Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Liu, Shuwen; Yan, Zhengbing; Wang, Zhihui; Serbin, Shawn; Visser, Marco; Zeng, Yuan; Ryu, Youngryel; Su, Yanjun; Guo, Zhengfei; Song, Guangqin; Wu, Qianhan; Zhang, He; Cheng, K. H.; Dong, Jinlong; Hau, Billy Chi Hang; Zhao, Ping; Yang, Xi; Liu, Lingli; Rogers, Alistair; Wu, Jin

doi:10.1016/j.rse.2023.113612

Title: Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Journal Article · Fri May 05 00:00:00 EDT 2023 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2023.113612· OSTI ID:1974176

Liu, Shuwen ^[1]; Yan, Zhengbing ^[2]; Wang, Zhihui ^[3];

^[4]; Visser, Marco ^[5]; Zeng, Yuan ^[6]; Ryu, Youngryel ^[7]; Su, Yanjun ^[8]; Guo, Zhengfei ^[1]; Song, Guangqin ^[1]; Wu, Qianhan ^[1]; Zhang, He ^[1]; Cheng, K. H. ^[1]; Dong, Jinlong ^[9]; Hau, Billy Chi Hang ^[1]; Zhao, Ping ^[10]; Yang, Xi ^[11]; Liu, Lingli ^[8]; Rogers, Alistair ^[4]; Wu, Jin ^[1]

University of Hong Kong, Pokfulam (Hong Kong)
University of Hong Kong, Pokfulam (Hong Kong); Chinese Academy of Sciences (CAS), Beijing (China)
Guangdong Academy of Sciences, Guangzhou (China)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Leiden University (Netherlands)
Chinese Academy of Sciences (CAS), Beijing (China); University of Chinese Academy of Sciences, Beijing (China)
Seoul National University (Korea, Republic of)
University of Chinese Academy of Sciences, Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China)
Chinese Academy of Sciences (CAS), Menglun (China)
Chinese Academy of Sciences (CAS), Guangzhou (China)
University of Virginia, Charlottesville, VA (United States)

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V_c,max and maximum electron transport rate, J_max). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V_c,max25 and J_max25) traits (n=135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. Here we further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V_c,max (R²=0.55, nRMSE=11.79%), Jmax (R²=0.54, nRMSE=12.34%), and five additional foliar traits (R²=0.38-0.60, nRMSE=10.11-13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectratrait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Natural Science Foundation of China (NSFC); Hong Kong Research Grant Council; HKU Seed Funding; Guangdong Academy of Sciences (GDAS); National Aeronautics and Space Administration (NASA); Ministry of Environment of Korea

Grant/Contract Number:: SC0012704; 31922090; 27306020; 17305321; AoE/E-603/18; 202011159154; 2020GDASYL-20200102001; 80GSFC22TA016; 2022003640002

OSTI ID:: 1974176

Report Number(s):: BNL-224282-2023-JAAM

Journal Information:: Remote Sensing of Environment, Vol. 293; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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