Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean

Gao, Bo‐Cai; Montes, Marcos J.; Davis, Curtiss O.; Goetz, Alexander

doi:10.1016/j.rse.2007.12.015

Cited by 325 publications

(188 citation statements)

References 39 publications

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“…These accurate but complex RTMs are frequently run in a forward mode, generating look-up tables (LUTs), which are later used during the inversion process for atmospheric compensation (Gao et al, 2009) or aerosol retrieval (Kokhanovsky, 2008;Kokhanovsky and Leeuw, 2009;Kokhanovsky et al, 2010), for instance. There are also a series of highly accurate, but computationally intensive Monte Carlo photon transport codes available.…”

Section: Introductionmentioning

confidence: 99%

Fast and simple model for atmospheric radiative transfer

2010

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Abstract. Radiative transfer models (RTMs) are of utmost importance for quantitative remote sensing, especially for compensating atmospheric perturbation. A persistent tradeoff exists between approaches that prefer accuracy at the cost of computational complexity, versus those favouring simplicity at the cost of reduced accuracy. We propose an approach in the latter category, using analytical equations, parameterizations and a correction factor to efficiently estimate the effect of molecular multiple scattering. We discuss the approximations together with an analysis of the resulting performance and accuracy. The proposed Simple Model for Atmospheric Radiative Transfer (SMART) decreases the calculation time by a factor of more than 25 in comparison to the benchmark RTM 6S on the same infrastructure. The relative difference between SMART and 6S is about 5% for spaceborne and about 10% for airborne computations of the atmospheric reflectance function. The combination of a large solar zenith angle (SZA) with high aerosol optical depth (AOD) at low wavelengths lead to relative differences of up to 15%. SMART can be used to simulate the hemispherical conical reflectance factor (HCRF) for spaceborne and airborne sensors, as well as for the retrieval of columnar AOD.

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Section: Introductionmentioning

confidence: 99%

Fast and simple model for atmospheric radiative transfer

2010

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“…The basic atmospheric effect model is well described in [10,[28][29][30][31]. We use MODerate resolution atmospheric TRANsmission version 4.1 (MODTRAN 4) [32] to estimate the radiance components in Equation (8).…”

Section: Basic Atmospheric Effect Modelingmentioning

confidence: 99%

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

et al. 2018

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Abstract:A key parameter for atmospheric correction (AC) is Aerosol Optical Depth (AOD), which is often estimated from sensor radiance (L rs,t (λ)). Noise, the dependency on surface type, viewing and illumination geometry cause uncertainty in AOD inference. We propose a method that determines pre-estimates of surface reflectance (ρ t,pre ) where effects associated with L rs,t (λ) are less influential. The method identifies pixels comprising pure materials from ρ t,pre . AOD values at the pure pixels are iteratively estimated using l 2 -norm optimization. Using the adjacency range function, the AOD is estimated at each pixel. We applied the method on Hyperspectral Mapper and Airborne Prism Experiment instruments for experiments on synthetic data and on real data. To simulate real imaging conditions, noise was added to the data. The estimation error of the AOD is minimized to 0.06-0.08 with a signal-to-reconstruction-error equal to 35 dB. We compared the proposed method with a dense dark vegetation (DDV)-based state-of-the-art method. This reference method, resulted in a larger variability in AOD estimates resulting in low signal-to-reconstruction-error between 5-10 dB. For per-pixel estimation of AOD, the performance of the reference method further degraded. We conclude that the proposed method is more precise than the DDV methods and can be extended to other AC parameters.

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“…A physically based approach for atmospheric correction is the radiative transfer modeling by e.g Modtran [54] or 6S [55]; the inversion of the radiative transfer code retrieves the directional bottom of atmosphere reflectance from the radiometrically-calibrated imagery. Commonly used approaches also are dark object methods [56], empirical line methods [57] and histogram matching methods [58]; reviews of different methods can be found in many sources [4][5][6]59]. The variations in atmospheric conditions, both in space and time, make the atmospheric correction challenging.…”

Section: Radiometric Correctionmentioning

confidence: 99%

Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

et al. 2009

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Abstract:The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images. The analysis is based on a literature research and a questionnaire submitted to various interest groups related to the photogrammetric process. An important contribution to this paper is a characterization of the photogrammetric image acquisition and image product generation systems. The questionnaire revealed many weaknesses in current processes, but the future prospects of radiometrically quantitative photogrammetry are promising.

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Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean

Cited by 325 publications

References 39 publications

Fast and simple model for atmospheric radiative transfer

Fast and simple model for atmospheric radiative transfer

Estimation of AOD Under Uncertainty: An Approach for Hyperspectral Airborne Data

Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

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