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

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.

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Publication Details

Journal
Remote Sensing
Published
2025-05-03
DOI
https://doi.org/10.3390/rs17091625
Citations
1
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
1.11
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article

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

Meijun Hu, Chongfan Guan, Xiangfen Cheng, Qingmei Pan et al.
1 citations
Remote Sensing
Plant Water Relations and Carbon Dynamics
1.11
article

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

Meijun Hu, Chongfan Guan, Xiangfen Cheng, Qingmei Pan, Xin Wang, Shoujia Sun, Jinsong Zhang, Xiang Gao, Zhipeng Li
article en
1 citations

Abstract

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.

Remote SensingVol. 17(9)
Nanjing Forestry University (CN), Chinese Academy of Forestry (CN), Research Institute of Forestry (CN)
Clean water and sanitation
Openalex Percentile: Top 25%
Plant Water Relations and Carbon Dynamics
1.11
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