Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models

Widlowski, Jean-Luc; Taberner, Malcolm; Pinty, B.; Bruniquel-Pinel, V.; Disney, Mathias; Fernandes, Richard; Gastellu-Etchegorry, Jean-Philippe; Gobron, Nadine; Kuusk, Andres; Lee, Thomas; Leblanc, Sylvain G.; Lewis, P.; Martin, Éric; Mõttus, Matti; North, Peter; Qin, Wei; Robustelli, Monica; Rochdi, Nadia; Ruiloba, Rosario; Soler, Cyril; Thompson, Richard L.; Verhoef, W.; Verstraete, M. M.; Xie, Da

doi:10.1029/2006jd007821

Cited by 219 publications

(129 citation statements)

References 66 publications

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“…When it comes to vegetation analysis, RTMs have found a wide range of applications to model, study and understand light interception by plant canopies and the interpretation of vegetation reflectance in terms of biophysical characteristics [2,10,11]. A diversity of canopy RTMs have been developed over the last three decades with varying degrees of complexity.…”

Section: Introductionmentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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Abstract:Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance (E di f , E dir ), direct and diffuse upward transmittance (T dir , T di f ), spherical albedo (S) and path radiance (L 0 ), the most accurate MLRA's were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400-2500 nm spectral range took no more than a few minutes on a contemporary computer-in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E di f and T di f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E dir and T dir atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

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Section: Introductionmentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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“…The RAdiation transfer Model Intercomparison (RAMI) initiative (http://rami-benchmark. jrc.ec.europa.eu) offers such a platform for a range of simple and very complex canopy scenarios (Pinty et al, 2001;Widlowski et al, 2007). The 1-D model implemented in JRC-TIP was found to be in very good agreement, i.e.…”

Section: Validationmentioning

confidence: 73%

Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Kaminski¹,

Pinty

Voßbeck³

et al. 2017

Biogeosciences

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Abstract. Earth observation (EO) land surface products have been demonstrated to provide a constraint on the terrestrial carbon cycle that is complementary to the record of atmospheric carbon dioxide. We present the Joint Research Centre Two-stream Inversion Package (JRC-TIP) for retrieval of variables characterising the state of the vegetation-soil system. The system provides a set of land surface variables that satisfy all requirements for assimilation into the land component of climate and numerical weather prediction models. Being based on a 1-D representation of the radiative transfer within the canopy-soil system, such as those used in the land surface components of advanced global models, the JRC-TIP products are not only physically consistent internally, but they also achieve a high degree of consistency with these global models. Furthermore, the products are provided with full uncertainty information. We describe how these uncertainties are derived in a fully traceable manner without any hidden assumptions from the input observations, which are typically broadband white sky albedo products. Our discussion of the product uncertainty ranges, including the uncertainty reduction, highlights the central role of the leaf area index, which describes the density of the canopy. We explain the generation of products aggregated to coarser spatial resolution than that of the native albedo input and describe various approaches to the validation of JRC-TIP products, including the comparison against in situ observations. We present a JRC-TIP processing system that satisfies all operational requirements and explain how it delivers stable climate data records. Since many aspects of JRC-TIP are generic, the package can serve as an example of a state-of-the-art system for retrieval of EO products, and this contribution can help the user to understand advantages and limitations of such products.

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“…SAIL has been used in many studies. It also has been validated several times by studies like the RAMI comparison (Pinty et al, 2001;Pinty et al, 2004;Widlowski et al 2007). Recently, Verhoef et al (2007a) have expanded the earlier versions of the model in order to simulate also thermal radiative transfer inside a canopy, see Eq.…”

Section: Radiative Transfer Modelsmentioning

confidence: 99%

Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements

Timmermans¹,

Verhoef

Tol³

et al. 2009

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration is usually estimated in remote sensing from single temperature value representing both soil and vegetation. This surface temperature is an aggregate over multiple canopy components. The temperature of the individual components can differ significantly, introducing errors in the evapotranspiration estimations. The temperature aggregate has a high level of directionality. An inversion method is presented in this paper to retrieve four canopy component temperatures from directional brightness temperatures. The Bayesian method uses both a priori information and sensor characteristics to solve the ill-posed inversion problem. The method is tested using two case studies: 1) a sensitivity analysis, using a large forward simulated dataset, and 2) in a reality study, using two datasets of two field campaigns. The results of the sensitivity analysis show that the Bayesian approach is able to retrieve the four component temperatures from directional brightness temperatures with good success rates using multi-directional sensors (S spectra r ≈0.3, S gonio r ≈0.3, and S AATSR r ≈0.5), and no improvement using monoangular sensors (S r ≈1). The results of the experimental study show that the approach gives good results for high LAI values (RMSE grass =0.50 K, RMSE wheat =0.29 K, RMSE sugar beet =0.75 K, RMSE barley =0.67 K); but for low LAI values the results were unsatisfactory (RMSE young maize =2.85 K). This discrepancy was found to originate from the presence of the metallic construction of the setup. As these disturbances, were only present for two Correspondence to: J. Timmermans (timmermans@itc.nl) crops and were not present in the sensitivity analysis, which had a low LAI, it is concluded that using masked thermal images will eliminate this discrepancy.

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Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models

Cited by 219 publications

References 66 publications

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates

Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements

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