文献类型：期刊文献

英文题名：Specific Responses to Environmental Factors Cause Discrepancy in the Link Between Solar-Induced Chlorophyll Fluorescence and Transpiration in Three Plantations

作者：Hu, Meijun[1,2,3] Sun, Shoujia[1,2,3] Cheng, Xiangfen[1,2,3] Pan, Qingmei[1,2,3] Zhang, Jinsong[1,2,3] Wang, Xin[1,2,3] Guan, Chongfan[1,2,3] Li, Zhipeng[1,2,3] Gao, Xiang[1,2,3]

第一作者：Hu, Meijun

通信作者：Sun, SJ[1];Sun, SJ[2];Sun, SJ[3]

机构：[1]Chinese Acad Forestry, Key Lab Tree Breeding & Cultivat, Natl Forestry & Grassland Adm, Res Inst Forestry, Beijing 100091, Peoples R China;[2]Henan Xiaolangdi Forest Ecosyst Natl Observat & Re, Jiyuan 454650, Peoples R China;[3]Nanjing Forest Univ, Collaborat Innovat Ctr Sustainable Forestry Southe, Nanjing 210037, Peoples R China

年份：2025

卷号：17

期号：9

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20252018429889);Scopus(收录号：2-s2.0-105005028364);WOS:【SCI-EXPANDED(收录号:WOS:001487587500001)】；

语种：英文

外文关键词：SIF; transpiration; environmental control; plantation; random forest

摘要：Vegetation transpiration (Tr) is crucial for the water cycle, regional water balance, and plant growth but remains challenging to estimate at large scales. Sun-induced chlorophyll fluorescence (SIF) provides a novel method for estimating Tr, but its effectiveness is limited by species specificity, requiring continuous tower-based observations for comprehensive analysis across diverse ecosystems. In this study, SIF and Tr were simultaneously monitored in Chinese cork oak (ring-porous), poplar (diffuse-porous), and arborvitae (non-porous) plantations in northern China, and the SIF-Tr relationship was further analyzed. The results showed that SIF and Tr shared similar diurnal dynamics, although Tr exhibited midday saturation. SIF and Tr were closely correlated, and the correlation strengthened as the temporal scale aggregated. Environmental factors had nonlinear impacts on SIF and Tr. Therefore, the SIF-Tr relationship deteriorated to some extent at midday, with short-term stress reducing the correlation by 0.1-0.23. Compared to the linear empirical model, the inclusion of environmental factors improved the accuracy of SIF-based Tr estimation, increasing the R2 value by 0.12 to 0.37. At the same level of accuracy, the number of environmental variables required was higher at the half-hour scale than at the daily scale. This study demonstrated the species-specific influence of environmental factors on SIF and Tr in different plantations, enhanced the understanding of the SIF-Tr relationship, and provided theoretical and data support for future large-scale Tr predictions using satellite-based SIF.