Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

REMOTE SENSING OF ENVIRONMENT

卷:244

Article

Yang, Xiucheng^1,2  Qin, Qiming^1,3  Yesou, Herve⁴  Ledauphin, Thomas⁴  Koehl, Mathieu²  Grussenmeyer, Pierre²  Zhu, Zhe⁵ 

[1] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China

[2] Univ Strasbourg, INSA Strasbourg, Photogrammetry & Geomat Grp, ICube UMR 7357, F-67084 Strasbourg, France

[3] Minist Nat Resources China, Geog Informat Syst Technol Innovat Ctr, Beijing, Peoples R China

[4] Univ Strasbourg, Inst Telecom Phys Strasbourg, ICube SERTIT, UMR 7357, F-67412 Illkirch Graffenstaden, France

[5] Univ Connecticut, Dept Nat Resources & Environm, Storrs, CT 06269 USA

关键词: Dynamic mapping;    Google Earth Engine;    Sentinel-2;    Water bodies;    France;    Superpixel;    Spectral indices;    Monthly;   

DOI  :  10.1016/j.rse.2020.111803

来源: Elsevier

【 摘 要 】

The first national product of Surface Water Dynamics in France (SWDF) is generated on a monthly temporal scale and 10-m spatial scale using an automatic rule-based superpixel (RBSP) approach. The current surface water dynamic products from high resolution (HR) multispectral satellite imagery are typically analyzed to determine the annual trend and related seasonal variability. Annual and seasonal time series analyses may fail to detect the infra-annual variations of water bodies. Sentinel-2 allows us to investigate water resources based on both spatial and temporal high-resolution analyses. We propose a new automatic RBSP approach on the Google Earth Engine platform. The RBSP method employs combined spectral indices and superpixel techniques to delineate the surface water extent; this approach avoids the need for training data and benefits large-scale, dynamic and automatic monitoring. We used the proposed RBSP method to process Sentinel-2 monthly composite images covering a two-year period and generate the monthly surface water extent at the national scale, i.e., over France. Annual occurrence maps were further obtained based on the pixel frequency in monthly water maps. The monthly dynamics provided in SWDF products are evaluated by HR satellite-derived water masks at the national scale (JRC GSW monthly water history) and at local scales (over two lakes, i.e., Lake Der-Chantecoq and Lake Orient, and 200 random sampling points). The monthly trends between SWDF and GSW were similar, with a coefficient of 0.94. The confusion matrix-based metrics based on the sample points were 0.885 (producer's accuracy), 0.963 (user's accuracy), 0.932 (overall accuracy) and 0.865 (Matthews correlation coefficient). The annual surface water extents (i.e., permanent and maximum) are validated by two HR satellite image-based water maps and an official database at the national scale and small water bodies (ponds) at the local scale at Loir-et-Cher. The results show that the SWDF results are closely correlated to the previous annual water extents, with a coefficient > 0.950. The SWDF results are further validated for large rivers and lakes, with extraction rates of 0.929 and 0.802, respectively. Also, SWDF exhibits superiority to GSW in small water body extraction (taking 2498 ponds in Loir-et-Cher as example), with an extraction rate improved by approximately 20%. Thus, the SWDF method can be used to study interannual, seasonal and monthly variations in surface water systems. The monthly dynamic maps of SWDF improved the degree of land surface coverage by 25% of France on average compared with GSW, which is the only product that provides monthly dynamics. Further harmonization of Sentinel-2 and Landsat 8 and the introduction of enhanced cloud detection algorithm can fill some gaps of no-data regions.

【 授权许可】

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