Crop type mapping in Central and South Asia using Sentinel-1 and Sentinel-2 remote sensing data

Abstract

Crop type information derived from satellite remote sensing are of pivotal importance for quantifying crop growth and health status. However, such spatial information are not readily available for countries in Central and South Asia, where smallholder farmers play a dominant role in agricultural practice, and food security. In this study, we provide insights into crop type mapping for three study sites in the region: 1) Panfilov District in Kazakhstan, 2) Jaloliddin Balkhi District in Tajikistan, and 3) Multan District in Pakistan. A collection of Sentinel-2 and Sentinel-1 satellite data was used along with the random forest classification algorithm. To train and validate the classification model, field data were collected between May and October 2022 in each of the study areas. Our main objective was to evaluate the performance of a combined Sentinel-2 and Sentinel-1 mapping approach in comparison to a single source result. In addition, this contribution will provide insights into the performance with regard to crop type mapping accuracy of different temporal data aggregation intervals. Preliminary results indicate a small increase in overall accuracy for a combined Sentinel-2 and Sentinel-1 mapping approach. However, Sentinel-2 data might be sufficient for reliable crop type mapping, in case cloud coverage is not a constraint. Future studies might consider evaluating the potential benefit of using a full Sentinel-1 data set without temporal aggregation for mapping crop types.