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Surface water extraction in Yangtze River Basin based on sentinel time series image

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing 210008, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：liuyuchen191@mails.ucas.ac.cn
  • Introduction：刘宇晨，1997年生，男，硕士研究生，研究方向为水体遥感智能提取。E-mail： liuyuchen191@mails.ucas.ac.cn
LIU Yuchen12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

  • Email：yngao@hhu.edu.cn
  • Introduction：高永年，1977年生，男，教授，研究方向为流域遥感。E-mail： yngao@hhu.edu.cn
GAO Yongnian3*

Resümee

Traditional water extraction algorithms are mostly based on single-scene remote sensing image of a certain period and cannot show the highly variable characteristics of water bodies over time and space. Although some time series water products have appeared in China and abroad, their spatial resolution and water boundary accuracy still cannot meet the needs of some studies and applications. This paper takes the Yangtze River Basin with complex surface environment as the research area based on the Google Earth Engine (GEE) cloud platform. The Sentinel-2 MSI annual long time series image sets are combined with the “temporal characteristics” of pixels, and a high-precision water extraction algorithm with more universality, operability, and better effect in large-scale environment is proposed. Specifically, an algorithm based on time series image data is combined with multi-index and “temporal characteristic” fusion Digital Elevation Model (DEM). This algorithm selects automated water extraction index, Modified Normalized Difference Water Index (MNDWI), normalized difference vegetation index, and enhanced vegetation index for multi-index logical combination to extract water bodies. Near-infrared band reflectivity value and slope data set generated by SRTM DEM are used to assist in suppressing high reflectivity noise and shadow noise. The accuracy of water bodies in the whole basin is verified with the validation sample points, and the correct extraction rate is more than 96% through visual interpretation. The accuracy evaluation at the subpixel level shows that the mixed edge pixels account for 3.37% of the total pixels, the misclassification error is 0.46%, and the omission error is 0.21%, indicating that the proposed algorithm has a good inhibitory effect on the mixed pixels. Compared with the traditional NDWI and MNDWI water index based on spectral characteristics, the multi-index combined with temporal characteristic algorithm has better effect in suppressing shadow noise. Compared with some existing water products, the proposed algorithm can ensure the integrity of the whole water area and retain the local details of the water body. It has certain advantages in the extraction of small water bodies. Results of the remote sensing extraction of water bodies in the Yangtze River Basin show that the spatial distribution of water bodies in the basin is uneven, and the temporal and spatial changes in various water body types are obvious. From 2017 to 2020, 67.41% of the increase in permanent water bodies is transformed from seasonal water bodies, and the mutual conversion between seasonal water bodies and nonwater bodies is the most obvious. In addition, 74.64% of the increase in seasonal water bodies is converted from nonwater bodies, and 56.25% of the decrease in seasonal water bodies is converted to nonwater bodies. Experimental results show that the proposed algorithm has a certain universal importance in extracting water bodies in different spatiotemporal locations and different environments and can effectively avoid the phenomenon of “the same objects with different spectra” and “the same spectra with different objects” caused by the mixing of water and other ground objects. This algorithm has a good inhibitory effect on complex background noise and has high accuracy and precision.

Schlüsselwort

Google Earth Engine(GEE);Sentinel-2;remote sensing extraction of water bodies;temporal characteristics;Multi-index combination;shadow noise

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