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

Abstract

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, Vc,max and maximum electron transport rate, Jmax). 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 (Vc,max25 and Jmax25) 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.more » 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 Vc,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.« less

Authors:
 [1];  [2];  [3]; ORCiD logo [4];  [5];  [6];  [7];  [8];  [1];  [1];  [1];  [1];  [1];  [9];  [1];  [10];  [11];  [8];  [4];  [1]
+ Show Author Affiliations
  1. University of Hong Kong, Pokfulam (Hong Kong)
  2. University of Hong Kong, Pokfulam (Hong Kong); Chinese Academy of Sciences (CAS), Beijing (China)
  3. Guangdong Academy of Sciences, Guangzhou (China)
  4. Brookhaven National Lab. (BNL), Upton, NY (United States)
  5. Leiden University (Netherlands)
  6. Chinese Academy of Sciences (CAS), Beijing (China); University of Chinese Academy of Sciences, Beijing (China)
  7. Seoul National University (Korea, Republic of)
  8. University of Chinese Academy of Sciences, Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China)
  9. Chinese Academy of Sciences (CAS), Menglun (China)
  10. Chinese Academy of Sciences (CAS), Guangzhou (China)
  11. University of Virginia, Charlottesville, VA (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
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
OSTI Identifier:
1974176
Report Number(s):
BNL-224282-2023-JAAM
Journal ID: ISSN 0034-4257
Grant/Contract Number:  
SC0012704; 31922090; 27306020; 17305321; AoE/E-603/18; 202011159154; 2020GDASYL-20200102001; 80GSFC22TA016; 2022003640002
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 293; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; photosynthetic capacity; imaging spectroscopy; carbon science; individual tree crown; leaf to canopy scaling; canopy radiative transfer model; plant functional traits

Citation Formats

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, and Wu, Jin. Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy. United States: N. p., 2023. Web. doi:10.1016/j.rse.2023.113612.
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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. Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy. United States. https://doi.org/10.1016/j.rse.2023.113612
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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, and Wu, Jin. Fri . "Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy". United States. https://doi.org/10.1016/j.rse.2023.113612.
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@article{osti_1974176,
title = {Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy},
author = {Liu, Shuwen and Yan, Zhengbing and Wang, Zhihui and Serbin, Shawn and Visser, Marco and Zeng, Yuan and Ryu, Youngryel and Su, Yanjun and Guo, Zhengfei and Song, Guangqin and Wu, Qianhan and Zhang, He and Cheng, K. H. and Dong, Jinlong and Hau, Billy Chi Hang and Zhao, Ping and Yang, Xi and Liu, Lingli and Rogers, Alistair and Wu, Jin},
abstractNote = {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, Vc,max and maximum electron transport rate, Jmax). 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 (Vc,max25 and Jmax25) 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 Vc,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.},
doi = {10.1016/j.rse.2023.113612},
journal = {Remote Sensing of Environment},
number = ,
volume = 293,
place = {United States},
year = {Fri May 05 00:00:00 EDT 2023},
month = {Fri May 05 00:00:00 EDT 2023}
}
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Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging
journal, April 2021

  • Abdelbaki, Asmaa; Schlerf, Martin; Retzlaff, Rebecca
  • Remote Sensing, Vol. 13, Issue 9
  • DOI: 10.3390/rs13091748

A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species
journal, June 1980

  • Farquhar, G. D.; von Caemmerer, S.; Berry, J. A.
  • Planta, Vol. 149, Issue 1
  • DOI: 10.1007/BF00386231

Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function
journal, May 2018

  • Schweiger, Anna K.; Cavender-Bares, Jeannine; Townsend, Philip A.
  • Nature Ecology & Evolution, Vol. 2, Issue 6
  • DOI: 10.1038/s41559-018-0551-1

Retrieving structural and chemical properties of individual tree crowns in a highly diverse tropical forest with 3D radiative transfer modeling and imaging spectroscopy
journal, June 2018

  • Ferreira, Matheus Pinheiro; Féret, Jean-Baptiste; Grau, Eloi
  • Remote Sensing of Environment, Vol. 211
  • DOI: 10.1016/j.rse.2018.04.023

Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?
journal, February 2022

The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model
journal, February 2018

  • Ricciuto, Daniel; Sargsyan, Khachik; Thornton, Peter
  • Journal of Advances in Modeling Earth Systems, Vol. 10, Issue 2
  • DOI: 10.1002/2017MS000962

Spectroscopy of canopy chemicals in humid tropical forests
journal, December 2011

  • Asner, Gregory P.; Martin, Roberta E.; Knapp, David E.
  • Remote Sensing of Environment, Vol. 115, Issue 12
  • DOI: 10.1016/j.rse.2011.08.020

Mapping functional diversity from remotely sensed morphological and physiological forest traits
journal, November 2017

What is global photosynthesis? History, uncertainties and opportunities
journal, March 2019

  • Ryu, Youngryel; Berry, Joseph A.; Baldocchi, Dennis D.
  • Remote Sensing of Environment, Vol. 223
  • DOI: 10.1016/j.rse.2019.01.016

Mapping landscape canopy nitrogen content from space using PRISMA data
journal, August 2021

  • Verrelst, Jochem; Rivera-Caicedo, Juan Pablo; Reyes-Muñoz, Pablo
  • ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 178
  • DOI: 10.1016/j.isprsjprs.2021.06.017
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