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Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model
Open AccessArticle

Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

1
CESBIO, Université de Toulouse, CNRS/UPS/IRD/CNES/INRAE, 18 Avenue Edouard Belin, bpi 2801, 31401 Toulouse CEDEX 9, France
2
CNRM, Université de Toulouse, Meteo-France, CNRS, 31057 Toulouse, France
3
INRAE, TETIS, University of Montpellier, 500 rue François Breton, 34093 Montpellier CEDEX 5, France
4
CNRS, IRD, University Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, France
*
Author to whom correspondence should be addressed.
Water 2020, 12(3), 866; https://doi.org/10.3390/w12030866
Received: 5 February 2020 / Revised: 14 March 2020 / Accepted: 16 March 2020 / Published: 20 March 2020
(This article belongs to the Special Issue Applications of Remote Sensing and GIS in Hydrology II)
The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as “Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)” proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites. View Full-Text
Keywords: change detection algorithm; Sentinel-1; soil moisture; MODIS; ASCAT; Sentinel-2 change detection algorithm; Sentinel-1; soil moisture; MODIS; ASCAT; Sentinel-2
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Foucras, M.; Zribi, M.; Albergel, C.; Baghdadi, N.; Calvet, J.-C.; Pellarin, T. Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy. Water 2020, 12, 866.

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