SenSAgri: Unprecedented capacity of Sentinel-1 & Sentinel-2 (S2) data for crop mapping

Theresa Taona Mazarire

This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified.




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