&lt;title&gt;Atmospheric correction for shortwave spectral imagery based on MODTRAN4&lt;/title&gt;

Adler‐Golden, Steven M.; Matthew, Michael W.; Bernstein, Lawrence S.; Levine, Robert Y.; Berk, Alexander; Richtsmeier, Steven C.; Acharya, Prabhat K.; Anderson, Gail P.; Felde, Jerry W.; Gardner, James A.; Hoke, Michael L.; Jeong, Laila S.; Pukall, Brian; Ratkowski, Anthony J.; Burke, Hsiao-hua K.

doi:10.1117/12.366315

Cited by 259 publications

(172 citation statements)

References 11 publications

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“…Radiometric coefficients for each band were included in the WV2 metadata and used to convert raw digital counts to radiance. Atmospheric correction was performed using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm based on the MODTRAN4 radiation transfer code (Adler-Golden et al, 1999). Inputs to FLAASH included the radiance image, scene date and location, ground elevation and sensor altitude, spectral band configuration, visibility (40 km), and standard models for the atmosphere (sub-arctic summer) and aerosols (rural).…”

Section: Image Data Acquisition and Processingmentioning

confidence: 99%

Mapping the bathymetry of supraglacial lakes and streams on the Greenland ice sheet using field measurements and high-resolution satellite images

et al. 2014

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Abstract. Recent melt events on the Greenland ice sheet (GrIS) accentuate the need to constrain estimates of sea level rise through improved characterization of meltwater pathways. This effort will require more precise estimates of the volume of water stored on the surface of the GrIS. We assessed the potential to obtain such information by mapping the bathymetry of supraglacial lakes and streams from WorldView2 (WV2) satellite images. Simultaneous in situ observations of depth and reflectance from two streams and a lake with measured depths up to 10.45 m were used to test a spectrally based depth retrieval algorithm. We performed optimal band ratio analysis (OBRA) of continuous field spectra and spectra convolved to the bands of the WV2, Landsat 7 (ETM+), MODIS, and ASTER sensors. The field spectra yielded a strong relationship with depth (R 2 = 0.94), and OBRA R 2 values were nearly as high (0.87-0.92) for convolved spectra, suggesting that these sensors' broader bands would be sufficient for depth retrieval. Our field measurements thus indicated that remote sensing of supraglacial bathymetry is not only feasible but potentially highly accurate. OBRA of spectra from 2 m-pixel WV2 images acquired within 3-72 h of our field observations produced an optimal R 2 value of 0.92 and unbiased, precise depth estimates, with mean and root mean square errors < 1 % and 10-25 % of the mean depth. Bathymetric maps produced by applying OBRA relations revealed subtle features of lake and channel morphology. In addition to providing refined storage volume estimates for lakes of various sizes, this approach can help provide estimates of the transient flux of meltwater through streams.

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Section: Image Data Acquisition and Processingmentioning

confidence: 99%

Mapping the bathymetry of supraglacial lakes and streams on the Greenland ice sheet using field measurements and high-resolution satellite images

et al. 2014

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“…Since the AISA images were collected on different days, spanning a period of about five months, the atmospheric -Golden et al, 1999;Matthew et al, 2000Matthew et al, , 2002) is a 'first-principles' atmospheric correction program, developed based on the radiative transfer code, MODTRAN 4 (MODerate spectral resolution atmospheric TRANsmittance; Berk et al, 2000). First-principles atmospheric correction typically involves three steps: (i) Retrieval of atmospheric parameters (primarily visibility/optical depth, aerosol type, and column water vapor amount), (ii) Solution of the radiative transfer equation using the retrieved/derived atmospheric parameters and conversion of the radiance values into reflectance values, and (iii) Spectral polishing to remove spectral artifacts that may have been introduced during the correction process (Matthew et al, 2002).…”

Section: Atmospheric Correctionmentioning

confidence: 99%

Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

et al. 2012

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“…The absolute atmospheric correction was accomplished using the fast line-of-sight atmospheric analysis of hypercubes (FLAASH). FLAASH method is based on MOD-TRAN (MODerate spectral resolution atmospheric TRANsmittance) that includes water vapor content, dioxide carbon concentration, and other atmospheric information for modelling atmospheric effects in the image (Adler-Golden et al 1999). As output, FLAASH produces a surface reflectance (R sup ) image.…”

Section: Oli Processingmentioning

confidence: 99%

An investigation into the effectiveness of relative and absolute atmospheric correction for retrieval the TSM concentration in inland waters

Bernardo

Watanabe

Rodrigues

et al. 2016

Model. Earth Syst. Environ.

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The absolute atmospheric correction inputs are not always available, and then such parameters are assumed based on geographical location, acquisition time and sensor type. These assumptions can imply in errors in retrieving the remote-sensing reflectance (R rs ), and affects the optically active compounds estimates. As an alternative, relative atmospheric correction, i.e. radiometric normalization, can be used in cases where there is no information about atmospheric conditions. The main goal of this work was to perform a comparative analysis between absolute and relative atmospheric correction to estimate total suspended matter (TSM) concentrations in the Barra Bonita Hydroelectric Reservoir (São Paulo State, Brazil). The corrections were applied to the operational land imager, on-board Lansat-8 satellite. The R rs errors from each correction were computed considering in situ data, and the lowest error was obtained for green spectral band (RMSE absolute = 11.5 % and RMSE relative = 12.3 %). Using a regional algorithm that was developed using the in situ measurements (the model was TSM = 1742.7*B3 -5.42, with R 2 = 0.60, p-value = 0.05), the estimated TSM concentrations from absolute and relative corrections retrieved a RSME of 11 and 6 %, respectively. The errors from absolute correction can be originated from the input parameters that were adopted, such as CO 2 concentration, initial visibility, and water vapor information. The relative correction can be more appropriate in such cases because, besides atmospheric effects, the method try to minimize the illumination variability using normalization between temporal images, which improves the reflectances, and consequently, decreases TSM retrieval errors.

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<title>Atmospheric correction for shortwave spectral imagery based on MODTRAN4</title>

Cited by 259 publications

References 11 publications

Mapping the bathymetry of supraglacial lakes and streams on the Greenland ice sheet using field measurements and high-resolution satellite images

Mapping the bathymetry of supraglacial lakes and streams on the Greenland ice sheet using field measurements and high-resolution satellite images

Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

An investigation into the effectiveness of relative and absolute atmospheric correction for retrieval the TSM concentration in inland waters

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