An extended PROSPECT: Advance in the leaf optical properties model separating total chlorophylls into chlorophyll a and b

Yao, Zhen; Huang, Jingfeng; Wang, Fumin; Blackburn, George Alan; Zhang, Hankui K.; Wang, Xiuzhen; Wei, Chuanwen; Zhang, Kangyu; Chen, Wei

doi:10.1038/s41598-017-06694-y

Cited by 48 publications

(50 citation statements)

References 29 publications

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“…3). All these results were consistent with those obtained on other datasets [46][47][48][74][75]. leaves.…”

Section: Retrieved Biochemical Parameterssupporting

confidence: 92%

“…However, the RMSE remained below or equal to 1.82 µg.cm -2 for these pigments, which still indicated good accuracy. As suggested in previous work, separating carotenoids into carotenes and xanthophylls in PROSPECT might improve estimations, but this is not yet possible in the current version of the model [42,75]. Leaf water and dry matter contents were also correctly estimated (Fig.…”

Section: Retrieved Biochemical Parametersmentioning

confidence: 61%

“…For example, the lutein content might increase when plants are exposed to TPH, but the zeaxanthin content might be altered. Consequently, separating carotenoid pigments would benefit the detection of TPHaffected vegetation, but remains difficult when using hyperspectral spectroscopy [42,75].…”

Section: Seasonal and Interspecific Variability Of Leaf Pigmentsmentioning

confidence: 99%

“…5). In that sense, making improvements to PROSPECT, including the separation of chlorophylls a and b, might help improve TPH predictions [75].…”

Section: Prediction Of Tph From Leaf Pigmentsmentioning

confidence: 99%

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Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties

Lassalle

Fabre

Crédoz

et al. 2019

Journal of Hazardous Materials

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Recent advances in hyperspectral spectroscopy suggest making use of leaf optical properties for monitoring soil contamination in oil production regions by detecting pigment alterations induced by Total Petroleum Hydrocarbons (TPH). However, this provides no quantitative information about the level of contamination. To achieve this, we propose an approach based on the inversion of the PROSPECT model. 1620 leaves from five species were collected on a site contaminated by 16 to 77 g.kg-1 of TPH over a 14-month period. Their spectral signature was measured and used in PROSPECT model inversions to retrieve leaf biochemistry. The model performed well for simulating the spectral signatures (RMSE < 2%) and for estimating leaf pigment contents (RMSE ≤ 2.95 µg.cm-2 for chlorophylls). Four out of the five species exhibited alterations in pigment contents when exposed to TPH. A strong correlation was established between leaf chlorophyll content and soil TPH concentrations (R 2 ≥ 0.74) for three of them, allowing accurate predictions of TPH (RMSE = 3.20 g.kg-1 and RPD = 5.17). The accuracy of predictions varied by season and improved after the growing period. This study demonstrates the capacity of PROSPECT to estimate oil contamination and opens up promising perspectives for larger-scale applications.

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“…3). All these results were consistent with those obtained on other datasets [46][47][48][74][75]. leaves.…”

Section: Retrieved Biochemical Parameterssupporting

confidence: 92%

Section: Retrieved Biochemical Parametersmentioning

confidence: 61%

Section: Seasonal and Interspecific Variability Of Leaf Pigmentsmentioning

confidence: 99%

“…5). In that sense, making improvements to PROSPECT, including the separation of chlorophylls a and b, might help improve TPH predictions [75].…”

Section: Prediction Of Tph From Leaf Pigmentsmentioning

confidence: 99%

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Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties

Lassalle

Fabre

Crédoz

et al. 2019

Journal of Hazardous Materials

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“…GM2 and PSSRb indices), thus confirming previous results obtained on another oil-tolerant species (Lassalle et al, 2019b). These pigment are usually of less contribution to leaf optical properties, because of the masking effect of chlorophylls, which are present at higher concentrations in leaves and share common light absorption features (Feret et al, 2008;Zhang et al, 2017). Our study shows that they also contribute to the spectral response of vegetation to oil contamination.…”

Section: Elastic Net Regressionssupporting

confidence: 90%

Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression

Lassalle

Crédoz

Hédacq

et al. 2019

Ecotoxicology and Environmental Safety

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The persistence of soil contamination after cessation of oil activities remains a major environmental issue in tropical regions. The assessment of the contamination is particularly difficult on vegetated sites, but promising advances in reflectance spectroscopy have recently emerged for this purpose. This study aimed to exploit vegetation reflectance for estimating low concentrations of Total Petroleum Hydrocarbons (TPH) in soils. A greenhouse experiment was carried out for 42 days on Cenchrus alopecuroides (L.) under realistic tropical conditions. The species was grown on oil-contaminated mud pit soils from industrial sites, with various concentrations of TPH. After 42 days, a significant decrease in plant growth and leaf chlorophyll and carotenoid contents was observed for plants exposed to 5 to 19 g.kg-1 TPH in comparison to the controls (p < 0.05). Conversely, pigment contents were higher for plants exposed to 1 g.kg-1 TPH (hormesis phenomenon). These modifications proportionally affected the reflectance of C. alopecuroides at leaf and plant scales, especially in the visible region around 550 and 700 nm. 33 vegetation indices were used for linking the biochemical and spectral responses of the species to oil using elastic net regressions. The established models indicated that chlorophylls a and b and β-carotene were the main pigments involved in the modifications of reflectance (R 2 > 0.7). The same indices also succeeded in estimating the concentrations of TPH using random forest regression, at leaf and plant scales (RMSE = 1.46 and 1.63 g.kg-1 and RPD = 5.09 and 4.44, respectively). Four out of the 33 indices contributed the most to the models (>75%). This study opens up encouraging perspectives for monitoring the cessation of oil activities in tropical regions. Further researches will focus on the application of our approach at larger scale, on airborne and satellite imagery.

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Simulating global dynamic surface reflectances for imaging spectroscopy spaceborne missions - LPJ-PROSAIL

Poulter¹,

Currey

Calle

et al. 2022

Preprint

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Imaging spectroscopy is a remote-sensing technique that retrieves reflectances across visible to shortwave infrared wavelengths at high spectral resolution (<10 nm). Spectroscopic reflectance data provide novel information on the properties of the Earth's terrestrial and aquatic surfaces. Until recently, imaging spectroscopy missions were limited spatially and temporally using airborne instruments, such as the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG), providing the main source of observations. Here, we present a land-surface modeling framework to help support end-to-end traceability of emerging imaging spectroscopy spaceborne missions. The LPJ-wsl dynamic global vegetation model is coupled with the canopy radiative transfer model, PROSAIL, to generate global, gridded, daily visible to shortwave infrared (VSWIR) spectra.LPJ-wsl variables are cross-walked to meet required PROSAIL parameters, which include leaf structure, Chlorophyll a+b, brown pigment, equivalent water thickness, and dry matter content. Simulated spectra are compared to a boreal forest site, a temperate forest, managed grassland, and a tropical forest site using reflectance data from canopy imagers mounted on towers and from air and spaceborne platforms. We find that canopy nitrogen and leaf-area index are the most uncertain variables in translating LPJ-wsl to PROSAIL parameters but at first order, LPJ-PROSAIL successfully simulates surface reflectance dynamics. Future work will optimize functional relationships required for improving PROSAIL parameters and include the development of the LPJ-model to represent improvements in leaf water content and canopy nitrogen. The LPJ-PROSAIL model can support missions such as NASA's Surface Biology and Geology (SBG) and higher-level modeled products.

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An extended PROSPECT: Advance in the leaf optical properties model separating total chlorophylls into chlorophyll a and b

Cited by 48 publications

References 29 publications

Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties

Application of PROSPECT for estimating total petroleum hydrocarbons in contaminated soils from leaf optical properties

Estimating persistent oil contamination in tropical region using vegetation indices and random forest regression

Simulating global dynamic surface reflectances for imaging spectroscopy spaceborne missions - LPJ-PROSAIL

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