Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Berger, Katja; Atzberger, Clement; Danner, Martin; D’Urso, Guido; Mauser, Wolfram; Vuolo, Francesco; Hank, Tobias

doi:10.3390/rs10010085

Cited by 228 publications

(190 citation statements)

References 167 publications

(227 reference statements)

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“…Turbid medium models are best suited for dense canopies with small vegetation elements, for example, grasses, agricultural crops, and forests. A widely used model in this category is the PROSAIL model (Berger et al, ; Jacquemoud et al, ), which combines the PROSPECT leaf optical properties model (Jacquemoud & Baret, ) and the Scattering by Arbitrarily Inclined Leaves canopy bidirectional reflectance model (Verhoef, ). In geometric optical models, the canopy architecture is described with different geometric objects (e.g., cones, spheroids, ellipsoids, and cubes), according to a given distribution and optical properties (J. Chen et al, ; J. M. Chen & Leblanc, ; X. Li & Strahler, ).…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

551

252

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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

551

252

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“…The RTM approach suffers from ill-posed problems and high computational cost. Furthermore, the accuracy of the RTM inversion results are highly reliant on the realism of the RTM simulation and appropriate RTM parameter initialization [8,17,18].…”

Section: Introductionmentioning

confidence: 99%

Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models

Wang

Chang

et al. 2019

Remote Sensing

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A in situ hyperspectral dataset containing multiple growth stages over multiple growing seasons was used to build paddy rice leaf area index (LAI) estimation models with a special focus on the effects of paddy rice growth stage development. The univariate regression method applied to the vegetation index (VI), the traditional multivariate calibration method of partial least squares regression (PLSR), and modern machine learning methods such as support vector regression (SVR), random forests (RF), and artificial neural networks (ANN) based on the original and first-derivative hyperspectral data were evaluated in this study for paddy rice LAI estimation. All the models were built on the whole growing season and on each separate vegetative, reproductive and ripening growth stage of paddy rice separately. To ensure a fair comparison, the models of the whole growing season were also validated on data for each separate growth stage of the standalone validation dataset. Moreover, the optimal band pairs for calculating narrowband difference vegetative index (DVI), normalized difference vegetation index (NDVI) and simple ratio vegetation index (SR) were determined for the whole growing season and for each separate growth stage separately. The results showed that for both the whole growing season and for each single growth stage, the red-edge and near-infrared band pairs are optimal for formulating the narrowband DVI, NDVI and SR. Among the four multivariate calibration methods, SVR and RF yielded more accurate results than the other two methods. The SVR and RF models built on first-derivative spectra provided more accurate results than the corresponding models on the original spectra for both whole growing season models and separate growth stage models. Comparing the prediction accuracy based on the whole growing season revealed that the RF and SVR models showed an advantage over the VI models. However, comparing the prediction accuracy based on each growth stage separately showed that the VI models provided more accurate results for the vegetative growth stages. The SVR and RF models provided more accurate results for the ripening growth stage. However, the whole growing season RF model on first-derivative spectra could provide reasonable accuracy for each single growth stage.

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“…We use a realistic representation of global leaf trait variability provided by the TRY to optimize a vegetation radiative transfer model (PROSAIL), commonly used by the remote sensing community [41]. Instead of using the common lookup tables available in the literature to parametrize the model [19,21,42], we exploit the potential of the TRY database to infer the distributions and correlations among some key leaf traits (leaf chlorophyll C ab , leaf dry matter C dm and water C w contents) required by PROSAIL.…”

Section: Global Plant Traitsmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Cited by 228 publications

References 167 publications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Effects of Growth Stage Development on Paddy Rice Leaf Area Index Prediction Models

Global Estimation of Biophysical Variables from Google Earth Engine Platform

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