Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Airborne SAR Data
2.3. Ground Measurements
2.4. Effective Roughness Modeling as a Tool for Soil Moisture Retrieval
2.5. Experimental Set-Up
3. Results and Discussion
3.1. Backscatter Simulations and Soil Moisture Retrieval Using In Situ Measured Surface Roughness Parameters
3.2. Modeling Effective Root-Mean-Square Height s from SAR Backscatter
3.3. Modeling Effective Correlation Length l from SAR Backscatter and Bistatic Scattering
3.4. Soil Moisture Retrieval Based on Effective Roughness Modeling
3.5. Evaluating Different Soil Moisture Retrieval Approaches
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Active-Passive SAR System | |
Central Frequency | 1.375 GHz |
Polarization | HH, VV, HV and VH |
Signal Bandwidth | 50 MHz |
Along-track baseline | ∼400 m |
Across-track baseline | ∼25 m |
Zenith incidence angle range | 20°–55° |
Zenith scattering angle range | 20°–55° |
Average azimuth scattering angle ATI | −171.6° |
Average azimuth scattering angle XTI | −179.2° |
L-Band Backscatter | KGEmax | ||
---|---|---|---|
HH | 0.083 | 2.88 | 0.723 |
VV | 0.056 | 2.16 | 0.768 |
HV | 0.025 | 1.87 | 0.791 |
L-Band | KGEmax | ||
---|---|---|---|
HH | −1.7 | −5.7 | 0.566 |
VV | −7 | −77.1 | 0.720 |
HV | −8.5 | −203.3 | 0.682 |
L-Band | KGEmax | |||
---|---|---|---|---|
XTI | HH | −1.3 | 0.9 | 0.667 |
VV | −7.9 | −91 | 0.617 | |
ATI | HH | −1.2 | 2.4 | 0.664 |
VV | −8 | −90.9 | 0.575 |
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Tronquo, E.; Lievens, H.; Bouchat, J.; Defourny, P.; Baghdadi, N.; Verhoest, N.E.C. Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling. Remote Sens. 2022, 14, 1650. https://doi.org/10.3390/rs14071650
Tronquo E, Lievens H, Bouchat J, Defourny P, Baghdadi N, Verhoest NEC. Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling. Remote Sensing. 2022; 14(7):1650. https://doi.org/10.3390/rs14071650
Chicago/Turabian StyleTronquo, Emma, Hans Lievens, Jean Bouchat, Pierre Defourny, Nicolas Baghdadi, and Niko E. C. Verhoest. 2022. "Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling" Remote Sensing 14, no. 7: 1650. https://doi.org/10.3390/rs14071650