Atmospheric water vapor sensitivity and compensation requirement for Earth‐looking imaging spectrometers in the solar‐reflected spectrum

Green, Robert O.

doi:10.1029/2000jd900799

Cited by 17 publications

(14 citation statements)

References 18 publications

(10 reference statements)

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“…Retrieval errors also depend on other factors, e.g., a shift in channel locations, coregistration between different channels, type of spectral interpolation between absorption and window channels, haze particles, subpixel clouds, etc. (these are not considered here; a discussion of these effects is presented in [23] and [31]). …”

Section: Application Of the Methodology: Case Studymentioning

confidence: 99%

Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

Richter

Schläpfer

2008

IEEE Trans. Geosci. Remote Sensing

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Considerations on water vapor and surface reflectance retrievals for a spaceborne imaging spectrometer Considerations on water vapor and surface reflectance retrievals for a spaceborne imaging spectrometer Abstract Abstract-The retrievals of atmospheric water vapor column and surface reflectance from air-or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time consuming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a methodology to compile the database for a specified retrieval accuracy in water vapor and surface reflectance for a given set of input surface reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considerations are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and surface reflectance, respectively. It should be pointed out that this is not intended as a general claim of retrieval accuracy achievable under typical remote sensing conditions, but these figures apply only to the theoretical conditions of the calculation, i.e., assuming the same conditions for forward simulation and retrieval. Nevertheless, these figures are indispensable for the design of a database, which is an important step for the atmospheric correction of imaging spectrometer data and the sole topic of this paper Abstract-The retrievals of atmospheric water vapor column and surface reflectance from air-or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time consuming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a methodology to compile the database for a specified retrieval accuracy in water vapor and surface reflectance for a given set of input surface reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considerations are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and surface reflectance, respectively. It s...

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Section: Application Of the Methodology: Case Studymentioning

confidence: 99%

Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

Richter

Schläpfer

2008

IEEE Trans. Geosci. Remote Sensing

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“…The primary example of such work is the retrieval of column-integrated atmospheric water vapor and liquid water in vegetation (Gao & Goetz,1990). Successful estimates depend on careful calibration of the sensor, in terms of the wavelength positions of the bands and the radiometry (Green, 2001).…”

Section: Introductionmentioning

confidence: 99%

Interpretation of snow properties from imaging spectrometry

Dozier

Green

Nolin

et al. 2009

Remote Sensing of Environment

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181

115

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“…We inverted AVIRIS-calibrated radiance for apparent surface reflectance using a nonlinear least squares water vapor fitting model (Green, Conel, & Roberts, 1993) that incorporates the atmospheric transmission model MODTRAN4 (Berk et al, 1998). This algorithm accounts for atmospheric spatial heterogeneity by solving for the atmospheric conditions pixel-by-pixel from the AVIRIS radiance data and computes R S,k (Green, 2001). …”

Section: Retrieval Of Apparent Surface Reflectancementioning

confidence: 99%

Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data

Painter

Dozier

Roberts

et al. 2003

Remote Sensing of Environment

Self Cite

315

228

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We describe and validate an automated model that retrieves subpixel snow-covered area and effective grain size from Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) data. The model analyzes multiple endmember spectral mixtures with a spectral library of snow, vegetation, rock, and soil. We derive snow spectral endmembers of varying grain size from a radiative transfer model; spectra for vegetation, rock, and soil were collected in the field and laboratory. For three AVIRIS images of Mammoth Mountain, California that span common snow conditions for winter through spring, we validate the estimates of snow-covered area with fine-resolution aerial photographs and validate the estimates of grain size with stereological analysis of snow samples collected within 2 h of the AVIRIS overpasses. The RMS error for snowcovered area retrieved from AVIRIS for the combined set of three images was 4%. The RMS error for snow grain size retrieved from a 3 Â 3 window of AVIRIS data for the combined set of three images is 48 Am, and the RMS error for reflectance integrated over the solar spectrum and over all hemispherical reflectance angles is 0.018. D

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Atmospheric water vapor sensitivity and compensation requirement for Earth‐looking imaging spectrometers in the solar‐reflected spectrum

Cited by 17 publications

References 18 publications

Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

Interpretation of snow properties from imaging spectrometry

Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data

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