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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (6) : 64-76     DOI: 10.6046/zrzyyg.2024342
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Band selection optimization for constructing water indices based on Sentinel-2A/B data
XIA Xingsheng1,2,3(), LEI Boyang1,2,3, DOU Chunjuan1,2,3, CHEN Qiong1,2,3, PAN Yaozhong1,4
1. Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education), Qinghai Normal University, Xining 810016, China
2. Qinghai Provincial Key Laboratory of Physical Geography and Environmental Process, School of Geographical Science, Qinghai Normal University, Xining 810016, China
3. Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
4. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Abstract  

The simple and efficient water index method has been widely used to monitor and identify surface water along with its spatiotemporal variations. However, with the application of narrow-band multispectral sensors, this method faces a challenge in selecting optimal bands with similar features when the data source changes during large-scale water monitoring. Guided by the normalized difference water index (NDWI) and the modified NDWI (MNDWI), and based on the Google Earth Engine (GEE) platform, this study constructed water indices using the green bands and eight red bands from the Sentienl-2A/B multispectral sensor data. Employing Otsu's method, this study identified and extracted water bodies in six quadrats measuring 90 km × 90 km across different temporal and spatial ranges in China. The results indicate that the optimal band combination for water body extraction varied across different times and locations. Compared to the eight water indices constructed from the Sentienl-2A/B multispectral sensor data, the water index based on the combination of B3 and B11 bands, combined with Otsu's method, achieved optimal water identification and extraction results. These results were observed in summer in the lake regions of the Northeast China Plain and mountains, the eastern plains, the Inner Mongolian Plateau, the Yunnan-Guizhou Plateau, Xinjiang, and the Qinghai-Tibet Plateau. In both spring and summer, the water index based on the combination of B3 and B11 bands exhibited an overall accuracy (OA) exceeding 90% and a Kappa coefficient above 0.9, indicating its applicability across different time periods. Overall, the results of this study provide a reference for the design and development of sensors targeting water extraction and monitoring, and for feature band selection in water monitoring and extraction applications based on narrow-band remote sensing data.

Keywords Sentinel-2A/B      water index      water body extraction      band selection optimization      Google Earth Engine (GEE)     
ZTFLH:  TP79  
Issue Date: 31 December 2025
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Xingsheng XIA
Boyang LEI
Chunjuan DOU
Qiong CHEN
Yaozhong PAN
Cite this article:   
Xingsheng XIA,Boyang LEI,Chunjuan DOU, et al. Band selection optimization for constructing water indices based on Sentinel-2A/B data[J]. Remote Sensing for Natural Resources, 2025, 37(6): 64-76.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024342     OR     https://www.gtzyyg.com/EN/Y2025/V37/I6/64
Fig.1  Study quadrats
波段 中心波长/nm 光谱带
宽/nm
空间分
辨率/m
Bl(Coastal aerosol) 443.9/442.3 27/45 60
B2(Blue) 496.6/492.1 98/98 10
B3(Green) 560/559 45/46 10
B4(Red) 664.5/665 38/39 10
B5(Vegetation Red Edge) 703.9/703.8 19/20 20
B6(Vegetation Red Edge) 740.2/739.1 18/18 20
B7(Vegetation Red Edge) 782.5/779.7 28/28 20
B8(NIR) 835.1/833 145/133 10
B8A(Narrow NIR) 864.8/864 33/32 20
B9(Water Vapour) 945/943.2 26/27 60
B10(SWIR-Cirrus) 1 373.5/1 376.9 75/76 60
B11(SWIR) 1 613.7/1 610.4 143/141 20
B12(SWIR) 2 202.4/2 185.7 242/238 20
Tab.1  Sentinel-2A/B satellite multispectral data parameters
样方
1
2
3
4
5
6
Tab.2  Spectral feature analysis
Fig.2  Change of pixel of eight exponents in six quadrats
波段组合 样方1 样方2 样方3 样方4 样方5 样方6
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.3  Spatial distribution of 8 indices in six quadrats
类别 春季 夏季 秋季 冬季
样方1
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.4  Water extraction results from the sample area of lake district in the Northeast Plain mountainous region
类别 春季 夏季 秋季 冬季
样方2
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.5  Water extraction results from the lake sample area in the Eastern Plain region
类别 春季 夏季 秋季 冬季
样方3
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.6  Water extraction results of sample area in lake area of the Inner Mongolian plateau
类别 春季 夏季 秋季 冬季
样方4
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.7  Water extraction results of lake sample area in the Yunnan-Guizhou plateau
类别 春季 夏季 秋季 冬季
样方5
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.8  Water extraction results from lake sample areas in Xinjiang region
类别 春季 夏季 秋季 冬季
样方6
B3B4
B3B5
B3B6
B3B7
B3B8
B3B8A
B3B11
B3B12
Tab.9  Water extraction results from lake sample areas in the Qinghai Tibet Plateau region
Fig.3  Evaluation results of extraction accuracy of six quadrat water bodies
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