• Crop area estimation using satellite data. To utilize a regression estimator to combine in-situ area frame sampling (AFS) surveys and crop maps derived from Earth remote sensing data.
• Crop classification using multi-polarization SAR data. To use satellite SAR data to better discriminate between crops with similar spectral-temporal behaviour but different physical structure. To perform an analysis and find optimal combinations of polarizations and SAR modes to increase classification accuracy of agriculture crops.
• Adaptation of crop growth models. To use a time-series of in-situ and satellite measurements of crop and soil parameters to adapt crop growth models for a given territory for yield forecasting.
Ukrainian specialists are participating in the next international projects and initiatives related to JECAM.
• SOAR-JECAM Project “SAR parameters optimization for crop classification”. The SOAR project cover a wide range of applications and exploit the new capabilities of RADARSAT-2. Often it has selected quad-polarized data to test what new information can be garnered from this dataset and used for agricultural needs. Our activities are based on Radarsat-2 quad-polarized images consists in optimization of SAR parameters (polarization, incidence angle, beam mode etc) to provide timely and economically efficient crop maps/area estimates for Ukrainian landscape. Quad-pol SAR data will be used along with optical images to classify cropland. Special attention will be devoted to winter and summer crops that are difficult to classify using optical data only.
• ESA Sentinel2-Agriculture participation as a “Champion User”. SPOT-4 is observing JECAM Ukraine test sites every 5 days to simulate Sintinel-2.
The project is preparing for the launch of the Sentinel-2 mission, which will increase the Earth Observation capacity for agricultural monitoring, in terms of resolution, revisit frequency and coverage. The Sentinel-2 Agriculture project aims at providing to the international user community validated algorithms and best practices to produce EO products for agriculture monitoring preparing for the exploitation of Sentinel-2 observations. Sentinel-2 Agriculture main activities will focus on user driven development of agricultural EO products, benchmarking and validating required algorithms and demonstration of resulting Sentinel-2 EO products and services to users of the global agricultural community.
• Within ImagineS FP 7 Project we are providing the validation of global satellite products.
The main objectives of IMAGINES are to (i) improve the retrieval of basic biophysical variables, mainly LAI, FAPAR and the surface albedo, identified as Terrestrial Essential Climate Variables, by merging the information coming from different Sentinel sensors and other GMES contributing missions; (ii) develop qualified software able to process multi-sensor data at the global scale on a fully automatic basis; (iii) propose an original agriculture service relying upon a new method to assess the biomass, based on the assimilation of satellite products in a Land Data Assimilation System (LDAS) in order to monitor the crop/fodder biomass production together with the carbon and water fluxes; (iv) demonstrate the added value of this agriculture service for a community of users acting at global, European, national, and regional scales.