Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

Abstract

Abstract-Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant. Index Terms-Global

Potential Applications of GNSS-R Observations over Agricultural Areas: Results from the GLORI Airborne Campaign

“…For the case of Γ RL and reflected signal SNR, the retrieved sensitivity was greater than that in the case of the Leimon and GRASS campaigns, which reported values of~20 dB/(m 3 /m 3 ) [25,26]. They were in agreement with values retrieved in [19] using TechDemosat data (38 dB/(m 3 /m 3 )). With the decrease of S with increasing LAI (i.e., vegetation cover development), we also observed a decrease in the correlation coefficient between GNSS-R observables (Γ RL or reflected SNR) and soil moisture.…”

“…Although this technique was initially used for oceanographic applications, various experimental in situ and airborne campaigns have led to a considerably improved understanding of the interaction of these signals with the Earth's surface, and their potential for monitoring land surface conditions, including that of the cryosphere [14]. In recent years, the GNSS-R technique has been increasingly tested and validated over land surfaces [15][16][17][18][19][20][21], using either single GNSS-R physical signal, an essential step to propose operational satellite algorithms for land surface properties mapping.…”

Wetland monitoring with Global Navigation Satellite System reflectometry

“…The GNSS‐R was first proposed by Hall and Cordey []. GNSS signals reflected from the Earth's surface are used to measure surface properties such as ocean winds [ Ruf et al ., ; Zavorotny and Voronovich , ; Garrison et al ., ], sea ice coverage [ Fabra et al ., ], soil moisture [ Chew et al ., , , ; Camps et al ., , ; Egido et al ., ; Katzberg et al ., ; Rodriguez‐Alvarez et al ., ; Small et al ., ], and potentially mean sea slope and topography [ Clarizia et al ., ; Cardellach et al ., ]. Soil moisture can impact the Bowen ratio and thereby affects tropospheric water vapor profile (measurable with GNSS Radio Occultation or RO) and changes atmospheric dynamic patterns.…”

Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture

“…In addition to satellite‐based microwave SM estimation missions, researchers have also tested the viability of surface‐reflected Global Navigation Satellite System (GNSS) signals in estimating surface SM (Camps et al, ; Chew & Small, ; Masters, ). The first dedicated spaceborne Global Positioning System (GPS) reflectometry (GPS‐R) receiver on board the UK‐Disaster Monitoring Constellation satellite (also known as BNSCSAT‐1, launched in September 2003) proved that GNSS signals can reliably describe surface conditions including ocean, snow, and land surface properties (Gleason et al, ).…”

“…Chew et al () and Camps et al () demonstrated the sensitivity of the GPS‐R data set on SM and vegetation cover using data from the UK TechDemoSat‐1 (TDS‐1), which was launched in July 2014. However, TDS‐1‐derived data have important limitations in data acquisition, both spatially and temporally, because the TDS‐1 GPS‐R payload is only active for 2 out of every 8 days (Clarizia et al, ).…”