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Comparing Aircraft-Based Remotely Sensed Energy Balance Fluxes with Eddy Covariance Tower Data Using Heat Flux Source Area Functions

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  • 1 Remote Sensing Services Laboratory, Biological and Irrigation Engineering Department, Utah State University, Logan, Utah
  • | 2 Plant, Soils and Biometeorology Department, Utah State University, Logan, Utah
  • | 3 National Soil Tilth Laboratory, ARS, USDA, Ames, Iowa
  • | 4 Hydrology and Remote Sensing Laboratory, ARS, USDA, Beltsville, Maryland
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Abstract

In an effort to better evaluate distributed airborne remotely sensed sensible and latent heat flux estimates, two heat flux source area (footprint) models were applied to the imagery, and their pixel weighting/integrating functionality was investigated through statistical analysis. Soil heat flux and sensible heat flux models were calibrated. The latent heat flux was determined as a residual from the energy balance equation. The resulting raster images were integrated using the 2D footprints and were compared to eddy covariance energy balance flux measurements. The results show latent heat flux estimates (adjusted for closure) with errors of (mean ± std dev) −9.2 ± 39.4 W m−2, sensible heat flux estimate errors of 9.4 ± 28.3 W m−2, net radiation error of −4.8 ± 20.7 W m−2, and soil heat flux error of −0.5 ± 24.5 W m−2. This good agreement with measured values indicates that the adopted methodology for estimating the energy balance components, using high-resolution airborne multispectral imagery, is appropriate for modeling latent heat fluxes. The method worked well for the unstable atmospheric conditions of the study. The footprint weighting/integration models tested indicate that they perform better than simple pixel averages upwind from the flux stations. In particular the flux source area model (footprint) seemed to better integrate the resulting heat flux image pixels. It is suggested that future studies test the methodology for heterogeneous surfaces under stable atmospheric conditions.

Corresponding author address: Christopher M. U. Neale, Biological and Irrigation Engineering Department, Utah State University, Logan, UT 84322-4105. Email: cneale@cc.usu.edu

Abstract

In an effort to better evaluate distributed airborne remotely sensed sensible and latent heat flux estimates, two heat flux source area (footprint) models were applied to the imagery, and their pixel weighting/integrating functionality was investigated through statistical analysis. Soil heat flux and sensible heat flux models were calibrated. The latent heat flux was determined as a residual from the energy balance equation. The resulting raster images were integrated using the 2D footprints and were compared to eddy covariance energy balance flux measurements. The results show latent heat flux estimates (adjusted for closure) with errors of (mean ± std dev) −9.2 ± 39.4 W m−2, sensible heat flux estimate errors of 9.4 ± 28.3 W m−2, net radiation error of −4.8 ± 20.7 W m−2, and soil heat flux error of −0.5 ± 24.5 W m−2. This good agreement with measured values indicates that the adopted methodology for estimating the energy balance components, using high-resolution airborne multispectral imagery, is appropriate for modeling latent heat fluxes. The method worked well for the unstable atmospheric conditions of the study. The footprint weighting/integration models tested indicate that they perform better than simple pixel averages upwind from the flux stations. In particular the flux source area model (footprint) seemed to better integrate the resulting heat flux image pixels. It is suggested that future studies test the methodology for heterogeneous surfaces under stable atmospheric conditions.

Corresponding author address: Christopher M. U. Neale, Biological and Irrigation Engineering Department, Utah State University, Logan, UT 84322-4105. Email: cneale@cc.usu.edu

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