FLEX End-to-End Mission Performance Simulator

Vicent, Jorge; Sabater, Neus; Tenjo, Carolina; Acarreta, J. R.; Manzano, M. Robles; Rivera, Juan Pablo; Jurado, Pedro Real; Franco, Raffaella; Alonso, Luis; Verrelst, Jochem; Moreno, José

doi:10.1109/tgrs.2016.2538300

Cited by 46 publications

(32 citation statements)

References 23 publications

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“…These outputs extend from the UV into the far-infrared (0.2-10,000 µm) spectral range and are provided at a resolution as fine as 0.1 cm −1 (0.01-0.1 nm in the VIS-SWIR spectral range). However, in most MODTRAN remote sensing application (e.g., [9,[64][65][66]), these outputs are decomposed in a set of key atmospheric transfer functions [63,67] that allows to invert the atmospheric TOA radiance equation. The standard formulation under the Lambertian assumption is given by:…”

Section: Modtran5mentioning

confidence: 99%

“…Forthcoming optical satellite missions increasingly make use of end-to-end mission performance simulators (E2ES) to generate simulated scenes as would be detected by the optical sensor (e.g., [9,88]). A drawback is that scene rendering can take a long processing time, especially when based on computationally expensive models such as SCOPE or MODTRAN [89].…”

Section: New Processing Opportunities With Emulatorsmentioning

confidence: 99%

“…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%

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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: Modtran5mentioning

confidence: 99%

Section: New Processing Opportunities With Emulatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…They have to be analyzed and consolidated before reaching conclusions whether emulators offer truly an attractive alternative over the original RTM. Specifically, SCOPE is currently used as baseline model in the FLEX End-to-End simulator [28] for delivering of R and SIF spectral outputs, and in the development of SIF retrieval methods [29] and subsequent interpretation towards photosynthetic activity [21,[30][31][32]. Thus, if evaluated as sufficiently accurate, an emulated version of SCOPE will greatly speed up all these processes.…”

Section: Introductionmentioning

confidence: 99%

SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra

et al. 2017

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Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside of these RTMs is that they are computationally expensive, which makes them impractical in routine processing, such as scene generation and retrieval applications. To bypass their computational burden, a computationally effective technique has been proposed by only using a limited number of model runs, called emulation. The idea of emulation is approximating the original RTM by a surrogate machine learning model with low computation time. However, a concern is whether the emulator reaches sufficient accuracy. To this end, we analyzed key aspects of emulator development that may impact the precision of emulating SCOPE-like R and SIF spectra, being: (1) type of machine learning, (2) type of dimensionality reduction (DR) method, and (3) number of components and lookup table (LUT) size. The machine learning family of Gaussian processes regression and neural networks were found best suited to function as emulators. The classical principal component analysis (PCA) remains a robust DR method, but the number of components needs to be optimized depending on the complexity of the spectral data. Based on a small Latin hypercube sampling LUT of 500 samples (70% used for training) covering a selection of SCOPE input variables, the best-performing emulators can reconstruct any combination for the selected SCOPE input variables with relative errors along the spectral range below 2% for R and 4% for SIF. That is sufficient for a precise reconstruction for the large majority of possible combinations, and errors can be further reduced when increasing LUT size for training. As a proof of concept, we imported the best-performing emulators into a newly developed Automated Scene Generator Module (A-SGM) to generate a R and SIF synthetic scene of a vegetated surface. Using emulators as alternative of SCOPE reduced the processing time from the order of days to the order of minutes while preserving sufficient accuracy.

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“…Although this opens up possibilities to implement the quality check into the processing of the FLEX/Sentinel-3 tandem mission, it requires a more in-depth inspection. Therefore, follow-up analyses are foreseen by using the FLEX End-to-End simulator [43], which has the versatility to assess the impact of all of the proposed improvements and subsequently quantify the impact on the estimated SIF.…”

Section: Apparent Reflectance Spectral Distortion Analysis and Exploimentioning

confidence: 99%

Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator

et al. 2017

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Abstract:In the last decade, significant progress has been made in estimating Solar-Induced chlorophyll Fluorescence (SIF) by passive remote sensing techniques that exploit the oxygen absorption spectral regions. Although the O 2 -B and the deep O 2 -A absorption bands present a high sensitivity to detect SIF, these regions are also largely influenced by atmospheric effects. Therefore, an accurate Atmospheric Correction (AC) process is required to measure SIF from oxygen bands. In this regard, the suitability of a two-step approach, i.e., first an AC and second a Spectral Fitting technique to disentangle SIF from reflected light, has been evaluated. One of the advantages of the two-step approach resides in the derived intermediate products provided prior to SIF estimation, such as surface apparent reflectance. Results suggest that errors introduced in the AC, e.g., related to the characterization of aerosol optical properties, are propagated into systematic residual errors in the apparent reflectance. However, of interest is that these errors can be easily detected in the oxygen bands thanks to the high spectral resolution required to measure SIF. To illustrate this, the predictive power of the apparent reflectance spectra to detect and correct inaccuracies in the aerosols characterization is assessed by using a simulated database with SCOPE and MODTRAN radiative transfer models. In 75% of cases, the aerosol optical thickness, the Angstrom coefficient and the scattering asymmetry factor are corrected with a relative error below of 0.5%, 8% and 3%, respectively. To conclude with, and in view of future SIF monitoring satellite missions such as FLEX, the analysis of the apparent reflectance can entail a valuable quality indicator to detect and correct errors in the AC prior to the SIF estimation.

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FLEX End-to-End Mission Performance Simulator

Cited by 46 publications

References 23 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

SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra

Impact of Atmospheric Inversion Effects on Solar-Induced Chlorophyll Fluorescence: Exploitation of the Apparent Reflectance as a Quality Indicator

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