Simulation of Sentinel-3 images by four-stream surface–atmosphere radiative transfer modeling in the optical and thermal domains

Verhoef, W.; Bach, Heike

doi:10.1016/j.rse.2011.10.034

Cited by 73 publications

(62 citation statements)

References 11 publications

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“…RTMs are deterministic models that describe absorption and scattering, and some of them even describe the microwave region, thermal emission or sun-induced chlorophyll fluorescence emitted by vegetation (e.g., [3,4]). They are useful in a wide range of applications including (i) sensitivity analysis; (ii) developing inversion models to accurately retrieve atmospheric and vegetation properties from remotely sensed data (see [5] for a review); and (iii) to generate artificial scenes as would be observed by an optical sensor (e.g., [6,7]). Plant and atmospheric RTMs are currently used in an end-to-end simulator that functions as a virtual laboratory in the development of next-generation optical missions [8,9].…”

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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“…RTMs describe absorption and scattering, and some of them even describe sun-induced chlorophyll fluorescence, the microwave region and thermal emission. They are useful in a wide range of applications, including designing vegetation indices, performing sensitivity analyses, developing inversion models to accurately retrieve vegetation properties from remotely sensed data (see Verrelst et al [4] for a review) and to generate artificial scenes as would be observed by an optical sensor, e.g., [5]. Plant and atmospheric RTMs are currently used in an end-to-end simulator that functions as a virtual laboratory in the development of new optical sensors, for instance in preparation of EnMAP [6] or of ESA's candidate eight Earth Explorer mission, FLEX (Fluorescence Explorer) [7].…”

Section: Introductionmentioning

confidence: 99%

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

et al. 2015

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Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. When it comes to studying vegetation properties, RTMs allows us to study light interception by plant canopies and are used in the retrieval of biophysical variables through model inversion. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. Emulators are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We hereby present an "Emulator toolbox" that enables analysing multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. The toolbox is included in the free-access ARTMO's MATLAB suite for parameter retrieval and model inversion and currently contains both linear and non-linear MO-MLRAs, namely partial least squares regression (PLSR), kernel ridge regression (KRR) and neural networks (NN). These MO-MLRAs have been evaluated on their precision and speed to approximate the soil vegetation atmosphere transfer model SCOPE (Soil Canopy Observation, Photochemistry and Energy balance). SCOPE generates, amongst others, Remote Sens. 2015, 7 9348 sun-induced chlorophyll fluorescence as the output signal. KRR and NN were evaluated as capable of reconstructing fluorescence spectra with great precision. Relative errors fell below 0.5% when trained with 500 or more samples using cross-validation and principal component analysis to alleviate the underdetermination problem. Moreover, NN reconstructed fluorescence spectra about 50-times faster and KRR about 800-times faster than SCOPE. The Emulator toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together.

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“…Therefore, a careful modeling of each contribution is required to accurately calculate TOA radiance. A number of comprehensive simulation tools coupling surface and atmosphere radiative transfer processes have been introduced [18][19][20][21][22][23][24][25][26][27][28]. However, some of these studies are often simplified through various assumptions such as flat terrain, Lambertian assumption for the surface reflectance and some others are more accurate but at very high computational cost that limits their application.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, the four-stream approach proposed by Verhoef and Bach [13,26,27] is a good example of a trade-off between computational efficiency and accuracy. It integrates the Soil-Leaf-Canopy radiative transfer model SLC with the atmospheric radiative transfer model MODTRAN.…”

Section: Introductionmentioning

confidence: 99%

“…The approach was initially developed for flat terrain and it has been recently extended to account for sloped terrain as well [27]. However, the extended approach only accounts for the slope and aspect of the observed pixel, while the topographic effects caused by the adjacent features (e.g., cast shadow and terrain reflected radiance) are not taken into account.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain

et al. 2015

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Topography affects the fraction of direct and diffuse radiation received on a pixel and changes the sun-target-sensor geometry, resulting in variations in the observed radiance. Retrieval of surface-atmosphere properties from top of atmosphere radiance may need to account for topographic effects. This study investigates how such effects can be taken into account for top of atmosphere radiance modeling. In this paper, a system for top of atmosphere radiance modeling over heterogeneous non-Lambertian rugged terrain through radiative transfer modeling is presented. The paper proposes an extension of "the four-stream radiative transfer theory" , 2007) mainly aimed at representing topography-induced contributions to the top of atmosphere radiance modeling. A detailed account for BRDF effects, adjacency effects and topography effects on the radiance modeling is given, in which sky-view factor and non-Lambertian reflected radiance from adjacent slopes are modeled precisely. The paper also provides a new formulation to derive the atmospheric coefficients from MODTRAN with only two model runs, to make it more computationally efficient and also avoiding the use of zero surface albedo as used in the four-stream radiative transfer theory. The modeling begins with four surface reflectance factors calculated by the Soil-Leaf-Canopy radiative transfer model SLC at the top of canopy and propagates them through the effects of the atmosphere, which is explained by six atmospheric coefficients, derived from MODTRAN radiative OPEN ACCESSRemote Sens. 2015, 7 8020 transfer code. The top of the atmosphere radiance is then convolved with the sensor characteristics to generate sensor-like radiance. Using a composite dataset, it has been shown that neglecting sky view factor and/or terrain reflected radiance can cause uncertainty in the forward TOA radiance modeling up to 5 (mW/m 2 ·sr·nm). It has also been shown that this level of uncertainty can be translated into an over/underestimation of more than 0.5 in LAI (or 0.07 in fCover) in variable retrieval.

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Simulation of Sentinel-3 images by four-stream surface–atmosphere radiative transfer modeling in the optical and thermal domains

Cited by 73 publications

References 11 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

An Emulator Toolbox to Approximate Radiative Transfer Models with Statistical Learning

Modeling Top of Atmosphere Radiance over Heterogeneous Non-Lambertian Rugged Terrain

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