Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned
Abstract
:1. Introduction
- From the peak of the DDM (direct and reflected signals), and assuming that the scattered signal has a dominant coherent component, the calibrated reflection coefficient is computed taking into account the antenna pattern gains in the directions where the signals are collected, and the distances from the transmitter to the receiver, and from the transmitter to the specular reflection point plus from the specular reflection point to the receiver.
- Then, following an approach similar to the Single Channel Algorithm in SMAP [17], or in [10,12], but neglecting the single scattering albedo (ω = 0), the computed reflectivity is compensated for surface roughness (h) and vegetation effects (τ: vegetation optical depth, or VOD), in order to derive the flat surface reflectivity:
- 3.
- Then, considering the variation of the SM in the dielectric constant model for soil (among other variables, such as the clay fraction and physical temperature…), the soil moisture value can be in principle estimated through a minimization process.
2. Methodology: Field Experiment Description
2.1. Airborne Instrumentation and Configuration
- CORTO (COmpact Reflectometer for Terrain Observations) is a miniature version of the LARGO [25] reflectometer at L1 GPS bands developed by the Universitat Politècnica de Catalunya. It performs the correlations between the direct and the reflected GPS signals and outputs reflectivity.
2.2. Vegetation Optical Depth (VOD) Estimation from Normalized Differential Vegetation Index (NDVI)
- that the value of the b parameter directly determined at the satellite scale “is nearly identical to what was proposed for crops in the ESA SMOS (Soil Moisture and Ocean Salinity mission) algorithm, but half as large as what is currently used by SMAP” [29], and
- the current values of the h parameter may be too smooth (low) for crop regions, as reported in [20] for the South Fork SMAP Core Validation Site in the Corn Belt state of Iowa.
2.3. Soil Surface Roughness and Soil Moisture
3. GNSS-R Soil Moisture Retrieval: Results
3.1. In Situ Surface Roughness Correction
3.2. Ad Hoc Soil Surface Roughness Correction
- Compensation of vegetation effects (Equations (1)–(4)).
- Compensation of surface roughness effects (as in Equation (1) and (2): , for n = 0, 1, and 2.
- Apply SM retrieval algorithm by minimizing the difference between the computed flat bare soil reflectivity using ARIEL-derived SM, and GNSS-R calibrated reflectivity () plus the ad hoc surface roughness compensation term (in [dB]).
3.3. Vegetation and Roughness Compensation for Different Incidence Angles
- high reflectivity values encountered in vegetated areas (trees, higher soil moisture), but from the processing point of view, require a too high attenuation compensation, leading to “corrected” flat bare reflectivity values larger than one (0 dB) in Figure 14. This is illustrated in Figure 16 by the dark blue dot over a forested area in the middle of the image, pointed by the triangle, and legend with the parameters.
- there is likely a subsurface variability of the soil moisture that is detected by GNSS-R, as there are regions in the fields where the reflectivity suddenly increases (see lines of light blue or green values in Figure 10, with some dark blue—high reflectivity—observations in the middle), while all other parameters are nearly constant.
3.4. Empirical Roughness Compensation
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Camps, A.; Park, H.; Castellví, J.; Corbera, J.; Ascaso, E. Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned. Remote Sens. 2020, 12, 2064. https://doi.org/10.3390/rs12122064
Camps A, Park H, Castellví J, Corbera J, Ascaso E. Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned. Remote Sensing. 2020; 12(12):2064. https://doi.org/10.3390/rs12122064
Chicago/Turabian StyleCamps, Adriano, Hyuk Park, Jordi Castellví, Jordi Corbera, and Emili Ascaso. 2020. "Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned" Remote Sensing 12, no. 12: 2064. https://doi.org/10.3390/rs12122064