[1] JIA Mingming. Remote sensing analysis of China’s mangrove forests dynamics during 1973 to 2013. 长春：中国科学院研究生院（东北地理与农业生态研究所）(2014).

[2] LIN Peng. A review on the mangrove research in China. Journal of Xiamen University（Natural Science）, 40, 592-603(2001).

[3] LI Xia, YE Jiaan, WANG Shugong et al. Estimating mangrove wetland biomass using radar remote sensing. National Remote Sensing Bulletin, 10, 387-396(2006).

[4] XIAO Weishan, WANG Xiaoqin, LING Feilong. The application of ALOS PALSAR data on mangrove forest extraction. Remote Sensing Technology and Application, 25, 91-96(2010).

[5] LU Chunyan, LEI Yifan, SU Ying et al. Remote sensing analysis of wetland dynamics based on object-oriented and deep learning in the low-elevation coastal zone of southeast Fujian. Remote Sensing Technology and Application, 36, 713-727(2021).

[6] XU Yi, ZHEN Jianing, JIANG Xiapeng et al. Mangrove species classification with UAV-based remote sensing data and XGBoost. National Remote Sensing Bulletin, 25, 737-752(2021).

[7] GIRI C, DUKE N. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20, 154-159(2011).

[8] ZHOU Shuling, XU Hanqiu. Study on dynamics of costal protecting forest in Quanzhou Bay based on decision tree classification. Remote Sensing Technology and Application, 26, 619-626(2011).

[9] THAKUR S, MONDAL I et al. Shoreline changes and its impact on the mangrove ecosystems of some islands of Indian Sundarbans， North-East coast of India. Journal of Cleaner Production, 284, 124764(2021).

[10] WANG X X, XIAO X M, ZOU Z H et al. Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 163, 312-326(2020).

[11] MASENYAMA A, SIBANDA M. Spectral saturation in the remote sensing of high-density vegetation traits： A systematic review of progress， challenges， and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 297-309(2023).

[12] BALOLOY A B, BLANCO A C. ANA R R C，. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 95-117(2020).

[13] RODRIGUES F H, CERRI R I, DE ANDRADE KOLYA A et al. Comparison of vegetation indices and image classification methods for mangrove mapping at semi-detailed scale in southwest of Rio de Janeiro， Brazil. Remote Sensing Applications： Society and Environment, 30, 100965(2023).

[14] TRAN T V, REEF R, ZHU X. A review of spectral indices for mangrove Remote Sensing. Remote Sensing, 14, 4868(2022).

[15] XUE Chaohui, QIAN Siyu. Fusion of Landsat-8 and Sentinel-2 data for mangrove phenology information extraction and classification. National Remote Sensing Bulletin, 26, 1121-1142(2022).

[16] ZHANG Xuehong. Decision tree algorithm of automatically extracting mangrove forests information from Landsat-8 OLI imagery. Remote Sensing for Land and Resources, 28, 182-187(2016).

[17] ALJAHDALI M O, MUNAWAR S, KHAN W R. Monitoring mangrove forest degradation and regeneration： Landsat time series analysis of moisture and vegetation indices at Rabigh Lagoon，Red Sea. Forests, 12, 52(2021).

[18] SHI T, LIU J, HU Z et al. New spectral metrics for mangrove forest identification. Remote Sensing Letters, 7, 885-894(2016).

[19] QI Yali, FU Dongyang, LIU Dazhao et al. Erosion-accretion analysis of bare flats outside the mangrove area in Yingluo Bay， southwestern South China Sea. Marine Sciences, 43, 6-14(2019).

[21] WANG G, GUAN D, XIAO L et al. Changes in mangrove community structures affecting sediment carbon content in Yingluo Bay of South China. Marine Pollution Bulletin, 149, 110581(2019).

[22] BUNTING P, ROSENQVIST A, LUCAS R et al. The global mangrove watch—A new 2010 global baseline of mangrove extent. Remote Sensing, 10, 1669(2018).

[23] GUPTA K, MUKHOPADHYAY A, GIRI S et al. An index for discrimination of mangroves from non-mangroves using Landsat-8 OLI imagery. MethodsX, 5, 1129-1139(2018).

[24] SUN Hongyu, WANG Changyao, NIU Zheng et al. Analysis of the vegetation cover change and the relationship between NDVI and environmental factors by using NOAA time series data. National Remote Sensing Bulletin, 2, 204-210(1998).

[25] YOUNES CÁRDENAS N, JOYCE K E, MAIER S W. Monitoring mangrove forests： Are we taking full advantage of technology. International Journal of Applied Earth Observation and Geoinformation, 63, 1-14(2017).

[26] LI Jing, SHEN Yingying, JIAO Lipeng et al. Dynamic monitoring of water areas in Yanzhou coalfield based on Landsat TM/OLI images. Transactions of the Chinese Society of Agricultural Engineering, 33, 243-250(2017).

[27] WINARSO G P A D. New mangrove index as degradation /health indicator using remote sensing data： Segara Anakan and Alas Purwo case study(2014).

[28] ZHAO C P, QIN C Z. A fine resolution mangrove map of China for 2019 derived from 10-m-resolution satellite observations and Google Earth images. Science Data Bank(2).