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ARS Home » Research » Publications at this Location » Publication #142243
Title: WATERSHED SCALE REMOTE SENSING RESEARCH IN HYDROMETEOROLOGY

Author
item Kustas, William - Bill
item Hatfield, Jerry
item Jackson, Thomas
item Moran, Mary
item Rango, Albert
item Ritchie, Jerry
item Schmugge, Thomas
item Rawls, Walter

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/30/2002
Publication Date: 12/30/2002
Citation: Kustas, W.P., Hatfield, J.L., Jackson, T.J., Moran, Mary, S., Rango, A., Ritchie, J., Schmugge, T.J., Rawls, W.J. 2002. Watershed scale remote sensing research in Hydrometeorology [abstract]. In: Proceedings of First Interagency Conference on Research in the Watershed, October 27-30, 2003, Benson, Arizona.

Interpretive Summary:

Technical Abstract: This paper provides an overview of watershed scale remote sensing research in hydrometeorology with an emphasis on the major contributions that have been made by United States Department of Agriculture-Agricultural Research Service (USDA-ARS). The major contributions are separated into deriving from remote sensing 1) hydrometeorological state variables and 2) energy fluxes, particularly evapotranspiration which includes plant water stress. For the state variables, remote sensing algorithms have been developed for estimating land surface temperatures from brightness temperature observations correcting for atmospheric and emissivity effects, estimating near-surface soil moisture from passive microwave remote sensing, determining snow cover from visible and snow water equivalent from microwave data, and estimating landscape roughness, topography, vegetation height and fractional cover from lidar distancing technology. For the hydrometeorological fluxes, including plant water stress, models estimating evapotranspiration have been developed using land surface temperature as a key boundary condition with recent schemes designed to more reliably handle partial vegetation cover conditions. ARS researchers continue to develop new and improved remote sensing algorithms for evaluating state variables and fluxes. These include investigating the utility of combining multifrequency remote sensing data for improved estimation of land surface properties, and incorporating remote sensing for evaluating the effects of landscape heterogeneity on atmospheric dynamics and mean air properties and resulting feedbacks on the land surface fluxes.