Machine Learning Based Soil Moisture Retrieval Algorithm and Validation at Selected Agricultural Sites Over India Using Cygnss Data

Shivani Tyagi,Prashant K Srivastava,Arundhati Misra,Dharmendra Kumar Pandey,Deepak Putrevu

doi:10.1109/igarss47720.2021.9555095

Abstract

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m 3 /m 3 during pre-monsoon and 0.65 and 0.053 m 3 /m 3 in post monsoon periods, respectively.

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