南海典型岛礁浅海地形遥感监测及时序变化研究*

doi:10.11978/2024218

Abstract

Abstract:

The Nansha Islands, located in the southern part of the South China Sea, hold significant strategic importance for China’s maritime rights and interests, development strategy, and national security due to its geographical location. It is of great significance to national strategy to carry out research on water depth monitoring and topographic changes of Nansha Islands and reefs. In this study, combined with Sentinel-2 and ICESat-2 (ice, cloud, land elevation satellite-2) data, an active-passive fusion remote sensing sounding algorithm was used to invert the shallow sea topography of the Bai Jiao and Beizi Dao areas in the Nansha Islands. This method enables time series analysis to reveal long-term trends and short-term fluctuations in water depth and reef terrain, providing new insights into the impacts of human activities and natural factors on reef topography changes. The research results are as follows: 1) A high-precision water depth inversion model was validated, showing excellent performance in the study area (R²> 0.9, MAE < 0.4 m, RMSE < 0.7 m). 2) From 2018 to 2024, both Bai Jiao and Beizi experienced varying degrees of terrain and spatial structure changes. Due to reclamation projects, the land area of Bai Jiao increased, whereas Beizi Dao’s changes were influenced by natural factors, exhibiting dynamic variations. 3) The topographic changes of Bai Jiao were mainly driven by human factors, including land reclamation and resource exploitation, while Beizi Dao’s changes mainly resulted from natural factors such as monsoons, typhoons, seawater erosion, and sediment deposition. This study demonstrates the effectiveness of active-passive fusion bathymetry, providing a valuable reference for efficient water depth and terrain detection of typical reefs in China. Moreover, it offers crucial technical support for long-term spatiotemporal monitoring of shallow sea terrain in areas such as the Nansha Islands.

Key words: South China Sea islands and reefs, Sentinel-2, ICESat-2, bathymetric inversion, spatiotemporal monitoring

CLC Number:

P737.13

CHEN Yuchen, FU Dongyang, TAO Bangyi, LI Jizhe, ZHU Yixian, LIU Bei, LIN Ye, CHAI Xia. Study on remote sensing monitoring and time series change of shallow sea topography of typical islands and reefs in the South China Sea^*[J].Journal of Tropical Oceanography, 2025, 44(5): 140-153.

Figures/Tables 17

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References 61

[1]	白宽宽, 李志威, 张鹏, 等, 2024. 基于多源遥感解译的洞庭湖区自然湖岸线演变过程研究[J]. 湖泊科学, 36(5): 1537-1549.
	BAI KUANKUAN, LI ZHIWEI, ZHANG PENG, et al, 2024. Shoreline evolution of natural lakes in Lake Dongting area based on multi-source remote sensing interpretation[J]. Journal of Lake Sciences, 36(5): 1537-1549 (in Chinese with English abstract).
[2]	曹彬才, 方勇, 江振治, 等, 2020. ICESat-2激光卫星与光学遥感影像融合水深测量[J]. 海洋测绘, 40(5): 21-25.
	CAO BINCAI, FANG YONG, JIANG ZHENZHI, et al, 2020. Water depth measurement from the fusion of ICESat-2 laser satellite and optical remote sensing image[J]. Hydrographic Surveying and Charting, 40(5): 21-25 (in Chinese with English abstract).
[3]	陈安娜, 马毅, 张靖宇, 2018. 水深被动光学遥感反演模型适用性研究[J]. 海洋环境科学, 37(6): 953-960.
	CHEN ANNA, MA YI, ZHANG JINGYU, 2018. Study on applicability of water-depth passive optical remote sensing inversion models[J]. Marine Environmental Science, 37(6): 953-960 (in Chinese with English abstract).
[4]	陈韶阳, 2011. 南沙群岛价值分类评价和开发策略研究[D]. 青岛: 中国海洋大学.
	CHEN SHAOYANG, 2011. Research on classified value evaluation and development strategy of Nansha Islands[D]. Qingdao: Ocean University of China (in Chinese with English abstract).
[5]	郭松涛, 邢帅, 张国平, 等, 2024. 异构卫星遥感数据融合的水深反演模型[J]. 测绘通报, 2024(5): 19-23.
	GUO SONGTAO, XING SHUAI, ZHANG GUOPING, et al, 2024. Bathymetric inversion model for fusion of heterogeneous satellite remote sensing data[J]. Bulletin of Surveying and Mapping, 2024(5): 19-23 (in Chinese with English abstract).
[6]	孔瑞瑶, 谢涛, 马明, 等, 2022. CatBoost模型在水深反演中的应用[J]. 测绘通报, (7): 33-37.
	KONG RUIYAO, XIE TAO, MA MING, et al, 2022. Application of CatBoost model in water depth inversion[J]. Bulletin of Surveying and Mapping, (7): 33-37 (in Chinese with English abstract).
[7]	赖文典, 2022. 基于神经网络从大气层顶光学卫星数据反演光学浅水水深[D]. 福建: 厦门大学: 1-74.
	LAI WENDIAN, 2022. A neural network basef-algorithm to retrieve bottom depth of optically shallow waters from top-of-atmosphere measurements[D]. Fujian: Xiamen University: 1-74 (in Chinese with English abstract).
[8]	李晨轩, 韦志刚, 2024. 1979—2021年中国南海海表风速和大风事件的变化特征[J]. 高原气象, 43(3): 696-710.
	LI CHENXUAN, WEI ZHIGANG, 2024. Variation characteristics of surface wind speed and gale events in the South China Sea from 1979 to 2021[J]. Plateau Meteorology, 43(3): 696-710 (in Chinese with English abstract).
[9]	李宁宁, 陈义兰, 唐秋华, 等, 2022. 岛礁周边水深光学遥感反演模型比较与分析[J]. 海洋测绘, 42(1): 50-54.
	LI NINGNING, CHEN YILAN, TANG QIUHUA, et al, 2022. Comparison and analysis of bathymetric optical remote sensing inversion models around islands[J]. Hydrographic Surveying and Charting, 42(1): 50-54 (in Chinese with English abstract).
[10]	李全兴, 潘国富, 钱万民, 1996. 水深测量与海底面状况声探测技术的进展[J]. 海洋测绘, 16(1): 6-11.
	LI QUANXING, PAN GUOFU, QIAN WANMIN, 1996. Progress in bathymetry and acoustic detection technology of seabed condition[J]. Hydrographic Surveying and Charting, 16(1): 6-11 (in Chinese).
[11]	李文杰, 闫世强, 蒋莹, 等, 2019. 自适应确定DBSCAN算法参数的算法研究[J]. 计算机工程与应用, 55(5): 1-7, 148.
	LI WENJIE, YAN SHIQIANG, JIANG YING, et al, 2019. Research on method of self-adaptive determination of DBSCAN algorithm parameters[J]. Computer Engineering and Applications, 55(5): 1-7, 148 (in Chinese with English abstract).
[12]	李晓敏, 2021. 南海西沙群岛珊瑚岛礁高分遥感监测与动态研究[D]. 呼和浩特: 内蒙古大学.
	LI XIAOMIN, 2021. High-resolution remote sensing monitoring and dynamics of coral islands and reefs in the Xisha Islands, South China Sea[D]. Huhehaote: Inner Mongolia University (in Chinese with English abstract).
[13]	林丽茹, 赵辉, 2012. 南海海域浮游植物叶绿素与海表温度季节变化特征分析[J]. 海洋学研究, 30(4): 46-54.
	LIN LIRU, ZHAO HUI, 2012. Analysis on the relations between sea surface temperature and phytoplankton Chlorophyll-a in the South China Sea[J]. Journal of Marine Sciences, 30(4): 46-54 (in Chinese with English abstract).
[14]	刘宝银, 2001. 南沙群岛遥感融合信息特征分析与计量[M]. 北京: 海洋出版社.
	LIU BAOYIN, 2001. Analysis and measurement of remote sensing fusion information characteristics in Nansha Islands[M]. Beijing: Ocean Press (in Chinese).
[15]	刘长达, 2022. ICESat-2与Sentinel-2浅海水深反演及海岸带分类研究[D]. 青岛: 自然资源部第一海洋研究所.
	LIU CHANGDA, 2022. Research of shallow bathymetry and coastal zone classification using ICESat-2 and sentinel-2[D]. Qingdao: First Institute of Oceanography, MNR: 1-57 (in Chinese with English abstract).
[16]	马毅, 张杰, 张靖宇, 等, 2018. 浅海水深光学遥感研究进展[J]. 海洋科学进展, 36(3): 331-351.
	MA YI, ZHANG JIE, ZHANG JINGYU, et al, 2018. Progress in shallow water depth mapping from optical remote sensing[J]. Advances in Marine Science, 36(3): 331-351 (in Chinese with English abstract).
[17]	孟文君, 李杰, 张凯, 等, 2021. 基于改进DBSCAN算法的ICESat-2海面点数据去噪处理及精度评估[J]. 海洋通报, 40(6): 675-682.
	MENG WENJUN, LI JIE, ZHANG KAI, et al, 2021. De-noising and accuracy evaluation of ICESAT-2 sea surface data based on DBSCAN algorithm[J]. Marine Science Bulletin, 40(6): 675-682 (in Chinese with English abstract).
[18]	潘春梅, 丁谦, 曹文玉, 2002. 南沙群岛岛礁地形地貌TM影像特征分析[J]. 国土资源遥感, 14(2): 34-37, 60.
	PAN CHUNMEI, DING QIAN, CAO WENYU, 2002. TM image analysis of island reef topography of the Nansha Islands[J]. Remote Sensing for Land & Resources, 14(2): 34-37, 60 (in Chinese with English abstract).
[19]	宋玮, 张杰, 姬光荣, 等, 2003. 基于SAR与TM图像的南沙双子群礁特征分析[C]// 海洋环境科学与数值模拟国家海洋局重点实验室, 中国海洋学会海洋遥感专业委员会, 中国遥感委员会, 等, 2003. 第十四届全国遥感技术学术交流会论文摘要集. 海洋科学进展, 22(suppl): 177-181 (in Chinese).
[20]	唐蕾, 2023. 1993—2019年南海海平面高度时空变化研究[D]. 金华: 浙江师范大学: 1-54.
	TANG LEI, 2023. Spatiotemporal variations of sea level in the South China during 1993-2019[D]. Jinhua: Zhejiang Normal University: 1-54 (in Chinese with English abstract).
[21]	王媞, 丛帅, 陈健斌, 等, 2024. 台风“杜苏芮”远端影响下黄河三角洲沉积特征变化[J]. 海洋地质与第四纪地质, 44(5): 50-57.
	WANG TI, CONG SHUAI, CHEN JIANBIN, et al, 2024. Variations in sedimentation characteristics of the Yellow River Delta under the remote influence of typhoon Doksuri[J]. Marine Geology & Quaternary Geology, 44(5): 50-57 (in Chinese with English abstract).
[22]	汪万智, 邹欣庆, 李海宇, 2022. 南海珊瑚礁光学浅水海域多光谱遥感水深反演[J]. 高校地质学报, 28(5): 758-767.
	WANG WANZHI, ZOU XINQING, LI HAIYU, 2022. Water depth inversion from multispectral imagery over optically shallow waters of coral reefs in South China Sea[J]. Geological Journal of China Universities, 28(5): 758-767 (in Chinese with English abstract).
[23]	吴士存, 2022. 南沙争端的由来与发展: 南海纷争史国别研究[M]. 北京: 中华书局 (in Chinese).
[24]	肖海婷, 黄荣永, 刘羿, 等, 2024. 台风影响下西沙灰沙岛的时空变化特征[J]. 热带海洋学报, 44(2): 157-177.
	XIAO HAITING, HUANG RONGYONG, LIU YI, et al, 2024. Spatio-temporal changes of lime-sand islands in the Xisha Islands under the impact of typhoon[J]. Journal of Tropical Oceanography, 44(2): 157-177 (in Chinese with English abstract).
[25]	谢丛霜, 陈鹏, 潘德炉, 2024. 主被动遥感融合辐射传输卷积神经网络水深反演方法[J]. 光子学报, 53(8): 46-58.
	XIE CONGSHUANG, CHEN PENG, PAN DELU, 2024. Bathymetric inversion method for active-passive remote sensing fused radiative transfer information convolutional neural networks[J]. Acta Photonica Sinica, 53(8): 46-58 (in Chinese with English abstract).
[26]	熊媛, 黄荣永, 余克服, 2022. 基于多时相多光谱遥感影像的珊瑚礁面积估算方法研究: 以西沙群岛羚羊礁为例[J]. 海洋学报, 44(8): 151-168.
	XIONG YUAN, HUANG RONGYONG, YU KEFU, 2022. Estimation of coral reef area from multi-temporal and multi-spectral satellite images: a case study on Lingyang Reef, Xisha Islands[J]. Haiyang Xuebao, 44(8): 151-168 (in Chinese with English abstract).
[27]	杨鑫科, 2024. 海洋ICESat-2卫星激光雷达数据去噪处理及典型应用[D]. 杭州: 杭州电子科技大学.
	YANG XINKE, 2024. De-noising processing and typical application of ocean ICESat-2 satellite lidar data[D]. Hangzhou: Hangzhou Dianzi University (in Chinese with English abstract).
[28]	岳子琳, 朱卫东, 邱振戈, 等, 2022. 南海珊瑚岛礁地貌遥感识别研究[J]. 海洋科学, 46(4): 67-80.
	YUE ZILIN, ZHU WEIDONG, QIU ZHENGE, et al, 2022. Remote sensing recognition of coral islands and reefs in the South China Sea[J]. Marine Sciences, 46(4): 67-80 (in Chinese with English abstract).
[29]	张更垒, 田振环, 杨娜娜, 等, 2023. 空天陆海一体化测绘技术在南海岛礁调查中的应用展望[J]. 海洋科学前沿, (4): 233-244.
	ZHANG GENGLEI, TIAN ZHENHUAN, YANG NANA, et al, 2023. Application prospect of space-air-land-sea integrated surveying and mapping technology in the investigation of islands and reefs in the South China Sea[J]. Advances in Marine Sciences, (4): 233-244 (in Chinese with English abstract).
[30]	张清凌, 陈楚群, 施平, 2003. 南沙群岛海域水体漫衰减系数K_d(490)的特性研究[J]. 热带海洋学报, 22(1): 9-16.
	ZHANG QINGLING, CHEN CHUQUN, SHI PING, 2003. Characteristics of K_d(490) around Nansha islands in South China Sea[J]. Journal of Tropical Oceanography, 22(1): 9-16 (in Chinese with English abstract).
[31]	张晓冬, 张文静, 朱首贤, 等, 2016. 海口湾可见光遥感测深方法研究[J]. 海洋通报, 35(1): 54-63.
	ZHANG XIAODONG, ZHANG WENJING, ZHU SHOUXIAN, et al, 2016. Study on the water depth extraction method using visible remote sensing in the Haikou Bay[J]. Marine Science Bulletin, 35(1): 54-63 (in Chinese with English abstract).
[32]	张艳, 2024. 融合异源卫星数据结合BRT非线性方法的河流深度反演[J]. 水利科技与经济, 30(7): 148-151.
	ZHANG YAN, 2024. River depth inversion by combining heterogeneous satellite data with BRT nonlinear method[J]. Water Conservancy Science and Technology and Economy, 30(7): 148-151 (in Chinese).
[33]	赵辉, 齐义泉, 王东晓, 等, 2005. 南海叶绿素浓度季节变化及空间分布特征研究[J]. 海洋学报, 27(4): 45-52.
	ZHAO HUI, QI YIQUAN, WANG DONGXIAO, et al, 2005. Study on the features of chlorophyll-a derived from SeaWiFS in the South China Sea[J]. Acta Oceanologica Sinica, 27(4): 45-52 (in Chinese with English abstract).
[34]	赵宗慈, 罗勇, 黄建斌, 2023. 全球变暖和厄尔尼诺事件[J]. 气候变化研究进展, 19(5): 663-666.
	ZHAO ZONGCI, LUO YONG, HUANG JIANBIN, 2023. Global warming and El Niño events[J]. Climate Change Research, 19(5): 663-666 (in Chinese with English abstract).
[35]	朱串串, 2018. 长江口九段沙面积估算模型与演变分析[D]. 南京: 南京师范大学.
	ZHU CHUANCHUAN, 2018. A dissertstion submitted in partial fulfillment of the requirements for the degree of master of science[D]. Nanjing: Nanjing Normal University (in Chinese with English abstract).
[36]	祝笑, 2024. 耦合IceSat-2和Sentinel-2卫星数据与BRT回归模型的河流水深反演[J]. 水利科技与经济, 30(10): 22-25.
	ZHU XIAO, 2024. Inversion of river depth by couplingIceSat-2 andSentinel-2 satellite data with BRT regression model[J]. Water Conservancy Science and Technology and Economy, 30(10): 22-25 (in Chinese).
[37]	ALBRIGHT A, GLENNIE C, 2021. Nearshore bathymetry from fusion of sentinel-2 and ICESat-2 observations[J]. IEEE Geoscience and Remote Sensing Letters, 18(5): 900-904.
[38]	BARET F, GUYOT G, MAJOR D J, 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation[C]// 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium. Vancouver: IEEE: 1355-1358.
[39]	CHADHA R K, LATHA G, YEH H, et al, 2005. The tsunami of the great Sumatra earthquake of M 9.0 on 26 December 2004 - Impact on the east coast of India[J]. Current Science, 88(8): 1297-1301.
[40]	DRUSCH M, DEL BELLO U, CARLIER S, et al, 2012. Sentinel-2: ESA’s optical high-resolution mission for GMES operational services[J]. Remote Sensing of Environment, 120: 25-36.
[41]	HAN TONG, ZHANG HUAGUO, CAO WENTING, et al, 2023. Cost-efficient bathymetric mapping method based on massive active-passive remote sensing data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 203: 285-300.
[42]	HODÚL M, BIRD S, KNUDBY A, et al, 2018. Satellite derived photogrammetric bathymetry[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 142: 268-277.
[43]	LAI WENDIAN, LEE ZHONGPING, WANG JUNWEI, et al, 2022. A portable algorithm to retrieve bottom depth of optically shallow waters from top-of-atmosphere measurements[J]. Journal of Remote Sensing, 2022: 9831947.
[44]	LE QUILLEUC A, COLLIN A, JASINSKI M F, et al, 2022. Very high-resolution satellite-derived bathymetry and habitat mapping using Pleiades-1 and ICESat-2[J]. Remote Sensing, 14(1): 133.
[45]	LEE Z, CARDER K L, MOBLEY C D, et al, 1998. Hyperspectral remote sensing for shallow waters. I. A semianalytical model[J]. Applied Optics, 37(27): 6329-6338.
[46]	LEE Z, CARDER K L, MOBLEY C D, et al, 1999. Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization[J]. Applied Optics, 38(18): 3831-3843.
[47]	LIU YUHUI, ZHOU YU, YANG XIAOQIANG, 2024. Bathymetry derivation and slope-assisted benthic mapping using optical satellite imagery in combination with ICESat-2[J]. International Journal of Applied Earth Observation and Geoinformation, 127: 103700.
[48]	LOWELL K, HERMANN J, 2024. Accuracy of bathymetric depth change maps using multi-temporal images and machine learning[J]. Journal of Marine Science and Engineering, 12(8): 1401.
[49]	LYU JINHAO, LI SHAOYU, WANG XIAOMING, et al, 2024. Long-term satellite-derived bathymetry of Arctic supraglacial lake from ICESat-2 and sentinel-2[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1- 2024: 469-477.
[50]	LYZENGA D R, 1978. Passive remote sensing techniques for mapping water depth and bottom features[J]. Applied Optics, 17(3): 379-383.
[51]	LYZENGA D R, 1985. Shallow-water bathymetry using combined lidar and passive multispectral scanner data[J]. International Journal of Remote Sensing, 6(1): 115-125.
[52]	LYZENGA D R, MALINAS N P, TANIS F J, 2006. Multispectral bathymetry using a simple physically based algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2251-2259.
[53]	MA YUE, XU NAN, LIU ZHEN, et al, 2020. Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets[J]. Remote Sensing of Environment, 250: 112047.
[54]	NEUMANN T A, MARTINO A J, MARKUS T, et al, 2019. The ice, cloud, and land elevation satellite-2 mission: a global geolocated photon product derived from the advanced topographic laser altimeter system[J]. Remote Sensing of Environment, 233: 111325.
[55]	PALM S P, YANG YUEKUI, HERZFELD U, et al, 2021. ICESat-2 atmospheric channel description, data processing and first results[J]. Earth and Space Science, 8(8): e2020EA001470.
[56]	PARRISH C, MAGRUDER L, NEUENSCHWANDER A, et al, 2019. Validation of ICESat-2 ATLAS bathymetry and analysis of ATLAS’s bathymetric mapping performance[J]. Remote Sensing, 11(14): 1634.
[57]	STUMPF R P, HOLDERIED K, SINCLAIR M, 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types[J]. Limnology and Oceanography, 48(1part2): 547-556.
[58]	THOMAS N, PERTIWI A P, TRAGANOS D, et al, 2021. Space-borne cloud-native satellite-derived bathymetry (SDB) models using ICESat-2 and sentinel-2[J]. Geophysical Research Letters, 48( 6): e2020GL092170.
[59]	TYCE R C, 1986. Deep seafloor mapping systems - a review[J]. Marine Technology Society Journal, 1986(20): 4-16.
[60]	YE MENGYING, YANG CHANGBAO, ZHANG XUQING, et al, 2024. Shallow water bathymetry inversion based on machine learning using ICESat-2 and sentinel-2 data[J]. Remote Sensing, 16(23): 4603.
[61]	ZHANG XIAOHAN, CHEN YIFU, LE YUAN, et al, 2022. Nearshore bathymetry based on ICESat-2 and multispectral images: comparison between sentinel-2, landsat-8, and testing Gaofen-2[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 2449-2462.

年份	航道宽度/m	航道面积/km²	平均水深/m	陆地面积/km²
2022	84.25	0.02	14.77	0.17
2023	281.92	0.07	10.48	0.30 (5月)、0.96 (10月)
2024	299.98	0.09	11.37	1.55 (1月)、1.94 (6月)

经过时间	台风名称	最大风速/(m·s^-1)	气压/hPa	移动方向
2018.11.22	天兔(Usagi)	30	1004	西西南
2021.12.18	雷伊(Rai)	52	935	西西北

年份	沙滩区域面积/km²	沙滩区域面积变化率/%	礁坪区域面积/km²	礁坪区域面积变化率/%	礁前斜坡区域面积/km²	礁前斜坡区域面积变化率/%
2018	0.091		1.189		0.480
2018	0.091	1.9	1.189	-1.0	0.480	-4.2
2019	0.090	1.9	1.201	-1.0	0.501	-4.2
2019	0.090	-15.0	1.201	2.4	0.501	4.4
2020	0.103	-15.0	1.172	2.4	0.479	4.4
2020	0.103	-5.8	1.172	0.3	0.479	2.2
2021	0.109	-5.8	1.169	0.3	0.468	2.2
2021	0.109	23.1	1.169	-2.5	0.468	4.7
2022	0.084	23.1	1.197	-2.5	0.446	4.7
2022	0.084	-27.9	1.197	2.4	0.446	-8.4
2023	0.107	-27.9	1.169	2.4	0.483	-8.4
2023	0.107	-4.5	1.169	1.5	0.483	-1.9
2024	0.112	-4.5	1.151	1.5	0.493	-1.9

Study on remote sensing monitoring and time series change of shallow sea topography of typical islands and reefs in the South China Sea^*

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Study on remote sensing monitoring and time series change of shallow sea topography of typical islands and reefs in the South China Sea*

RichHTML

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Abstract

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Figures/Tables 17

References 61

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Study on remote sensing monitoring and time series change of shallow sea topography of typical islands and reefs in the South China Sea^*