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    Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil

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    Amazirh et al_2018 (1)_removed.pdf
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    Type
    Article
    Authors
    Amazirh, Abdelhakim
    Merlin, Olivier
    Er-Raki, Salah
    Gao, Qi
    Rivalland, Vincent
    Malbeteau, Yoann
    Khabba, Said
    Escorihuela, Maria José cc
    KAUST Department
    Water Desalination and Reuse Research Center (WDRC)
    Biological and Environmental Sciences and Engineering (BESE) Division
    Date
    2018-04-24
    Online Publication Date
    2018-04-24
    Print Publication Date
    2018-06
    Embargo End Date
    2020-04-24
    Permanent link to this record
    http://hdl.handle.net/10754/627861
    
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    Abstract
    Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015–2016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39°–43°). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03 m3 m−3, which is far smaller than 0.16 m3 m−3 when using S1 (VV) only.
    Citation
    Amazirh A, Merlin O, Er-Raki S, Gao Q, Rivalland V, et al. (2018) Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil. Remote Sensing of Environment 211: 321–337. Available: http://dx.doi.org/10.1016/j.rse.2018.04.013.
    Sponsors
    This study was conducted within the International Joint Laboratory-TREMA (http://trema.ucam.ac.ma/), and received funding from the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action (REC project, grant agreement no: 645642) http://rec.isardsat.com/. The MIXMOD-E project (ANR-13-JS06-0003-01) is also acknowledged. We would like to thank also the Moroccan CNRST (Centre National pour la Recherche Scientifique et Technique) for awarding a PhD scholarship to Abdelhakim Amazirh.
    Publisher
    Elsevier BV
    Journal
    Remote Sensing of Environment
    DOI
    10.1016/j.rse.2018.04.013
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S0034425718301585
    https://hal.archives-ouvertes.fr/hal-01912888/file/Amazirh%20et%20al_2018%20%281%29.pdf
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.rse.2018.04.013
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Water Desalination and Reuse Research Center (WDRC)

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