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Remote Sens. 2015, 7(8), 9954-9974; doi:10.3390/rs70809954

On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation

1
Instituto Hispano Luso de Investigaciones Agrarias, Universidad de Salamanca. Duero 12, 37185 Villamayor (Salamanca), Spain
2
Universitat Politècnica de Catalunya-BarcelonaTech, Department of Signal Theory and Communications (TSC). Jordi Girona 1-3, 08034 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 14 June 2015 / Revised: 20 July 2015 / Accepted: 31 July 2015 / Published: 5 August 2015
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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). View Full-Text
Keywords: GNSS-R; Landsat 8; airborne; soil moisture; reflectivity; temperature; synergy GNSS-R; Landsat 8; airborne; soil moisture; reflectivity; temperature; synergy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sánchez, N.; Alonso-Arroyo, A.; Martínez-Fernández, J.; Piles, M.; González-Zamora, Á.; Camps, A.; Vall-llosera, M. On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation. Remote Sens. 2015, 7, 9954-9974.

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