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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Publications at this Location » Publication #168045

Title: ASSESSMENT OF SATELLITE-BASED SURFACE ENERGY FLUX MODELS FOR AGRICULTURAL LANDS

Author
item French, Andrew

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/15/2004
Publication Date: 11/9/2004
Citation: French, A.N. 2004. Assessment of satellite-based surface energy flux models for agricultural lands. Agronomy Abstracts. CD-Rom 3688.

Interpretive Summary:

Technical Abstract: Satellite-based surface energy flux modeling is important for both agricultural and hydrological studies and is vital for plant growth and irrigation requirements. Some aspects of the local surface energy balance, such as local net radiation and turbulent fluxes, can be monitored with high quality ground-based observations, and can be the basis for model validation. But for heterogeneous landscapes, remote sensing observations are essential for accurate spatial flux estimation. Currently there are several physically-based remote sensing energy balance models that potentially yield accurate surface flux estimates. However, practical experience is limited and optimal models are unknown. This study compares three energy flux estimation approaches, in an experimental framework, to evaluate actual model performance. The setting, the Soil-Moisture-Atmospheric Coupling Experiment (SMACEX) 2002 site in central Iowa over corn and soybean fields, contained ground-based flux observations at 14 eddy covariance stations.The considered models are: 1) TSEB, a two-source soil/vegetation approach, 2) SEBAL, a one-source contextual model, and 3) the Carlson NDVI/Temperature triangle model. All models produce realistic flux estimates for a July 1, 2002 trial, but their spatial patternsare significantly different. These differences highlight model and observational assumptions and uncertainties, and illustrate difficulties encountered, such as soil moisture estimation under dense crop cover.