Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy

Serbin, Shawn P.; Singh, Aditya; Desai, Ankur R.; DuBois, Sean; Jablonski, A.; Kingdon, Clayton C.; Kruger, Eric L.; Townsend, Philip A.

doi:10.1016/j.rse.2015.05.024

Cited by 143 publications

(138 citation statements)

References 67 publications

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“…This study demonstrated success in the early detection and evaluation of leaf-level biochemical and physiological responses when visual symptoms of O 3 stress were minimal. The proof of concept for the use of spectral reflectance to detect phenotypic variation within a C 4 species is a significant advance over previous uses of similar techniques that have distinguished larger species-level differences in natural or agricultural ecosystems (Asner and Martin, 2008;Asner et al, 2011;Serbin et al, 2012Serbin et al, , 2014Serbin et al, , 2015.…”

Section: Discussionmentioning

confidence: 99%

“…Reflectance spectroscopy has been used to successfully distinguish broad-scale spatial and temporal variation in leaf foliar traits and photosynthetic metabolism across species in diverse agroecosystems (Serbin et al, 2015), tropical ecosystems (Asner and Martin, 2008;Asner et al, 2011), and northern temperate and boreal forests . In this study, we test the utility of leaf hyperspectral reflectance as a high-throughput method for phenotyping diverse germplasm subject to abiotic stress in maize.…”

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confidence: 99%

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High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance

et al. 2016

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High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (l = 500-2,400 nm) as a high-throughput phenotyping approach for rapid and accurate assessment of leaf photosynthetic and biochemical traits in maize (Zea mays). Leaf traits were measured with standard wet-laboratory and gas-exchange approaches alongside measurements of leaf reflectance. Partial least-squares regression was used to develop a measure of leaf chlorophyll content, nitrogen content, sucrose content, specific leaf area, maximum rate of phosphoenolpyruvate carboxylation, [CO 2 ]-saturated rate of photosynthesis, and leaf oxygen radical absorbance capacity from leaf reflectance spectra. Partial least-squares regression models accurately predicted five out of seven traits and were more accurate than previously used simple spectral indices for leaf chlorophyll, nitrogen content, and specific leaf area. Correlations among leaf traits and statistical inferences about differences among genotypes and treatments were similar for measured and modeled data. The hyperspectral reflectance approach to phenotyping was dramatically faster than traditional measurements, enabling over 1,000 rows to be phenotyped during midday hours over just 2 to 4 d, and offers a nondestructive method to accurately assess physiological and biochemical trait responses to environmental stress.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance

et al. 2016

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“…The capacity to utilize remote sensing observations to inform model parameterizations, representations, and trait‐environment relationships across spatial and temporal scales is increasing (Dahlin et al ., ; Serbin et al ., ; Schimel et al ., ; Shugart et al ., ; Singh et al ., ). Importantly, remote sensing observations can provide a synoptic view of trait variability and functional diversity across landscapes (e.g.…”

Section: Scaling Physiologymentioning

confidence: 99%

A roadmap for improving the representation of photosynthesis in Earth system models

et al. 2016

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SummaryAccurate representation of photosynthesis in terrestrial biosphere models (TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO 2 assimilation (A) to key environmental variables: light, temperature, CO 2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models.

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“…In addition, two recent studies also have shown that leaf spectroscopy provides an accurate, rapid means to assess leaf age at both individual and community scales (Chavana‐Bryant et al ., ; Wu et al ., ). Furthermore, some studies also suggest that it is possible that spectroscopy‐based models of leaf V c,max25 and age could be extended to the canopy scale by leveraging imaging spectroscopy instrumentation on tower, unmanned aerial systems and manned airborne platforms (Serbin et al ., ; De Moura et al ., ). These developments highlight the potential to map changes in V c,max25 and leaf age over unprecedented spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 98%

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

Rogers

Albert

et al. 2019

New Phytologist

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Summary Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (Vc,max) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting Vc,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of Vc,max was tested. Vc,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict Vc,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts Vc,max of mature leaves in Panamanian tropical forests (R2 = 0.90). However, this single‐age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for Vc,max and leaf age enabled construction of the Vc,max–age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in Vc,max, assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize Vc,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

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Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy

Cited by 143 publications

References 67 publications

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance

A roadmap for improving the representation of photosynthesis in Earth system models

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

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