Evaluation of Radar Backscattering Models IEM, OH, and Dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths

10 citations as recorded by crossref.

1. Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization Q. Liu et al. 10.3390/s22041611
2. Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania F. Kirimi et al. 10.4236/ars.2016.51003
3. Evaluation of Radar/Optical Based Vegetation Descriptors in Water Cloud Model for Soil Moisture Retrieval S. Chaudhary et al. 10.1109/JSEN.2021.3099937
4. Soil Moisture Monitoring with Dual-Incidence-Angle RISAT-1 Data: A Pilot Study from Vidarbha Region D. Sahadevan et al. 10.1007/s12524-019-00998-4
5. Uncertainty Quantification of Soil Moisture Estimations Based on a Bayesian Probabilistic Inversion C. Ma et al. 10.1109/TGRS.2017.2664078
6. Soil Moisture Mapping Along Climatic Gradient by Dual-Polarization Sentinel-1 C-Band Data J. Chang et al. 10.1109/LGRS.2023.3236036
7. Estimation of Peatland Distribution Using Ratio Dual-pol from Sentinel-1A S. Putiamini et al. 10.1088/1755-1315/280/1/012012
8. Derivation of Soil Moisture using Modified Dubois Model with field assisted surface roughness on RISAT-1 data T. Palanisamy & V. R 10.15446/esrj.v22n1.59972
9. The Discrepancy Between Backscattering Model Simulations and Radar Observations Caused by Scaling Issues: An Uncertainty Analysis C. Ma et al. 10.1109/TGRS.2019.2899120
10. Application of sentinel-1 SAR-derived vegetation descriptors for soil moisture retrieval and plant height prediction during the wheat growth cycle R. Dave et al. 10.1080/01431161.2023.2170193