Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation

• We developed a soil moisture algorithm for GMES Sentinel-1 characteristics.
• An approach based on an Artificial Neural Network (ANN) has been proposed.
• The algorithm has been validated in several test areas in Italy, Australia, and Spain.
• The validation results were very much in line with GMES requirements.

The main objective of this research is to develop, test and validate a soil moisture content (SMC) algorithm for GMES Sentinel-1 characteristics. The SMC product, which is to be generated from Sentinel-1 data, requires an algorithm capable of processing operationally in near-real-time and delivering the product to the GMES services within 3 h from observation. An approach based on an Artificial Neural Network (ANN) has been proposed that represents a good compromise between retrieval accuracy and processing time, thus enabling compliance with the timeliness requirements. The algorithm has been tested and subsequently validated in several test areas in Italy, Australia, and Spain.In all cases the validation results were very much in line with GMES requirements (with RMSE generally < 4%SMC – between 1.67%SMC and 6.68%SMC – and very low bias), except for the case of the test area in Spain, where the validation results were penalized by the availability of only VV polarized SAR images and MODIS low-resolution NDVI. Nonetheless, the obtained RMSE was slightly higher than 4%SMC.