基于Sentinel-2影像的多种水体指数法提取地表水对比研究
Comparative Study of Multiple Water Index Methods for Surface Water Extraction based on Sentinel-2 Imagery
赵爽 1杨磊库 2刘凯 3冯叶 1梁新歌 1崔培培 4宋春桥3
作者信息
- 1. 河南理工大学 测绘与国土信息工程学院,河南 焦作 454000;中国科学院南京地理与湖泊研究所 流域地理学重点实验室,江苏 南京 210008
- 2. 河南理工大学 测绘与国土信息工程学院,河南 焦作 454000
- 3. 中国科学院南京地理与湖泊研究所 流域地理学重点实验室,江苏 南京 210008
- 4. 中国科学院南京地理与湖泊研究所 流域地理学重点实验室,江苏 南京 210008;江苏师范大学 地理测绘与城乡规划学院,江苏 徐州 221116
- 折叠
摘要
高时空分辨率的Sentinel-2影像日渐成为地表水体提取的主要遥感数据源,开展基于该卫星影像的多种水体指数方法提取效果的对比研究,对提升地表水遥感监测能力具有重要参考价值.本研究针对目前较为常用的7种水体指数(NDWI、MNDWI、AWEInsh、AWEIsh、WI2015、CDWI和MNDWI_VIs),以分布在华北、东北、长江中下游和西北的具有不同地表水体类型组合特征的4个样区为例,在GEE(Google Earth Engine)平台上采用Sentinel-2 MSI影像实现了基于7种水体指数的地表水提取,进而定量分析了不同指数提取水体的精度.结果表明:总体而言,7种水体指数均可以较好识别地表水,但在不同类型的地表水体提取时的表现存在一定的差异;NDWI指数在瞬时性水体(如水田、洪泛区等)会低估地表水的分布,漏分率较高;而AWEInsh、AWEIsh和WI2015指数整体存在高估倾向,错分率较高;MNDWI_VIs水体指数在复杂水体类型的区域提取精度保持最高;在长时序水体变化监测方面,7种水体的性能表现与基于单景影像所得结论基本一致.本研究为不同类型水体开展地表水监测提供了重要的科学依据.
Abstract
The high spatial and temporal resolution Sentinel-2 images are increasingly becoming the primary re-mote sensing data source for surface water extraction.A comparative study of the extraction effects of various water index methods based on this satellite image is a significant reference value for improving surface water's remote sensing monitoring capability.In this study,the seven water indexes(NDWI,MNDWI,AWEInsh,AWEIsh,WI2015,CDWI and MNDWI_VIs)are used to extract surface water from four sample areas with different combinations of surface water types in North China,Northeast China,the middle and lower reaches of the Yangtze River and Northwest China.The water indexes'accuracy is quantified using Sentinel-2 MSI imag-es on the GEE(Google Earth Engine)platform.The results show that,all seven water indexes generally can identify surface water well,but there are some differences in performance when extracting different types of sur-face water bodies;the NDWI index underestimate the distribution of surface water in transient water bodies(e.g.,paddy fields,floodplains,etc.)and have a high miss-score speed;while the AWEInsh,AWEIsh and WI2015 indexes have an overall tendency to overestimate and have a high miss-score rate;the MNDWI_VIs water index maintains the highest extraction accuracy in areas with complex water index;in the field of monitor-ing water changes in long time series,the performance of the seven water bodies is generally consistent with the conclusions obtained based on single-view imagery.This study provides an essential scientific basis for carrying out surface water monitoring in different water bodies.
关键词
Google/Earth/Engine/Sentinel-2/水体指数/混淆矩阵/水体频率Key words
Google Earth Engine/Sentinel-2/Water Index/Confusion matrix/Water Frequency引用本文复制引用
基金项目
中国科学院项目(XDA23100102)
科技部项目(2019YFA0607101)
出版年
2024
NETL
NSTL
万方数据