Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT+SAIL)

Vohland, Michael; Jarmer, Thomas

doi:10.1080/01431160701268947

Cited by 59 publications

(63 citation statements)

References 22 publications

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“…Similar trends were found for the synthetic data set: The highest accuracies were obtained for those variables that have the largest influence on the TOC spectrum, i.e., Cab, LAI, and ALA. This is conform to what several other studies observed [22,24,60]. Cw and, especially, Cdm were retrieved with less accuracy.…”

Section: Overall Performancesupporting

confidence: 77%

“…These accuracies can be considered reasonable given the fully automated nature of the approach and the assumption of total absence of a priori knowledge on land cover and development stage. They are well within the range of accuracies obtained for hyperspectral data in studies where significantly more knowledge on crop type and canopy was a priori available and, hence, the parameterization of model inversion could be better tailored to the prevailing conditions [21,22,24,50]. The regularization proposed by CRASh in most cases provided clear improvements compared to the case where no a priori info at all is available and minimization only takes place in the radiometric domain.…”

Section: Overall Performancementioning

confidence: 49%

“…The difficulty of accurately retrieving leaf water and leaf dry matter contents from top of canopy green vegetation spectra is explained by the low sensitivity of spectral reflectance to changes in their concentrations and the only small variations usually observed for such canopies. This has already been recognized by many authors [21,22,24,51,60,61]. Remarkably, also N and BS which, according to the predictive equations show only low retrievability, were estimated with reasonable accuracy from the synthetic top of canopy reflectance spectra.…”

Section: Overall Performancementioning

confidence: 67%

“…The predictive power of VIs in estimating LAI (expressed by R 2 and RMSE) decreases with increasing canopy density. Saturation of spectral reflectance and, hence, an accurate retrieval for LAI values is a well-known phenomenon [22,24]. A reverse trend can be observed for Cw and, to a smaller degree, for Cdm.…”

Section: A Priori Estimates Of the Solutionmentioning

confidence: 92%

“…The great advantage of RTM inversion is the fact that all variables influencing top of canopy reflectance can be explicitly accounted for. Various strategies have been proposed for the inversion including numerical optimization methods [21][22][23], look-up table approaches [24][25][26] and artificial neural network methods (e.g., [27][28][29]), which all have their strengths and weaknesses in specific situations [6]. The inversion of canopy RTMs is by nature an ill-posed problem mainly for two reasons [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

Remote Sensing

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Automated, image based methods for the retrieval of vegetation biophysical and biochemical variables are often hampered by the lack of a priori knowledge about land cover and phenology, which makes the retrieval a highly underdetermined problem. This study addresses this problem by presenting a novel approach, called CRASh, for the concurrent retrieval of leaf area index, leaf chlorophyll content, leaf water content and leaf dry matter content from high resolution solar reflective earth observation data. CRASh, which is based on the inversion of the combined PROSPECT+SAILh radiative transfer model (RTM), explores the benefits of combining semi-empirical and physically based approaches. The approach exploits novel ways to address the underdetermined problem in the context of an automated retrieval from mono-temporal high resolution data. To regularize the inverse problem in the variable domain, RTM inversion is coupled with an OPEN ACCESSRemote Sens. 2009, 1 1140 automated land cover classification. Model inversion is based on a two step lookup table (LUT) approach: First, a range of possible solutions is selected from a previously calculated LUT based on the analogy between measured and simulated reflectance. The final solution is determined from this subset by minimizing the difference between the variables used to simulate the spectra contained in the reduced LUT and a first guess of the solution. This first guess of the variables is derived from predictive semi-empirical relationships between classical vegetation indices and the single variables. Additional spectral regularization is obtained by the use of hyperspectral data. Results show that estimates obtained with CRASh are significantly more accurate than those obtained with a tested conventional RTM inversion and semi-empirical approach. Accuracies obtained in this study are comparable to the results obtained by various authors for better constrained inversions that assume more a priori information. The completely automated and image-based nature of the approach facilitates its use in operational chains for upcoming high resolution airborne and spaceborne imaging spectrometers.

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Section: Overall Performancesupporting

confidence: 77%

Section: Overall Performancementioning

confidence: 49%

Section: Overall Performancementioning

confidence: 67%

Section: A Priori Estimates Of the Solutionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Dorigo

Richter

Baret³

et al. 2009

Remote Sensing

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Spectral Field Campaigns: Planning and Data Collection

Schweiger

2020

Remote Sensing of Plant Biodiversity

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This chapter focuses on planning field campaigns and data collection relevant to plant biodiversity. Particular emphasis is placed on sampling spectra of plants across scales, from the leaf to the canopy and airborne level, considering the issue of matching ecological data with spectra. The importance of planning is highlighted from the perspective of the long-term sustainability of a project, which includes using and contributing to the development of standards for project documentation and archiving. These issues are critical to biodiversity researchers involved in data collection in situ and via remote sensing (RS).

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Assessment of vegetation status of Sali River basin, a tributary of Damodar River in Bankura District, West Bengal, using satellite data

Malik

Pal

Das

et al. 2019

Environ Dev Sustain

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Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT+SAIL)

Cited by 59 publications

References 22 publications

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Enhanced Automated Canopy Characterization from Hyperspectral Data by a Novel Two Step Radiative Transfer Model Inversion Approach

Spectral Field Campaigns: Planning and Data Collection

Assessment of vegetation status of Sali River basin, a tributary of Damodar River in Bankura District, West Bengal, using satellite data

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