ANALYSIS OF THE EFFECT OF INCIDENCE ANGLE AND MOISTURE CONTENT ON THE PENETRATION DEPTH OF L- AND S-BAND SAR SIGNALS INTO THE GROUND SURFACE

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1. PIML-SM: Physics-Informed Machine Learning to Estimate Surface Soil Moisture From Multisensor Satellite Images by Leveraging Swarm Intelligence A. Singh & K. Gaurav 10.1109/TGRS.2024.3502618
2. Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images A. Singh & K. Gaurav 10.1038/s41598-023-28939-9
3. Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data D. Dinesh et al. 10.3390/rs16183539
4. The SMOS-Derived Soil Water EXtent and equivalent layer thickness facilitate determination of soil water resources B. Usowicz et al. 10.1038/s41598-020-75475-x
5. Non-destructive detection of wheat moisture content with frequency modulated continuous wave system under L and S bands X. Kuang et al. 10.1016/j.compag.2024.109644
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7. Application potential and spatiotemporal uncertainty assessment of multi-layer soil moisture estimation in different climate zones using multi-source data J. Qian et al. 10.1016/j.jhydrol.2024.132229
8. Evaluation and improvement of spatiotemporal estimation and transferability of multi-layer and profile soil moisture in the Qinghai Lake and Heihe River basins using multi-strategy constraints J. Qian et al. 10.1016/j.geoderma.2025.117222
9. Estimation of Soil Moisture Applying Modified Dubois Model to Sentinel-1; A Regional Study from Central India A. Singh et al. 10.3390/rs12142266
10. Comparison of Different Dielectric Models to Estimate Penetration Depth of L- and S-Band SAR Signals into the Ground Surface A. Singh et al. 10.3390/geographies2040045
11. Drainage congestion due to road network on the Kosi alluvial Fan, Himalayan Foreland A. Singh et al. 10.1016/j.jag.2022.102892
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