Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

A novel technique for mapping soil moisture and irrigation at the scale of agricultural fields has been developed, achieving high accuracy with a Root Mean Square Error (RMSE) of less than 5%. This approach combines multi-temporal optical and Synthetic Aperture Radar (SAR) data from Sentinel-2 and Sentinel-1 satellites. The method was tested in the Kairouan plain, a semi-arid region in Tunisia, using an algorithm based on the Water Cloud Model (WCM) for soil water content spatialization between 2015 and 2017. Validation through ground measurements and neural networks confirmed the accuracy of the retrieved soil moisture products. The study further classified irrigated and rainfed areas by analyzing soil moisture and Normalized Difference Vegetation Index (NDVI) time series, producing irrigation maps at the field scale with an accuracy of 77% when using soil moisture indices alone.

Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., & Fanise, P. (2018). Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sensing, 10(12), 1953. https://doi.org/10.3390/rs10121953