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Song G, Wang Q, Jin J. Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information. JOURNAL OF PLANT PHYSIOLOGY 2022; 279:153831. [PMID: 36252398 DOI: 10.1016/j.jplph.2022.153831] [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: 06/20/2021] [Revised: 09/09/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R2 = 0.45-0.84) and years (R2 = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.
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Affiliation(s)
- Guangman Song
- Graduate School of Science and Technology, Shizuoka University, Shizuoka, 422-8529, Japan
| | - Quan Wang
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan.
| | - Jia Jin
- Faculty of Agriculture, Shizuoka University, Shizuoka, 422-8529, Japan
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Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3157-3172. [PMID: 35218184 PMCID: PMC9126737 DOI: 10.1093/jxb/erac077] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/22/2023]
Abstract
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.
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Affiliation(s)
- Peng Fu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher M Montes
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Matthew H Siebers
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Nuria Gomez-Casanovas
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Justin M McGrath
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Elizabeth A Ainsworth
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Carl J Bernacchi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Correspondence:
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Proximal Remote Sensing-Based Vegetation Indices for Monitoring Mango Tree Stem Sap Flux Density. REMOTE SENSING 2022. [DOI: 10.3390/rs14061483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Plant water use is an important function reflecting vegetation physiological status and affects plant growth, productivity, and crop/fruit quality. Although hyperspectral vegetation indices have recently been proposed to assess plant water use, limited sample sizes for established models greatly astricts their wide applications. In this study, we have managed to gather a large volume of continuous measurements of canopy spectra through proximally set spectroradiometers over the canopy, enabling us to investigate the feasibility of using continuous narrow-band indices to trace canopy-scale water use indicated by the stem sap flux density measured with sap flow sensors. The results proved that the newly developed D (520, 560) index was optimal to capture the variation of sap flux density under clear sky conditions (R2 = 0.53), while the best index identified for non-clear sky conditions was the D (530, 575) (R2 = 0.32). Furthermore, the bands used in these indices agreed with the reported sensitive bands for estimating leaf stomatal conductance which has a critical role in transpiration rate regulation over a short time period. Our results should point a way towards using proximal hyperspectral indices to trace tree water use directly.
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