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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (2) : 80-87     DOI: 10.6046/zrzyyg.2021125
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A water body identification model for lakes in Hoh Xil based on GF-6 WFV satellite data
WANG Renjun(), LI Dongying, LIU Baokang()
College of Resources and Environmental Engineering,Tianshui Normal University,Tianshui 741000,China
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Abstract  

Exploration of the water body extraction model based on GF-6 satellite images can provide new data sources and technical support for water body monitoring. First, GF-6 WFV satellite data of Zonag Lake was used to analyze the reflectance difference between water and other ground objects in each band of GF-6 WFV satellite data. Based on this, a novel water index named red side water index (RSWI) was constructed. Then, the overall accuracy and Kappa coefficient generated by the confusion matrix were used to verify RSWI and the other three water extraction models, which include the single-band threshold method, normalized difference water index, and modified shade water index. Finally, six typical lakes with different types of areas larger than 100 km2 in Hoh Xil were selected for analysis of general applicability. The results showed that compared with other methods, the decision tree model composed of RSWI and NIR bands effectively eliminates the influence of lake bottom sediments on water bodies and extracts shallow water bodies more completely, with an overall accuracy of 93.78% and a Kappa coefficient of 0.87. Additionally, it has been found that the stability and general applicability of RSWI are better than those of other water body models with respect to different types of lakes.
Keywords GF-6      lakes in Hoh Xil      water body identification model      read side water index     
ZTFLH:  TP79  
Corresponding Authors: LIU Baokang     E-mail: wrj_2021@163.com;liubk04@qq.com
Issue Date: 20 June 2022
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Renjun WANG
Dongying LI
Baokang LIU
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Renjun WANG,Dongying LI,Baokang LIU. A water body identification model for lakes in Hoh Xil based on GF-6 WFV satellite data[J]. Remote Sensing for Natural Resources, 2022, 34(2): 80-87.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021125     OR     https://www.gtzyyg.com/EN/Y2022/V34/I2/80
Fig.1  Location of study area
序号 波段 波长范围/μm
B1 蓝光 0.45~0.52
B2 绿光 0.52~0.59
B3 红光 0.63~0.69
B4 近红外 0.77~0.89
B5 红边1 0.69~0.73
B6 红边2 0.73~0.77
B7 紫边 0.40~0.45
B8 黄边 0.59~0.63
Tab.1  Bands of GF-6 image
Fig.2  Reflectance curves of typical ground objects
Fig.3  Flow chart of water extraction using RSWI
Fig.4  Water body images extracted from different water body models
Fig.5  Gray histogram of four kinds of method
Fig.6  Extraction results from different water models
方法 总体精度/% Kappa系数
RSWI 93.78 0.87
NDWI 92.41 0.85
MSWI 81.34 0.64
单波段阈值法 90.55 0.81
Tab.2  Precision contrast based on different water extraction models
湖泊名称 GF-6WFV影像 RSWI NDWI MSWI 单波段阈值法
太阳湖
可可西里湖
库赛湖
加德仁错
多格错仁错
西金乌兰湖
Tab.3  Comparison of water extraction in different lakes by different models
湖泊 湖泊性质 方法 总体精度/% Kappa系数



太阳湖


淡水湖		 RSWI 93.64 0.86
NDWI 92.37 0.83
MSWI 90.25 0.79
单波段阈值法 91.10 0.80



可可西里湖


微咸水湖		 RSWI 93.79 0.86
NDWI 88.86 0.78
MSWI 84.89 0.70
单波段阈值法 92.45 0.85



库赛湖


微咸水湖		 RSWI 97.06 0.93
NDWI 95.82 0.90
MSWI 92.86 0.84
单波段阈值法 95.79 0.91



加德仁错


咸水湖		 RSWI 96.18 0.92
NDWI 90.66 0.81
MSWI 88.11 0.76
单波段阈值法 89.81 0.79



多格错仁错


咸水湖		 RSWI 97.01 0.94
NDWI 93.69 0.87
MSWI 84.85 0.70
单波段阈值法 95.91 0.92



西金乌兰湖


盐湖		 RSWI 95.81 0.91
NDWI 94.38 0.88
MSWI 89.42 0.78
单波段阈值法 92.66 0.84
Tab.4  Precision evaluation of water extraction results from different types of lakes
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