A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics

ABSTRACTAccurate classification and mapping of crops is essential for supporting sustainable land management. Such maps can be created based on satellite remote sensing; however, the selection of input data and optimal classifier algorithm still needs to be addressed especially for areas where field data is scarce. We exploited the intra-annual variation of temporal signatures of remotely sensed observations and used prior knowledge of crop calendars for the development of a two-step processing chain for crop classification. First, Landsat-based time-series metrics capturing within-season phenological variation were preprocessed and analyzed using Google Earth Engine cloud computing platform. The developmental stage of each crop was modeled by fitting harmonic function. The model’s output was further used for the automatic generation of training samples. Second, several classification methods (support vector machines, random forest, decision fusion) were tested. As input data for crop classification, comp...