FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation

Cooley, T.; Anderson, Gail P.; Felde, Gerald W.; Hoke, Michael L.; Ratkowski, Anthony J.; Chetwynd, James H.; Gardner, James A.; Adler‐Golden, Steven M.; Matthew, Michael W.; Berk, Alexander; Bernstein, L. S.; Acharya, Prabhat K.; Miller, David P.; Lewis, Paul E.

doi:10.1109/igarss.2002.1026134

Cited by 285 publications

(186 citation statements)

References 15 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%

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%

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

et al. 2016

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“…The pan sharpened Worldview-2 image, acquired on 9 th December 2009, was comprised of eight wavebands each with a spatial resolution of 0.5 m. Pre-processing procedures including atmospheric correction using the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) algorithm provided in the Environment for Visualising Images (TM) software (Cooley et al 2002), image masking and water column correction were applied to the satellite image to yield radiance data that could be used as input to a classification algorithm. Bathymetric information to support these corrections was provided by the Australian Hydrographic office in the form of an airborne survey flown by LADS (Laser Airborne Depths Sounder), corrected to a horizontal datum of WGS1984 and a vertical datum of Lowest Astronomical tide (Fugro, 2011).…”

Section: Image Processingmentioning

confidence: 99%

The use of remote sensing to scale up measures of carbonate production on reef systems: a comparison of hydrochemical and census-based estimation methods

Hamylton

Silverman

Shaw

2013

International Journal of Remote Sensing

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(2013). The use of remote sensing to scale up measures of carbonate production on reef systems: a comparison of hydrochemical and census-based estimation methods. International Journal of Remote Sensing, 34 (18), 6451-6465.The use of remote sensing to scale up measures of carbonate production on reef systems: a comparison of hydrochemical and census-based estimation methods AbstractThe present study uses remote-sensing imagery to estimate carbonate production of the complete One Tree Island reef system, Great Barrier Reef, using hydrochemical (alkalinity reduction) and census-based (budgetary) methods. For five sites representing different benthic cover types across the reef system, carbonate production is determined using hydrochemical techniques that incubate substrates in a local aquarium and measure total alkalinity, total ammonia nitrogen, and total oxidized nitrogen. Local estimates are scaled up to the reef-system scale using a WorldView-2 satellite image, which is ground truthed against a field data set of 350 spatially referenced records of benthic assemblage. Annual total reef system carbonate production based on hydrochemical and census-based methods is estimated at 40,335 and 38,998 tonnes of calcium carbonate (CaCO3), respectively. The minimal difference (0.3%) between these estimates is attributed to under representation of small carbonate producers, such as benthic foraminifera, which are difficult to incorporate in the underwater video methodology employed to populate census budgets. This finding demonstrates the utility of remote sensing for upscaling local measures of carbonate production across reef systems accurately and consistently in spite of the use of different initial estimation methods. AbstractThe present study uses remote sensing imagery to estimate carbonate production of the complete One

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“…Some pre-processing steps are necessary before using image. Firstly, some bad pixel value in original image were replaced by the means of two pixels value beside its two sides; then the image was radiometrically corrected using calibration coefficient; at last, the image was atmospheric corrected using FLAASH model [10]. Figure 3 and figure 4 shows different ground objects spectra before and after atmospheric correction.…”

Section: Remote Sensing Datamentioning

confidence: 99%

An Unsupervised Classification Method for Hyperspectral Remote Sensing Image Based on Spectral Data Mining

Wen¹,

Yang²

2012

Advances in Data Mining Knowledge Discovery and Applications

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FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation

Cited by 285 publications

References 15 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

The use of remote sensing to scale up measures of carbonate production on reef systems: a comparison of hydrochemical and census-based estimation methods

An Unsupervised Classification Method for Hyperspectral Remote Sensing Image Based on Spectral Data Mining

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