Single-Pass Soil Moisture Retrievals Using GNSS-R: Lessons Learned

Camps, Adriano; Park, Hyuk; Castellví, Jordi; Corbera, Jordi; Ascaso, Emili

doi:10.3390/rs12122064

Cited by 32 publications

(30 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…by [23] and [33] neither. Besides, the challenges on modeling and correcting the roughness effects to retrieve soil moisture is discussed by [36]. There is still need and room for investigations on this topic.…”

Section: Experimental Datamentioning

confidence: 99%

Remote Sensing of Precipitation Using Reflected GNSS Signals: Response Analysis of Polarimetric Observations

Asgarimehr

Hoseini

Semmling

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

For the first time, rain effects on the polarimetric observations of the Global Navigation Satellite System Reflectometry (GNSS-R) are investigated. The physical feasibility of tracking the modifications in the surface roughness by rain splash and the surface salinity by the accumulation of freshwater is theoretically discussed. An empirical analysis is carried out using measurements of a coastal GNSS-R station with two side-looking antennas in right and left-handed circular polarization (RHCP and LHCP). Discernible drops in RHCP and LHCP powers are observed during rain over a calm sea. The power drop becomes larger in size at higher elevation angles. The average LHCP power drops by ≈ 5 dB at an elevation angle of 45 • . The amplitude of the correlation sum shows a dampening, responding to rain rate systematically. The LHCP observations show higher sensitivity to rainfall compared to RHCP observations. The retrieved standard deviation of surface heights shows a steady increase over the rain rate. The derived surface salinity shows a decrease at rains higher than 10 mm/h. This study confirms the potential under environmental conditions of the GNSS-R groundbased station, e.g. with salinity mostly lower than 30 psu, over a calm sea, being a starting point for future investigations.

show abstract

Section: Experimental Datamentioning

confidence: 99%

Remote Sensing of Precipitation Using Reflected GNSS Signals: Response Analysis of Polarimetric Observations

Asgarimehr

Hoseini

Semmling

et al. 2022

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…At lower elevation angles, signal depolarization and multiple scattering effects must be taken into account to properly model vegetation effects in GNSS-Reflectometry, for this type of forest, and probably for other types of dense vegetation as well. This limitation of the model is what nowadays limits the range of elevation angles that can be used for soil moisture retrievals using GNSS-R, as shown in [13].…”

Section: Discussionmentioning

confidence: 99%

L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning

et al. 2020

Self Cite

View full text Add to dashboard Cite

At L-band (1–2 GHz), and particularly in microwave radiometry (1.413 GHz), vegetation has been traditionally modeled with the τ-ω model. This model has also been used to compensate for vegetation effects in Global Navigation Satellite Systems-Reflectometry (GNSS-R) with modest success. This manuscript presents an analysis of the vegetation impact on GPS L1 C/A (coarse acquisition code) signals in terms of attenuation and depolarization. A dual polarized instrument with commercial off-the-shelf (COTS) GPS receivers as back-ends was installed for more than a year under a beech forest collecting carrier-to-noise (C/N0) data. These data were compared to different ground-truth datasets (greenness, blueness, and redness indices, sky cover index, rain data, leaf area index or LAI, and normalized difference vegetation index (NDVI)). The highest correlation observed is between C/N0 and NDVI data, obtaining R2 coefficients larger than 0.85 independently from the elevation angle, suggesting that for beech forest, NDVI is a good descriptor of signal attenuation at L-band, which is known to be related to the vegetation optical depth (VOD). Depolarization effects were also studied, and were found to be significant at elevation angles as large as ~50°. Data were also fit to a simple τ-ω model to estimate a single scattering albedo parameter (ω) to try to compensate for vegetation scattering effects in soil moisture retrieval algorithms using GNSS-R. It is found that, even including dependence on the elevation angle (ω(θe)), at elevation angles smaller than ~67°, the ω(θe) model is not related to the NDVI. This limits the range of elevation angles that can be used for soil moisture retrievals using GNSS-R. Finally, errors of the GPS-derived position were computed over time to assess vegetation impact on the accuracy of the positioning.

show abstract

“…It was validated at 171 in-situ soil moisture stations, which resulted in a median unbiased RMSE of ~ 0.049 cm 3 /cm 3 . At present, the main remaining challenges are to properly correct for the impact of the upwelling vegetation cover and the small-scale surface roughness in the reflectivity, so as to improve the accuracy in SMC retrievals [132]. To do so, multi-pass and single-pass techniques can be applied, each one with pros and cons.…”

Section: ) Soil Moisture Contentmentioning

confidence: 99%