SENTINEL-2 MULTI-TEMPORAL DATA FOR RICE CROP CLASSIFICATION IN NEPAL

The global coverage of Sentinel-2 provides widespread opportunities for accurately mapping and monitoring key crops in emerging countries, like in the case of Nepal's rice production. While previous studies based on other satellites show some important spatial and temporal limitations, the use of operational Sentinel-2 data still remains unexplored in this regard. As a result, this work investigates the viability of using the Sentinel-2 instrument for a precise rice crop classification in Nepal. Initially, we define a dataset made of multi-temporal Sentinel-2 data from the Terai region of Nepal. Then, we conduct several classification experiments to provide empirical evidences about the suitability of different classification models when identifying rice crops in developing countries, where only limited ground-truth data could be available. The experiments reveal the suitability of using Sentinel-2 for accurately mapping rice crops in Nepal with a CNN-based classification model.