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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (5) : 53-61     DOI: 10.6046/zrzyyg.2024198
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A precise coastline extraction method using surface moisture content and long-time-series remote sensing imagery
GONG Shaojun1(), CHEN Chao2(), FAN Jing3
1. College of Marine Science and Technology，Zhejiang Ocean University，Zhoushan 316022，China
2. School of Geography Science and Geomatics Engineering，Suzhou University of Science and Technology，Suzhou 215009，China
3. Zaozhuang Network Security Guarantee Center，Zaozhuang 277899，China
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Abstract  

Coastlines serve as one of the most essential basic geographic elements. However，conventional methods generally face challenges in the accurate detection of their location，due to instantaneous remote sensing imaging and dynamic tidal phenomena. In response to this，this study developed a novel coastline extraction model that incorporates information on surface moisture content derived from long-time-series satellite remote sensing imagery. First，all available remote sensing images covering the study area during the target period were acquired to construct a high-quality remote sensing image stack. Second，the wetness components indicative of the surface moisture content were obtained using the tasseled cap transformation （TCT），from which a wetness index stack was constructed. Then，the wetness components were subjected to maximum value synthesis using the maximum spectral index composite （MSIC） algorithm，generating a maximum water surface composite image. Finally，the composite image was segmented using the OTSU algorithm to extract accurate coastline information. Validation experiments were conducted on Zhoushan Island using the Google Earth Engine （GEE） cloud computing platform and remote sensing imagery from the operational land imager （OLI） onboard the Landsat 8 satellite. The results indicate that the proposed model can precisely locate different types of coastlines with high spatial accuracy. Compared to visual interpretation，the model exhibited a mean deviation and a root mean square error （RMSE） of 3.42 m and 6.79 m，respectively，with 99.42% of validation points falling within one pixel width. This study provides an effective technical framework for high-accuracy coastline extraction，holding great significance for scientific management and sustainable development of coastal resources.
Keywords coastline      wetness component      tasseled cap transformation （TCT）      long-time-series remote sensing imagery      precise localization     
ZTFLH:  TP79  
Issue Date: 28 October 2025
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Articles by authors
Shaojun GONG
Chao CHEN
Jing FAN
Cite this article:   
Shaojun GONG,Chao CHEN,Jing FAN. A precise coastline extraction method using surface moisture content and long-time-series remote sensing imagery[J]. Remote Sensing for Natural Resources, 2025, 37(5): 53-61.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024198     OR     https://www.gtzyyg.com/EN/Y2025/V37/I5/53
Fig.1  Image of the study area
Fig.2  Flowchart of the study
序号 成像日期 行 列 云量/% 潮汐信息（北京时间） 序号 成像日期 行 列 云量/% 潮汐信息（北京时间）
验潮站 高潮时刻 低潮时刻 验潮站 高潮时刻 低潮时刻
1 2022-01-03 117 39 1.05 沈家门 09：56 03：26 9 2022-03-24 117 40 13.50 岱山 13：54 08：54
2 2022-01-03 117 40 3.25 沈家门 09：56 03：26 10 2022-04-09 117 40 8.55 岱山 15：04 10：17
3 2022-01-19 117 40 0.35 沈家门 10：40 04：20 11 2022-05-02 118 39 11.24 岱山 11：12 05：46
4 2022-02-27 118 39 0.08 沈家门 07：03 14：43 12 2022-08-22 118 39 9.55 岱山 06：28 12：32
5 2022-03-08 117 39 0.05 沈家门 06：40 12：17 13 2022-10-02 117 39 2.63 岱山 14：52 08：16
6 2022-03-08 117 40 0.07 沈家门 06：40 12：17 14 2022-10-02 117 40 3.88 岱山 14：52 08：16
7 2022-03-15 118 39 0.15 沈家门 08：11 14：50 15 2022-11-10 118 39 11.81 岱山 11：30 05：21
8 2022-03-24 117 39 0.03 沈家门 12：42 07：24 16 2022-11-26 118 39 6.21 岱山 12：08 05：44
Tab.1  Remote sensing images and tidal information
缨帽变换后分量 波段
蓝光 绿光 红光 近红外 短波红外1 短波红外2
亮度指数 -0.236 3 -0.283 6 -0.425 7 0.809 7 0.004 3 -0.164 0
绿度指数 0.130 1 0.229 0 0.349 2 0.179 5 -0.627 0 -0.620 0
湿度指数 -0.823 9 0.084 9 0.439 6 -0.058 0 0.201 3 -0.277 0
TCT4 -0.329 4 0.055 7 0.105 6 0.185 5 -0.434 9 0.808 5
TCT5 0.107 9 -0.902 3 0.411 9 0.057 5 -0.025 9 0.025 2
TCT6 0.344 3 0.405 7 0.466 7 0.534 7 0.393 0 0.241 2
Tab.2  Tasseled cap transformation coefficients for Landsat8 satellite reflectance
Fig.3  Result of coastline extraction
Fig.4  Coastline extraction of different methods
Fig.5  Comparison of coastline with different types
方法 面积/km2 长度/km 面积误差/% 长度误差/%
目视解译 514.43 146.84 — —
本文方法 513.68 150.51 -0.14 2.50
NDWI法 516.19 152.40 0.34 3.79
MNDWI法 516.18 152.76 0.34 4.04
Tab.3  Comparisons in area and length of the coastline
Fig.6  Distribution map of points at different distances
方法 选取点数/个 距离平均值/m RMSE/m
本文方法 1 200 3.42 6.79
NDWI法 1 200 18.40 30.50
MNDWI法 1 200 18.03 30.12
Tab.4  Average and root mean square error
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