Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients
Abstract
1. Introduction
2. Data and Methods
2.1. CYGNSS Data
2.2. ICESAT and SMAP Data
2.3. Study Area and Validation Scheme
3. Methodology
- Values for Equation (3) can be obtained from GYGNSS products.
- BSR values are obtained from ICESTA2 or SMAP products.
- VWC or VOD are obtained from the SMAP products.
- Surface reflectivity Rlr(θ) is corrected by means of the previous values using Equation (5).
- Rvv and Rhh Fresnel coefficients (Equation (6)) are solved for low incidence angles (θi < 35°) where |Rvv| = |Rhh|.
- SMC is finally derived applying the Toppmodel [68].
4. Results
4.1. SMC Sensitivity Analysis to GNSS-R Reflectivity Γlr, Incidence Angle θ, BSR, and VOD Input Parameters
4.2. CYGNSS Derived SMC from ICESAT2 and SMAP/Sentinel1
4.3. CYGNSS SMC Validation Using SMAP Data
5. Discussion
6. Conclusions
- A new method to retrieve SMC from GNSS-R is presented and validated, purely based on a bistatic radar physical modeling of the dielectric permittivity using Fresnel reflection coefficients and accounting the effects of BSR and VOD.
- This new approach is applied and tested with one month of CYGNSS GNSS-R data (April 2019 at the eastern region of China), in combination withICESat-2 and/or SMAP BSR and VOD products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief.
- This CYGNSS SMC approach is validated with SMAP SMC products, and the statistical assessment provides an R-square of 0.6 (RMSE of 0.05 and zero p-value) for 4568 test points evaluated during April 2019 at the eastern region of China.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameter | Value |
---|---|
Number of observations | 4568 |
RMSE | 0.05 |
R-squared | 0.6 |
p-value | 0 |
β0 | 0.0669 ± 0.0290 |
β1 | 0.4916 ± 0.0135 |
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Calabia, A.; Molina, I.; Jin, S. Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients. Remote Sens. 2020, 12, 122. https://doi.org/10.3390/rs12010122
Calabia A, Molina I, Jin S. Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients. Remote Sensing. 2020; 12(1):122. https://doi.org/10.3390/rs12010122
Chicago/Turabian StyleCalabia, Andres, Iñigo Molina, and Shuanggen Jin. 2020. "Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients" Remote Sensing 12, no. 1: 122. https://doi.org/10.3390/rs12010122
APA StyleCalabia, A., Molina, I., & Jin, S. (2020). Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients. Remote Sensing, 12(1), 122. https://doi.org/10.3390/rs12010122