Welcome! I’m Dr. Sathyanarayan Rao, currently a Research Associate at the Indian Institute of Science, sharing results from our recent study exploring the benefits of soil-specific calibration in satellite-based (SAR) soil moisture estimation. What’s this video about? • We collected over 650 field soil moisture measurements in the Berambadi watershed, Karnataka, India. • Combined these with SAR backscatter data from both ALOS-2 (L-band) and Sentinel-1 (C-band) satellites. • Tested whether building separate machine learning models (Random Forest) for different soil textures improves soil moisture predictions compared to a generic “one-size-fits-all” model. • Found that soil-specific models especially help in sandy loam and loamy sand soils—but not so much for clay-rich soils. • Discussed which satellite features (like HH, HV, and polarization ratio) are most useful, and how these findings could help operational soil moisture monitoring in India and similar regions. Acknowledgments Special thanks to Prof. Sekhar Muddu for his support, the REWARD project (World Bank & Govt. of India) for funding, and all field collaborators. Preprint & Code/Data Repository [Link to preprint will be added here once available] [Link to GitHub/data catalog will be added here after publication] Questions or feedback? Feel free to comment below or reach out to me. If you find this helpful, please like and subscribe for more science content!