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Simultaneous Land Surface Temperature–Emissivity Retrieval in the Infrared Split Window

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  • 1 Department of Meteorology, The Florida State University, Tallahassee, Florida
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Abstract

A combined land surface temperature–emissivity retrieval algorithm is developed and tested for Geostationary Operational Environmental Satellite (GOES)-Imager and National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) split-window channels. By assuming that the spectral emissivities are constant over a short time period (12–24 h), two sets of split-window radiance measurements taken at two different times are used to retrieve two spectral emissivities and two land surface temperatures (LSTs) simultaneously. The algorithm employs an optimization scheme rather than a direct solver for a system of equations because of constraint requirements. The retrieved variables minimize the rms differences between measured satellite radiances and those predicted by a spectrally detailed radiative transfer model.

A GOES-8 version of the algorithm is validated with in situ radiometer measurements from the Department of Energy’s Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM CART) site. In addition, an AVHRR version is validated with in situ measurements from the First ISLSCP Field Experiment (FIFE) site, the Hydrological Atmospheric Pilot Experiment–Sahel (HAPEX–Sahel) site, and an LST validation site operated near Melbourne, Australia. The biases of the retrieved LSTs for the validation sites in the Australian, FIFE, and ARM CART study areas are approximately 0.08°, 1.7°, and 1.4°C, respectively, yielding an overall bias error of better than half the current expected accuracy limit of some ±3°C. The associated bias-adjusted rmse differences are approximately 0.78°, 4.8°, and 4.5°C, respectively, mostly driven by intercomparing in situ point measurements to area-integrated satellite pixel retrievals. The bias-adjusted rmse differences for HAPEX–Sahel are larger (5° and 11°C), resulting from incomplete characterization of site heterogeneity, insufficient radiosonde launch frequency, and poor data quality of the temperature–moisture soundings, rather than intrinsic algorithm problems. Notably, the averaged retrieved emissivities for the trouble-free sites are within the expected range of emissivities for vegetated surfaces.

The GOES-8 retrieved LSTs exhibit small amplitude, high-frequency noise, and a daily error cycle when compared to in situ measurements. The noise is attributed to random detector errors in the satellite observations for which the channel 4 noise-equivalent temperature difference is larger than that of channel 5. The systematic differences between validation measurements and retrievals are near zero during nighttime but exhibit a small semidiurnal oscillation during daytime. Notwithstanding a possible semidiurnal bias in the pyrgeometer validation measurements associated with imperfect solar dome heating corrections, plus unaccounted-for attenuation between the surface and pyrgeometer, the latter error cycle is attributed to a too-coarse sampling of the nonlinear diurnal evolution of the thermodynamic structure of the atmospheric boundary layer, particularly near the sunrise and sunset transition times. Thus, sounding frequency determines the error characteristics of the nonlinearly evolving split-window weighting functions.

Corresponding author address: Dr. Eric A. Smith, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-4520.

Email: esmith@metsat.met.fsu.edu

Abstract

A combined land surface temperature–emissivity retrieval algorithm is developed and tested for Geostationary Operational Environmental Satellite (GOES)-Imager and National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) split-window channels. By assuming that the spectral emissivities are constant over a short time period (12–24 h), two sets of split-window radiance measurements taken at two different times are used to retrieve two spectral emissivities and two land surface temperatures (LSTs) simultaneously. The algorithm employs an optimization scheme rather than a direct solver for a system of equations because of constraint requirements. The retrieved variables minimize the rms differences between measured satellite radiances and those predicted by a spectrally detailed radiative transfer model.

A GOES-8 version of the algorithm is validated with in situ radiometer measurements from the Department of Energy’s Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM CART) site. In addition, an AVHRR version is validated with in situ measurements from the First ISLSCP Field Experiment (FIFE) site, the Hydrological Atmospheric Pilot Experiment–Sahel (HAPEX–Sahel) site, and an LST validation site operated near Melbourne, Australia. The biases of the retrieved LSTs for the validation sites in the Australian, FIFE, and ARM CART study areas are approximately 0.08°, 1.7°, and 1.4°C, respectively, yielding an overall bias error of better than half the current expected accuracy limit of some ±3°C. The associated bias-adjusted rmse differences are approximately 0.78°, 4.8°, and 4.5°C, respectively, mostly driven by intercomparing in situ point measurements to area-integrated satellite pixel retrievals. The bias-adjusted rmse differences for HAPEX–Sahel are larger (5° and 11°C), resulting from incomplete characterization of site heterogeneity, insufficient radiosonde launch frequency, and poor data quality of the temperature–moisture soundings, rather than intrinsic algorithm problems. Notably, the averaged retrieved emissivities for the trouble-free sites are within the expected range of emissivities for vegetated surfaces.

The GOES-8 retrieved LSTs exhibit small amplitude, high-frequency noise, and a daily error cycle when compared to in situ measurements. The noise is attributed to random detector errors in the satellite observations for which the channel 4 noise-equivalent temperature difference is larger than that of channel 5. The systematic differences between validation measurements and retrievals are near zero during nighttime but exhibit a small semidiurnal oscillation during daytime. Notwithstanding a possible semidiurnal bias in the pyrgeometer validation measurements associated with imperfect solar dome heating corrections, plus unaccounted-for attenuation between the surface and pyrgeometer, the latter error cycle is attributed to a too-coarse sampling of the nonlinear diurnal evolution of the thermodynamic structure of the atmospheric boundary layer, particularly near the sunrise and sunset transition times. Thus, sounding frequency determines the error characteristics of the nonlinearly evolving split-window weighting functions.

Corresponding author address: Dr. Eric A. Smith, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-4520.

Email: esmith@metsat.met.fsu.edu

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