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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 242-249     DOI: 10.6046/gtzyyg.2019.03.30
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Comparison and analysis of water indexes in muddy coasts based on MODIS data: A case study of the Yellow River Delta coast
Jiaping WU1, Yang ZHANG1(), Jie ZHANG2, Shenglong FAN1, Chao YANG1, Xiaofang ZHANG1
1. College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2. Poyang Lake Research Center, Jiangxi Academy of Sciences, Nanchang 330096, China
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Abstract  

Muddy coastal areas have a unique and complex water environment. It is of great scientific significance to deeply analyze the water extraction efficiency of water index in this area. The authors took the Yellow River Delta coast as the study area and used the MODIS and Landsat remote sensing data of 2008, 2009 and 2015. The water extraction performance of 6 water index (NDWI, MNDWI, AWEInsh, AWEIsh, TCW, WI2015) were analyzed from spectral characteristics of land cover types. The best threshold of each water index was obtained through the ROC curve. The accuracy and extraction errors of water indexes in muddy coastal area were studied, and the influence of different land cover factors on water extraction was analyzed. The results show that the AWEInsh have the best performance in extraction of water, with an overall accuracy of 97.29%, mapping accuracy of 96.84%, and user accuracy of 97.69%. The accuracy of seawater extraction by different water indexes is higher than 90%. The extraction accuracy of land water is at general level and the map precision is less than 80%. The capability of NDWI for identifying tidal flat water is poor, and the accuracy of mapping is lower than that of other water indexes. The different water indexes have high omission error of land water, and the omission errors of seawater and tidal flat water are low. The MNDWI has the highest omission error of seawater. The influence of the tidal flat soil on the water extraction is the greatest, followed by the cultivated soil. The sparse vegetation, luxuriant vegetation, and built-up area have the least impact. This study provides a reference for the further development of water extraction methods suitable for muddy coastal areas.
Keywords MODIS      water index      ROC      accuracy evaluation      Yellow River Delta coast     
:  TP79  
Corresponding Authors: Yang ZHANG     E-mail: zhangyang2907@163.com
Issue Date: 30 August 2019
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Jiaping WU
Yang ZHANG
Jie ZHANG
Shenglong FAN
Chao YANG
Xiaofang ZHANG
Cite this article:   
Jiaping WU,Yang ZHANG,Jie ZHANG, et al. Comparison and analysis of water indexes in muddy coasts based on MODIS data: A case study of the Yellow River Delta coast[J]. Remote Sensing for Land & Resources, 2019, 31(3): 242-249.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.30     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/242
Fig.1  Location of the study area
传感器 Landsat影像 MODIS影像
成像时间 轨道号 传感器 成像时间 轨道号
Landsat5 TM 20081007T02: 26 121/34 MODIS/Terra 20081007T02: 10 h27v05
Landsat5 TM 20090503T02: 29 121/34 MODIS/Terra 20090503T02: 10 h27v05
Landsat5 TM 20090519T02: 29 121/34 MODIS/Terra 20090519T02: 10 h27v05
Landsat5 TM 20090604T02: 29 121/34 MODIS/Terra 20090604T02: 10 h27v05
Landsat7 ETM+ 20150325T02: 40 121/34 MODIS/Terra 20150325T02: 55 h27v05
Tab.1  Description of remote sensing data used in the study
MODIS影像成像时间 海水 潮滩水体 陆地水体 潮滩土壤 耕地土壤 稀疏植被 茂盛植被 建成区
20081007T02: 10 3 480 108 412 468 109 1 429 707 287
20090503T02: 10 3 402 106 492 783 1 764 35 0 418
20090519T02: 10 3 422 55 523 848 1 121 470 14 547
20090604T02: 10 3 492 41 467 1 062 498 537 301 602
20150325T02: 55 3 408 171 421 648 1 701 0 0 651
Tab.2  Distribution of sample numbers of various land cover types
水体指数 来源 公式
TCW Crist[3] 0.031 5B3+0.202 1B4+0.310 2B1+
0.159 4B2-0.680 6B6-0.610 9B7
NDWI McFeeters[4] (B4-B2)/(B4+B2)
MNDWI 徐涵秋[5] (B4-B6)/(B4+B6)
AWEInsh Feyisa等[6] 4(B4-B6)-(0.25B2+2.75B7)
AWEIsh Feyisa等[6] B3+2.5B4-1.5(B2+B6)-0.25B7
WI2015 Fisher等[7] 1.720 4+171B4+3B1-70B2-45B6-71B7
Tab.3  Calculation formulas of water indexes
Fig.2  ROC curves of different water indexes
Fig.3  Surface reflectance characteristics of MODIS in the tidal flats
水体
指数		 AUC/% FPR/% Kappa
系数		 总体精
度/%		 制图精
度/%		 用户精
度/%
NDWI 99.16 2.34 0.93 96.59 95.78 98.21
MNDWI 98.06 3.36 0.90 93.48 93.93 97.41
AWEInsh 99.54 2.14 0.95 97.29 96.84 97.69
AWEIsh 99.18 2.94 0.94 96.76 96.53 97.51
TCW 99.38 2.38 0.94 96.68 96.63 98.23
WI2015 99.28 3.50 0.94 96.92 97.22 97.39
Tab.4  The average water extraction accuracies of different water indexes
Fig.4  Mapping precisions of three water bodies with different water indexes
误差 地表覆盖类型 NDWI MNDWI AWEInsh AWEIsh TCW WI2015
错分误差 潮滩土壤 1.34 2 1.28 1.66 1.41 1.98
耕地土壤 0.41 0.57 0.3 0.55 0.34 0.59
稀疏植被 0 0.02 0.01 0 0.02 0.01
茂盛植被 0 0 0 0 0 0
建成区 0.03 0 0.01 0.02 0.01 0.02
漏分误差 海水 0.32 2.88 0.22 0.26 0.28 0.18
潮滩水体 0.82 0.11 0.07 0.22 0.06 0.11
陆地水体 3.1 3.08 2.85 3 3.04 2.5
Tab.5  Average water extraction errors of different water indexes(%)
Fig.5  Water extraction result diagrams of MNDWI
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