Improving EOS-04 (RISAT-1A) SAR based Operational 500m Soil Moisture Product Using Meteorological Observables and Machine Learning Model
This study presents a comprehensive validation of operational 500 m soil moisture product from ISRO’s Earth Observation Satellite-04 (EOS-04) (RISAT-1A) C-band SAR mission using in-situ measurements from Automated Weather Station (AWS) data from the National Centre for Geodesy (NCG) at Indian Institute of Technology, Kanpur. The analysis spans a period of seven months from Sep, 2023 to April, 2024. In addition to soil moisture, relative humidity and precipitation data were also integrated through machine learning model to improve the accuracy of EOS-04 operational 500 m Soil Moisture (SM) product. It was observed that the incorporation of meteorological variables resulted in a significant improvement in the accuracy of the EOS-04 soil moisture estimates. This study underscores the potential of meteorological observables and machine learning in refining SAR based Soil Moisture product for more precise environmental monitoring and resource management applications.
Authors
- Aviral Tripathi
- Onkar Dikshit (ORCID: https://orcid.org/0000-0003-3213-8218)
- Rıshı Prakash (ORCID: https://orcid.org/0000-0001-6471-5935)
- Anurag Vidyarthi (ORCID: https://orcid.org/0000-0002-9045-1782)
- Dharmendra Kumar Pandey (ORCID: https://orcid.org/0000-0002-4060-5368)
- Sushant Shekhar (ORCID: https://orcid.org/0000-0002-7817-6538)
Institutions
- Indian Space Research Organisation (IN)
- Jaypee Institute of Information Technology (IN)
- Graphic Era University (IN)
- Indian Institute of Technology Kanpur (IN)
Publication Details
- Published
- 2024-12-02
- DOI
- https://doi.org/10.1109/ingarss61818.2024.11105065
- Primary Topic
- Soil Moisture and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 0.00