文献类型：期刊文献

英文题名：Modeling strategies and influencing factors in retrieving canopy equivalent water thickness of mangrove forest with Sentinel-2 image

作者：Miao, Jing[1,2,3,4] Wang, Junjie[1,2,3,5] Zhao, Demei[1,2,3,4] Shen, Zhen[1,2,3,4] Xiang, Haoli[1,3,4] Gao, Changjun[6] Li, Wei[7] Cui, Lijuan[7] Wu, Guofeng[1,2,3,4]

第一作者：Miao, Jing

通信作者：Wang, JJ[1];Wang, JJ[2];Wang, JJ[3];Wang, JJ[4]

机构：[1]Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China;[2]Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China;[3]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China;[4]Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China;[5]Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China;[6]Guangdong Acad Forestry, Guangdong Prov Key Lab Silviculture Protect & Util, Guangzhou 510520, Peoples R China;[7]Chinese Acad Forestry, Inst Wetland Res, Beijing 100091, Peoples R China

年份：2024

卷号：158

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20240315400366);Scopus(收录号：2-s2.0-85182449981);WOS:【SCI-EXPANDED(收录号:WOS:001146785500001)】；

基金：Acknowledgments This work was supported by Shenzhen Science and Technology Program (JCYJ20210324093210029) .

语种：英文

外文关键词：Mangrove forest; Canopy EWT; Machine learning model; Radiative transfer model; Sentinel-2 image

摘要：Canopy equivalent water thickness (EWT) is an essential indicator of plant water status related to plant growth, temperature maintenance and transpiration. To our knowledge, this study was the first to map the mangrove canopy EWT using Sentinel-2 image at the reserve scale, aiming to explore three modeling strategies in retrieving mangrove canopy EWT, including machine learning models (Random Forest Regression (RFR) and Adaptive Boosting (Adaboost)), radiative transfer models (RTMs, PROSAIL-D and biophysical processor in Sentinel application platform) and hybrid models (PROSAIL-D + RFR and PROSAIL-D + Adaboost). We further investigated the impacts of four ecogeographical factors (species distribution, slope, elevation and distance to dam) on the spatial distribution of canopy EWT using Geodetector method. The results showed that Adaboost (R2 = 0.593, RMSE = 0.771 kg/m2 and RPIQ = 1.820), PROSAIL-D (R2 = 0.451, RMSE = 0.937 kg/m2 and RPIQ = 1.537) and PROSAIL-D + Adaboost (R2 = 0.501, RMSE = 0.890 kg/m2 and RPIQ = 0.887) was the optimal machine learning, RTM and hybrid model in retrieving mangrove canopy EWT, respectively. The red-edge3, NIR and SWIR bands of Sentinel-2 imagery were sensitive to canopy EWT. Moreover, Geodetector analysis demonstrated that species distribution had the most significant impact on canopy EWT, with the interaction between species and distance to dams contributing the most to the spatial difference of canopy EWT. In conclusion, this study suggests that Adaboost model and the hybrid method of PROSAIL-D and Adaboost were promising to map largescale canopy water conditions in mangrove ecosystems using Sentinel-2 imagery.