Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/4E3F0BBD-BDAB-41BC-97DE-C403C1442068 | ||
| Year | 2018 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
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| Bibliographic Citation | Y. Bao, L. Lin, S. Wu, K. A. Kwal Deng and G. P. Petropoulos, "Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model," Int. J. Appl. Earth Obs. Geoinf., vol. 72, pp. 76-85, Oct. 2018. doi: 10.1016/j.jag.2018.05.026 https://doi.org/10.1016/j.jag.2018.05.026 | ||
| Appears in Collections |
In this study, is presented a new methodology for retrieving surface soil moisture (SSM) under conditions of partial vegetation cover based on the synergy between Sentinel-1 Synthetic Aperture Radar (SAR) and Landsat Operational Land Image (OLI) data. To remove the effect of vegetation on SSM retrieval, the Landsat OLI spectral index is applied to build a model for the vegetation water content estimation. The model is substituted into the original water-cloud model, and thus a modified water-cloud model with a spectral index is built. Additionally, an SSM estimation model is developed based on the modified water-cloud model. The technique was tested at two experimental sites in the UK and Spain on which reference data of SSM are acquired operationally by ground observational networks. In overall, the key findings of our study were: (1) For a vegetation-covered surface, the normalized difference water index (NDWI) obtained from the 1.57–1.65 μm band reflectance data was the most suitable for removing the effects of vegetation cover on soil water content estimation; (2) Compared to the Sentinel-1 VH polarization, the backscattering coefficient at VV polarization was more suitable for soil moisture retrieval and obtained a higher accuracy; (3) The developed model could be used to retrieve SSM under vegetation cover with a high accuracy that indicates the correlation coefficient (R) between the estimated and measured soil moisture was 0.911 and that the root mean square error (RMSE) was 0.053 cm3/cm3; (4) The model can be used to retrieve regional SSM with a high spatial and temporal resolution. Our methodology for deriving SSM offers a number of advantages for many practical applications and research alike and its use by the wider community remains to be seen.
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