Large-scale soil moisture mapping using Earth observation data and its validation at selected agricultural sites over Indian region

This study brings out work carried out for large-scale soil moisture mapping based on a time-series-based methodology over India using SMAP L-band radiometer data and its validation at selected agriculture sites over India. A methodology has been developed to derive soil wetness index from the time-series of SMAP L-band brightness temperatures over India. Maximum and minimum brightness temperatures for each pixel are assigned to the driest and wettest periods, respectively. A daily soil wetness index over each pixel is computed by normalizing brightness temperature observations from these extreme values. This study also focuses on analysis of in-situ soil moisture data from different agricultural fields as validation sites to utilise for the validation of Soil Moisture (SAC-ISRO) data products, developed and operationalized by SAC (ISRO) since 2015. The in-situ data over a two-year period was analysed and the validation of the soil moisture (SAC-ISRO) with this in-situ data was conducted using statistical performance metrics, i.e., unbiased root mean square error (ubRMSE), Bias, root mean square error (RMSE) and coefficient of determination (R2). Overall, the results presented in this work demonstrate that very significant improvement was observed in soil moisture (SAC-ISRO) data products over different validation sites by applying scaling approach, which finally meets SMAP mission requirement and the performance of soil moisture (SAC-ISRO) data products derived from SMAP L-band radiometer data are closely matching with in-situ observations and have a very good agreement. This validation exercise and results are very useful to support the continuous development and improvement of the soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially over Indian regions.