文献类型：期刊文献

英文题名：Multi-scale estimation of photosynthetic capacity in larch forests using UAV hyperspectral data: From leaf to canopy

作者：Wu, Chunyan[1,2] Yang, Tingdong[3] Cheng, Min[4] Chen, Dongsheng[1] Sun, Xiaomei[1] Zhang, Shougong[1]

第一作者：吴春燕;Wu, Chunyan

通信作者：Sun, XM[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, State Key Lab Tree Genet & Breeding, Key Lab Tree Breeding & Cultivat State Forestry &, Beijing, Peoples R China;[2]Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Shaanxi, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing, Peoples R China;[4]Zhejiang Univ Finance & Econ, Inst Land & Urban Rural Dev, Hangzhou 310018, Peoples R China

年份：2025

卷号：11

外文期刊名：SCIENCE OF REMOTE SENSING

收录：Scopus(收录号：2-s2.0-105000241176);WOS:【ESCI(收录号:WOS:001467358600001)】；

基金：This work is supported by the National Key Research and Development Program of China (2023YFD2200801) , the Fundamental Research Funds of CAF (CAFYBB2023QA001) , State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A & F University, Yangling 712100 Shaanxi, P.R. China (F2010121002-202429) , the Open Fund of Hubei Key Laboratory of Regional Ecological Process and Environmental Evolution (REEC-OF-202405) , National Natural Science Foundation of China (General Program) (32371862) . We appreciate the comments and suggestions given by all anonymous reviewers of this study.

语种：英文

外文关键词：UAV hyperspectral imaging; LiDAR data fusion; Photosynthetic capacity estimation; Leaf-to-canopy scaling; Coniferous forests; Cross-site model generalizability

摘要：Understanding forest photosynthetic capacity is essential for monitoring carbon dynamics under global change. UAV-based imaging spectroscopy is a powerful tool for assessing canopy leaf traits, but the extension of spectraltrait relationships to the canopy scale remains unclear. This study uses UAV-based hyperspectral imaging data to evaluate the photosynthetic characteristics of larch forests across different climate zones in China. We investigate UAV-derived imaging spectroscopy for mapping canopy-level leaf physiological traits, including chlorophyll content, leaf nitrogen, and photosynthetic capacity (Vc, max and Jmax) across three distinct climate zones. Highresolution UAV imaging spectral data and ground-based leaf trait measurements, including biochemical (chlorophyll, leaf nitrogen), morphological (leaf mass per area, LMA), and physiological traits (Vc, max and Jmax), were collected from 150 tree crowns at all sites. We developed and validated models for estimating physiological traits from canopy spectra using Partial Least Squares Regression (PLSR), focusing on the transferability of leaflevel models to the canopy scale. The results show that UAV-based canopy spectra can effectively estimate canopy-level Vc, max25 (R2 = 0.56, RMSE = 9.57 mu mol CO2 m- 2 s-1, nRMSE = 17.7 %) and Jmax25 (R2 = 0.38, RMSE = 34.8, nRMSE = 18.6 %). Additionally, other leaf traits across all climate zones were accurately predicted, including leaf mass per area (LMA), leaf water content (LWC), chlorophyll content (Chl), nitrogen content (Narea), and phosphorus content (Parea), with R2 values ranging from 0.30 to 0.44 and nRMSE between 18.8 % and 24.4 %. Significant differences in canopy trait variability were observed, with Vc, max25 and Jmax25 values driven by climate variability. The range of Vc, max25 (40.5-70.6 mu mol CO2 m- 2 s-1) and Jmax25 (80.6-120.4 mu mol CO2 m- 2 s- 1) was wider at the ES site compared to the FS and TS sites, indicating that species differences have a greater impact on photosynthetic capacity. These models demonstrated good transferability, showing robust performance across forests in different climate zones with only slight differences in predictive accuracy. However, canopy structure significantly influenced spectral-trait relationships, particularly for Vc, max and Jmax. While canopy structure had a moderate impact on accuracy, canopy-scale models performed slightly lower than leaf-level models in some cases. This study offers new insights into UAV-based imaging spectroscopy for mapping canopy leaf physiological traits and emphasizes the need to understand different physiological mechanisms at the canopy scale when expanding spectral-trait relationships.