Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG)  toolbox (version 2.0)

Vicent, Jorge; Verrelst, Jochem; Sabater, Neus; Alonso, Luis; Rivera-Caicedo, Juan Pablo; Martino, Luca; Muñoz-Marí, Jordi; Moreno, José

doi:10.5194/gmd-13-1945-2020

Cited by 28 publications

(20 citation statements)

References 63 publications

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“…Moreover, an atmospheric RTM could be combined with PROSAIL-PRO to improve understanding of the radiative processes occurring in the Earth's atmosphere and to avoid uncertainties from atmospheric corrections (Vicent et al, 2020). Further, within hybrid retrieval workflows for precision farming applications, mapping of NNI could be introduced.…”

Section: Advantage and Limitations Of Hybrid Methodsmentioning

confidence: 99%

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Berger

Verrelst

Féret

et al. 2020

International Journal of Applied Earth Observation and Geoinfor

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103

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Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-based approach combined with machine learning regression to estimate crop N content. Within the workflow, the leaf optical properties model PROSPECT-PRO including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. The latter was then employed to generate a training database to be used for advanced probabilistic machine learning methods: a standard homoscedastic Gaussian process (GP) and a heteroscedastic GP regression that accounts for signal-to-noise relations. Both GP models have the property of providing confidence intervals for the estimates, which sets them apart from other machine learners. Moreover, a GP-based sequential backward band removal algorithm was employed to analyze the band-specific information content of PROSAIL-PRO simulated spectra for the estimation of aboveground N. Data from multiple hyperspectral field campaigns, carried out in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP), were exploited for validation. In these campaigns, corn and winter wheat spectra were acquired to simulate spectral EnMAP data. Moreover, destructive N measurements from leaves, stalks and fruits were collected separately to enable plant-organ-specific validation. The results showed that both GP models can provide accurate aboveground N simulations, with slightly better results of the heteroscedastic GP in terms of model testing and against in situ N measurements from leaves plus stalks, with root mean square error (RMSE) of 2.1 g/m². However, the inclusion of fruit N content for validation deteriorated the results, which can be explained by the inability of the radiation to penetrate the thick tissues of stalks, corn cobs and wheat ears. GP-based band analysis identified optimal spectral settings with ten bands mainly situated in the shortwave infrared (SWIR) spectral region. Use of well-known protein absorption bands from the literature showed comparative results. Finally, the heteroscedastic GP model was successfully applied on airborne hyperspectral data for N mapping. We conclude that GP algorithms, and in particular the heteroscedastic GP, should be implemented for global agricultural monitoring of aboveground N from future imaging spectroscopy data.

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Section: Advantage and Limitations Of Hybrid Methodsmentioning

confidence: 99%

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Berger

Verrelst

Féret

et al. 2020

International Journal of Applied Earth Observation and Geoinfor

Self Cite

103

View full text Add to dashboard Cite

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“…Widely used atmospheric RTMs include 6SV [27], libRadtran [28] and MODTRAN [29]. To overcome this limitation, the Atmospheric Look-up table Generator (ALG) is one of the few software packages that enables executing atmospheric RTMs with a friendly graphical user interface [30]. In addition, the few software packages available to automate the retrieval from TOA data are still experimental.…”

Section: Introductionmentioning

confidence: 99%

Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data

Estévez

Vicent

Rivera-Caicedo

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopyatmosphere radiative transfer models PROSAIL-6S were used to simulate a look-up table (LUT) of TOA radiance data and associated input variables. This LUT was then used to train the Bayesian machine learning algorithms Gaussian processes regression (GPR) and variational heteroscedastic GPR (VHGPR). PROSAIL simulations were also used to train GPR and VHGPR models for LAI retrieval from S2 images at bottom-of-atmosphere (BOA) level (L2A product) for comparison purposes. The BOA and TOA LAI products were consistently validated against a field dataset with GPR (R 2 of 0.78) and with VHGPR (R 2 of 0.80) and for both cases a slightly lower RMSE for the TOA LAI product. Because of delivering superior accuracies and lower uncertainties, VHGPR was further applied for LAI mapping using S2 acquisitions over the agricultural sites Marchfeld (Austria) and Barrax (Spain). The VHGPR models led to consistent LAI maps at BOA and TOA scale. The LAI maps were also compared against LAI maps as generated by the SNAP toolbox, which is based on a neural network (NN). Maps were again consistent, however the SNAP NN algorithm tend to overestimate over dense vegetation cover. Overall, this study demonstrated that hybrid LAI retrieval algorithms can be developed from TOA radiance data given a cloud-free sky, thus without the need of atmospheric correction. To the benefit of the community, the development of such hybrid algorithms for the retrieval vegetation properties from BOA or TOA images has been streamlined in the freely downloadable ALG-ARTMO software framework.

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“…Recent modifications of the MODTRAN codes now includes a line-by-line algorithm for high-resolution RT calculations as well as coupling to optical scattering codes for easy implementation of custom aerosols and clouds. The radiative transfer equation is solved through the use of discreet ordinates (DISORT) and combined with a statistical based simulation of the absorption and scattering effects [126]. MODTRAN is one of the more popular and well-validated methods for atmospheric correction, as it is more general purpose, has a spectral resolution of 1 cm −1 and coupled with absorption and scattering codes [7,125].…”

Section: Modtranmentioning

confidence: 99%

Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications

et al. 2021

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Atmospheric effects have a significant impact on the performance of airborne and space laser systems. Traditional methods used to predict propagation effects rely heavily on simplified assumptions of the atmospheric properties and of the interactions with the laser systems. These models need to be continually improved to develop high-resolution predictors of laser performance for applications including LIDAR (light detection and ranging), free-space optical communications, remote sensing, etc. The underlying causes of laser beam attenuation in the atmosphere are examined with particular focus on dominant linear effects: absorption, scattering, turbulence, and non-linear thermal effects such as blooming, kinetic cooling, and bleaching. These phenomena are quantitatively analyzed, highlighting the key assumptions made in the empirical modelling. Absorption and scattering, as the dominant causes of attenuation, are generally well applied in models, but the impact of non-linear phenomena is less well captured and applied as it tends to be application specific. Atmospheric radiative transfer codes, such as MODTRAN, ARTS, etc., and the associated spectral databases, such as HITRAN, are the effective implementation of the total propagative effects on the laser systems. These codes are powerful, widely used tools to analyze performance. However, atmospheric radiative transfer codes make several assumptions that reduce accuracy in favor of faster processing. The key atmospheric radiative transfer models are reviewed highlighting the associated methodologies, assumptions, and application. Empirical models are found to offer a robust analysis of atmospheric propagation, which is particularly well-suited for design, development, test and evaluation (DDT&E) purposes. As such, empirical, semi-empirical, and ensemble methodologies are suggested to compliment and augment the existing atmospheric radiative transfer codes. There is scope to evolve the numerical codes and empirical approaches to better suit aerospace applications, where fast analysis is required over a range of slant paths, incidence angles, altitudes, and atmospheric properties, which are not exhaustively captured in current quantitative performance assessment methods.

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Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)

Cited by 28 publications

References 63 publications

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method

Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data

Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications

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