The use of Sentinel-1 and -2 data for monitoring maize production in Rwanda

Jason Polly

Major challenges for satellite image analysis in the context of Rwanda include heavily clouded scenes and small plot sizes that are often intercropped. Sentinel-2 scenes corresponding to mid-season were analyzed, and spectral signatures of maize could be distinguished from those of other crops. Seasonal mean filtering was applied to Sentinel-1 scenes, and there was significant overlap in the spectral signatures across different types of vegetation. Random Forest models for classification of Sentinel scenes were developed using a training dataset that was constructed from high-resolution multispectral images acquired by unmanned aerial vehicles (UAVs) in several different locations in Rwanda and labeled as to the crop type by trained observers.



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