Relationships among Remotely Sensed Data, Surface Energy Balance, and Area-Averaged Fluxes over Partially Vegetated Land Surfaces

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  • 1 Department of Geography and Center for Remote Sensing, Boston University, Boston, Massachusetts
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Abstract

Numerous recent field experiments have examined the use of remote sensing to estimate land surface fluxes of latent and sensible heat using combinations of thermal, visible, and near-infrared data. While substantial progress has been made, significant problems remain unresolved with respect to both spatial aggregation of land surface fluxes over heterogeneous land surfaces and the use of thermal data for estimating sensible heat fluxes. In this paper a surface energy balance model is used, in association with remotely sensed and in situ data, to examine issues of measurement, scaling, and aggregation of high-frequency spatial variation in land surface properties and fluxes over regional scales. Results from this analysis show that instantaneous land surface fluxes modeled from high spatial resolution remotely sensed data may be estimated only approximately. Comparisons between modeled versus observed fluxes averaged over regional scales (≈225 km2), on the other hand, exhibit excellent agreement. Based on these results, it is concluded that the estimation of surface fluxes at high spatial resolution is problematic because the remotely sensed measurements reflect local land surface conditions, while land surface fluxes are produced by processes associated with surface-atmosphere interactions occurring over substantially larger areas. Because land surface-atmosphere interactions effectively integrate high-frequency spatial variance in land surface properties, relatively coarse spatial resolution (hundreds of meters to 1 km) or random samples of high-resolution data may be used for surface energy balance modeling over regional scales. Operational use of remote sensing to estimate land suffice fluxes, however, requires improved understanding of the nonlinearity in surface energy balance with respect to remotely sensed inputs and improved knowledge of the length scales and magnitude of spatial variance in land surface properties over regional scales.

Abstract

Numerous recent field experiments have examined the use of remote sensing to estimate land surface fluxes of latent and sensible heat using combinations of thermal, visible, and near-infrared data. While substantial progress has been made, significant problems remain unresolved with respect to both spatial aggregation of land surface fluxes over heterogeneous land surfaces and the use of thermal data for estimating sensible heat fluxes. In this paper a surface energy balance model is used, in association with remotely sensed and in situ data, to examine issues of measurement, scaling, and aggregation of high-frequency spatial variation in land surface properties and fluxes over regional scales. Results from this analysis show that instantaneous land surface fluxes modeled from high spatial resolution remotely sensed data may be estimated only approximately. Comparisons between modeled versus observed fluxes averaged over regional scales (≈225 km2), on the other hand, exhibit excellent agreement. Based on these results, it is concluded that the estimation of surface fluxes at high spatial resolution is problematic because the remotely sensed measurements reflect local land surface conditions, while land surface fluxes are produced by processes associated with surface-atmosphere interactions occurring over substantially larger areas. Because land surface-atmosphere interactions effectively integrate high-frequency spatial variance in land surface properties, relatively coarse spatial resolution (hundreds of meters to 1 km) or random samples of high-resolution data may be used for surface energy balance modeling over regional scales. Operational use of remote sensing to estimate land suffice fluxes, however, requires improved understanding of the nonlinearity in surface energy balance with respect to remotely sensed inputs and improved knowledge of the length scales and magnitude of spatial variance in land surface properties over regional scales.

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