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-2019-188

Cited by 6 publications

(8 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scattering and attenuation by the atmospheric constituents are wavelength dependent. While always required for quantitative analysis of MSI and HSI, atmospheric compensation is especially important when comparing scenes from different regions or collection dates as the effect of the atmosphere is inconsistent in space and time [261]. Atmospheric compensation can be accomplished using a scene-specific calibration methods such as the empirical line method (ELM) [262] or through radiative transfer models (RTMs) (see Chapter 6 of Manolakis et al [38] for a comprehensive description).…”

Section: Correction Of Passive Optical Rs Imagerymentioning

confidence: 99%

See 1 more Smart Citation

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

View full text Add to dashboard Cite

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

show abstract

Section: Correction Of Passive Optical Rs Imagerymentioning

confidence: 99%

“…A variety of RTMs are available for atmospheric correction-such as MODTRAN [264], LibRadTran [265] and 6 SV [266]-each with its own strengths and limitations. Such RTMs are often applied with specialized software tools or dedicated user interfaces [261].…”

Section: Correction Of Passive Optical Rs Imagerymentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The scattering and attenuation by the atmospheric constituents are wavelength dependent. While always required for quantitative analysis of MSI and HSI, atmospheric compensation is especially important when comparing scenes from different regions or collection dates as the effect of the atmosphere is inconsistent in space and time (Vincent et al 2019) . Atmospheric compensation can be accomplished using a scene-specific calibration called the empirical line method (ELM) (Smith and Milton 1999) or through radiative transfer models (RTMs) (see Manolakis et al 2016 for a comprehensive description).…”

Section: Correction Of Passive Optical Rs Imagerymentioning

confidence: 99%

“…RTMs are often applied with specialized software tools or dedicated user interfaces (Vincent et al 2019).…”

Section: Correction Of Passive Optical Rs Imagerymentioning

confidence: 99%

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

Rowan¹,

Kalácska²

2020

Preprint

View full text Add to dashboard Cite

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing can provide the efficient, accurate and large-scale monitoring needed to ensure proper SAV management. Our objective is to introduce remote sensing to researchers in the field of aquatic ecology. Applying remote sensing to the underwater environment is more complex in comparison to terrestrial studies due to the water column. A wide range of sensors and platforms from remotely operated vehicles to satellites are available for use in the underwater environment, a sample of which being presented herein. The utility of any sensor/platform combination varies depending on the aquatic conditions being observed. An overview of the required corrections, processing, and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detecting SAV are briefly presented and notable results and lessons are discussed.

show abstract

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

Cited by 6 publications

References 47 publications

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

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

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

Contact Info

Product

Resources

About