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Haynes RS, Lucieer A, Brodribb TJ, Tonet V, Cimoli E. Predicting key water stress indicators of Eucalyptus viminalis and Callitris rhomboidea using high-resolution visible to short-wave infrared spectroscopy. PLANT, CELL & ENVIRONMENT 2024. [PMID: 39119823 DOI: 10.1111/pce.15083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/18/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024]
Abstract
Drought is one of the main factors contributing to tree mortality worldwide and drought events are set to become more frequent and intense in the face of a changing climate. Quantifying water stress of forests is crucial in predicting and understanding their vulnerability to drought-induced mortality. Here, we explore the use of high-resolution spectroscopy in predicting water stress indicators of two native Australian tree species, Callitris rhomboidea and Eucalyptus viminalis. Specific spectral features and indices derived from leaf-level spectroscopy were assessed as potential proxies to predict leaf water potential (Ψleaf), equivalent water thickness (EWT) and fuel moisture content (FMC) in a dedicated laboratory experiment. New spectral indices were identified that enabled very high confidence linear prediction of Ψleaf for both species (R2 > 0.85) with predictive capacity increasing when accounting for a breakpoint in the relationships using segmented regression (E. viminalis, R2 > 0.89; C. rhomboidea, R2 > 0.87). EWT and FMC were also linearly predicted to a high accuracy (E. viminalis, R2 > 0.90; C. rhomboidea, R2 > 0.80). This study highlights the potential of spectroscopy as a tool for predicting measures of plant water noninvasively, enabling broader applications for monitoring and managing plant water stress.
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Affiliation(s)
- Ryan S Haynes
- School of Geography, Planning and Spatial Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
| | - Arko Lucieer
- School of Geography, Planning and Spatial Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
| | - Timothy J Brodribb
- School of Biological Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
| | - Vanessa Tonet
- School of Forestry & Environmental Studies, Yale University, New Haven, Connecticut, USA
| | - Emiliano Cimoli
- School of Geography, Planning and Spatial Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
- Insitute of Marine and Antarctic Studies (IMAS), University of Tasmania, Battery Point, Tasmania, Australia
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Ji F, Li F, Hao D, Shiklomanov AN, Yang X, Townsend PA, Dashti H, Nakaji T, Kovach KR, Liu H, Luo M, Chen M. Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset. THE NEW PHYTOLOGIST 2024; 243:111-131. [PMID: 38708434 DOI: 10.1111/nph.19807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/07/2024] [Indexed: 05/07/2024]
Abstract
Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.
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Affiliation(s)
- Fujiang Ji
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Fa Li
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Dalei Hao
- Atmospheric, Climate, & Earth Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, USA
| | - Alexey N Shiklomanov
- NASA Goddard Space Flight Center, 8800 Greenbelt Road, Mail code: 610.1, Greenbelt, MD, 20771, USA
| | - Xi Yang
- Department of Environmental Sciences, University of Virginia, 291 McCormick Road, Charlottesville, VA, 22904, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Hamid Dashti
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Tatsuro Nakaji
- Uryu Experimental Forest, Hokkaido University, Moshiri, Horokanai, Hokkaido, 074-0741, Japan
| | - Kyle R Kovach
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Haoran Liu
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Meng Luo
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Min Chen
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
- Data Science Institute, University of Wisconsin-Madison, 447 Lorch Ct, Madison, 53706, WI, USA
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Corrigendum to Predicting leaf traits across functional groups using reflectance spectroscopy. THE NEW PHYTOLOGIST 2024; 241:2300-2303. [PMID: 38240349 DOI: 10.1111/nph.19535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
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Zhang H, Plett JM, Catunda KLM, Churchill AC, Moore BD, Powell JR, Power SA, Yang J, Anderson IC. Rapid quantification of biological nitrogen fixation using optical spectroscopy. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:760-771. [PMID: 37891011 DOI: 10.1093/jxb/erad426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/27/2023] [Indexed: 10/29/2023]
Abstract
Biological nitrogen fixation (BNF) provides a globally important input of nitrogen (N); its quantification is critical but technically challenging. Leaf reflectance spectroscopy offers a more rapid approach than traditional techniques to measure plant N concentration ([N]) and isotopes (δ15N). Here we present a novel method for rapidly and inexpensively quantifying BNF using optical spectroscopy. We measured plant [N], δ15N, and the amount of N derived from atmospheric fixation (Ndfa) following the standard traditional methodology using isotope ratio mass spectrometry (IRMS) from tissues grown under controlled conditions and taken from field experiments. Using the same tissues, we predicted the same three parameters using optical spectroscopy. By comparing the optical spectroscopy-derived results with traditional measurements (i.e. IRMS), the amount of Ndfa predicted by optical spectroscopy was highly comparable to IRMS-based quantification, with R2 being 0.90 (slope=0.90) and 0.94 (slope=1.02) (root mean square error for predicting legume δ15N was 0.38 and 0.43) for legumes grown in glasshouse and field, respectively. This novel application of optical spectroscopy facilitates BNF studies because it is rapid, scalable, low cost, and complementary to existing technologies. Moreover, the proposed method successfully captures the dynamic response of BNF to climate changes such as warming and drought.
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Affiliation(s)
- Haiyang Zhang
- College of Life Sciences, Hebei University, Baoding, China
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Jonathan M Plett
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Karen L M Catunda
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Amber C Churchill
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
- Department of Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Ave., St Paul, MN 55108, USA
| | - Ben D Moore
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Jeff R Powell
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Sally A Power
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Jinyan Yang
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Ian C Anderson
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
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Cushman KC, Albert LP, Norby RJ, Saatchi S. Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue. THE NEW PHYTOLOGIST 2023; 240:1707-1711. [PMID: 37915249 DOI: 10.1111/nph.19237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 11/03/2023]
Abstract
This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.
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Affiliation(s)
- K C Cushman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Loren P Albert
- College of Forestry, Oregon State University, Corvallis, OR, 97331, USA
| | - Richard J Norby
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
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