Abstract
While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS) remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain), an innovative sensor developed by the Universitat Politècnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R) was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO) observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data).
Highlights
Recent studies have shown that the Global Navigation Satellite Systems Reflectometry (GNSS-R)signal has high sensitivity to the terrain dielectric constant, both from ground-based and airborne platforms [1,2,3,4]
The objective of this work was to test the capability of an airborne GNSS-R sensor (LARGO) to estimate and characterize both vegetation cover and soil moisture status
In view of the expected development of new GNSS-R-based missions, the potential interactions between soil moisture estimates and observations at high spatial resolution from airborne or satellite sensors allow for the development of synergistic approaches that can be later transferred to sensors on-board satellite platforms
Summary
Signal has high sensitivity to the terrain dielectric constant, both from ground-based and airborne platforms [1,2,3,4] This suggests that GNSS-R techniques could be potentially used to monitor vegetation and soil variables related to the water content. The synergy between low-resolution passive microwave observations and optical data at higher spatial resolution is the basis of several proposed multi-resolution soil moisture retrieval approaches for the ESA SMOS mission [7,8]. In this regard, downscaling or fusing techniques are applied to combine L-band radiometric data with optical/infrared imagery from multispectral sensors (e.g., SMOS and MODIS). The results of the research of [9] supported the use of MODIS-derived high resolution land surface temperature (LST) and SWIR-based vegetation indices to disaggregate SMOS observations
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