Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Satellite crop detection technologies are focused on the of different types crops in fields. The information crop-type area is more useful for food security than earlier phenology stage is. Currently, data obtained from remote sensing (RS) used to solve tasks related identification type agricultural crops; additionally, modern using AI methods desired postprocessing stage. In this paper, we develop a methodology supervised classification time series Sentinel-2 and Sentinel-1 data, compare accuracies based input datasets find how accuracy develops during season. EU, unified Land Parcel Identification System (LPIS) available provide essential field borders. To increase usability, also entire field. This improves overall accuracy.