Sentinel 2 coverage The Sentinel-2 data coverage for the period Jan 1 March 27, 2016, The Sentinel-2 data archive is of daily update.

15 The VEGA GEOGLAM: KMSS Meteor M coverage The Meteor M data (for both, 1 and 2) coverage for the period June 1, 2015 September 1, The Meteor M data archive contains data for the period since year 2011 with daily update. Also it is available for visualization, classification and downloading.

16 The VEGA GEOGLAM: Canopus V coverage The Canopus V data (MSS and PSS) coverage from 23 January, 2013 up to now. It is available for visualization only.

17 The VEGA GEOGLAM: Canopus V This scene was acquired on 14, June, 2014, sensor MSS. It covers GEOGLAM test site in Argentina.

18 The VEGA GEOGLAM: Canopus V This scene was acquired on 14, June, 2014, sensor PSS. It covers GEOGLAM test site in Argentina.

19 The VEGA GEOGLAM: MODIS detected active fires on croplands

20 The VEGA GEOGLAM: meteorological products example The max temperature map generated by the VEGA-GEOGLAM tools for October 21, 2014

21 The VEGA GEOGLAM: meteorological products example The accumulated precipitation map generated by the VEGA-GEOGLAM tools for October 19, 2014

22 The VEGA GEOGLAM field passport A field passport contains summary on available EO, in-situ and meteorological data. The system provides for corresponding user in-situ data editing tools.

23 The VEGA GEOGLAM: MODIS data access and analysis The VEGA-GEOGLAM provide access to NDVI multi-annual time-series data aggregated at users defined polygons (fields limits).

24 The VEGA GEOGLAM: NDVI profiles

25 The VEGA GEOGLAM: EO and in situ data integration and analysis NDVI dynamic for hayfield Using the VEGA-GEOGLAM tools users can define fields borders along with land use and crop types, as well as integrate into the system other ground collected data. Both EO and in-situ data can be jointly analysed using available tolls. Ground collected LAI data NDVI dynamic for arable land

26 The VEGA GEOGLAM EO data analysis tools: image classification Images and spectral bands selection A classification method and parameters set Map interface contains the supervised and unsupervised image classification tools. Training sites creation Classification results

27 The VEGA GEOGLAM EO data analysis tools: Supervised image classification Landsat derived winter crops map using VEGA- GEOGLAM classification tools MODIS derived winter crops map and training sites

28 Arable lands mapping using multi annual time series of MODIS data

29 Arable lands mapping in Kazakhstan using MODIS data time series

30 Fallow fields mapping in Kazakhstan using MODIS data for years 2014 and 2015

31 Potential for collaboration within the SIGMA RBK project Providing access to the VEGA GEOGLAM data and analysis tools ( Joint research and developments focused on land cover and land use mapping using remote sensing data Joint validation/calibration of remote sensing data derived products

32 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