Nataliia Kussul

TL;DR: A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.

TL;DR: Efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale and in terms of classification accuracy, the neural network based approach outperformed support vector machine, decision tree and random forest classifiers available in GEE.

TL;DR: These full-scale demonstration results clearly highlight the operational agriculture monitoring capacity of the Sen2-Agri system to exploit in near real-time the observation acquired by the Sentinel-2 mission over very large areas.

TL;DR: It is concluded that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.

TL;DR: Comparing pixel-based and parcel-based approaches to crop classification from multitemporal optical (Landsat-8) and synthetic-aperture radar (SAR) Sentinel-1 imagery finds that pixel- based overall classification accuracy can be increased from 85.32% to 89.40% when using parcel boundaries.