气候变化领域集成服务门户(CCPortal): Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data

CCPortal

DOI	10.1016/j.rse.2020.111803
	Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data
	Yang X.; Qin Q.; Yésou H.; Ledauphin T.; Koehl M.; Grussenmeyer P.; Zhu Z.
发表日期	2020
ISSN	00344257
卷号	244
英文摘要	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 intra-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. © 2020 Elsevier Inc.
英文关键词	Dynamic mapping; France; Google Earth Engine; Monthly; Sentinel-2; Spectral indices; Superpixel; Water bodies
语种	英语
scopus关键词	Dynamics; Extraction; Lakes; Satellite imagery; Superpixels; Time series analysis; Automatic monitoring; Cloud detection algorithms; Confusion matrices; Correlation coefficient; High resolution analysis; Multispectral satellite imagery; Seasonal time series; Seasonal variability; Small satellites; accuracy assessment; algorithm; annual variation; estimation method; multispectral image; pixel; satellite data; satellite imagery; Sentinel; spatiotemporal analysis; surface water; Loir et Cher
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179321
作者单位	Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, 100871, China; Photogrammetry and Geomatics Group, ICube UMR 7357, INSA Strasbourg, University of Strasbourg, Strasbourg, 67084, France; Geographic information system technology innovation center of the Ministry of natural resources of China, Beijing, China; ICube-SERTIT, UMR 7357, Institut Telecom Physiques Strasbourg, University of Strasbourg, Illkirch Graffenstaden, 67412, France; Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, United States
推荐引用方式 GB/T 7714	Yang X.,Qin Q.,Yésou H.,et al. Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data[J],2020,244.
APA	Yang X..,Qin Q..,Yésou H..,Ledauphin T..,Koehl M..,...&Zhu Z..(2020).Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data.Remote Sensing of Environment,244.
MLA	Yang X.,et al."Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data".Remote Sensing of Environment 244(2020).