Ukraine is one of the most developed agricultural countries in the world. For many applications, it is extremely important to provide reliable crop maps taking into account diversity of cropping systems used in Ukraine. The use of optical imagery only is limited due to cloud cover, and previous studies showed particular difficulties in discriminating summer crops in Ukraine such as maize, soybeans, sunflower, and sugar beet. This paper focuses on exploring feasibility and assessing efficiency of using multitemporal satellite synthetic-aperture radar (SAR) acquired in C-band and optical images for crop classification in Ukraine. Both optical (Landsat-8/OLI) and SAR (Radarsat-2) images are used to assess the impact of adding backscattering intensity from SAR images for classification purposes. SAR intensity information is very important due to availability of Sentinel-1 imagery over Ukraine starting March 2015. Different combinations of optical and SAR images, as well as SAR modes and polarizations, are assessed for better discrimination of crops. A committee of neural networks, in particular multilayer perceptrons (MLPs), is used to improve classification accuracy compared to several standard classifiers. It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images. Considering the summer crops, the major impact of adding backscatter intensity information from SAR images is in better separation of sunflower, soybeans, and maize.Index Terms-Crop classification, ensemble, joint experiment for crop assessment and monitoring (JECAM), Landsat-8, neural networks (NNs), Radarsat-2, Ukraine.

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ABSTRACT:Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.