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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #319579

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Advances in the Two Source Energy Balance (TSEB) model using very high resolution remote sensing data in vineyards

Author
item NIETO, H. - Institute For Sustainable Agriculture
item Kustas, William - Bill
item TORRES, A. - Utah State University
item ELARAB, M. - Utah State University
item SONG, L. - Beijing Normal University
item Alfieri, Joseph
item Prueger, John
item McKee, Lynn
item Anderson, Martha
item ALSINA, MIMAR - E & J Gallo Winery
item JENSEN, A. - Utah State University
item MCKEE, M. - Utah State University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 10/2/2015
Publication Date: 12/5/2015
Citation: Nieto, H., Kustas, W.P., Torres, A., Elarab, M., Song, L., Alfieri, J.G., Prueger, J.H., Mckee, L.G., Anderson, M.C., Alsina, M., Jensen, A., Mckee, M. 2015. Advances in the Two Source Energy Balance (TSEB) model using very high resolution remote sensing data in vineyards [abstract]. https://agu.confex.com/agu/fm15/meetingapp.cgi/Paper/61495.

Interpretive Summary:

Technical Abstract: The thermal-based Two Source Energy Balance (TSEB) model partitions the water and energy fluxes from vegetation and soil components providing thus the ability for estimating soil evaporation (E) and canopy transpiration (T) separately. However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures as well as the net radiation partitioning ('Rn), as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in agricultural areas, with vegetation clumped along rows and hence only partially covering the soil surface for much of the growing season, and with extreme effects on radiation and temperature partitioning in vineyards and orchards. To better understand the effects of strongly clumped vegetation on radiation and land surface temperature (LST) partitioning very high spatial resolution remote sensing data acquired from an Unmanned Aerial System (UAS) were collected over vineyards in California, as part of The Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). The multi-temporal observations from the UAS and very high pixel resolution permitted the estimation of reliable soil and leaf temperatures using a contextual algorithm based on the inverse relationship between LST and a vegetation index. An improvement in the algorithm estimating the effective leaf area index explicitly developed for vine rows and 'Rn using the 4SAIL Radiative Transfer Model is shown. The revisions to the TSEB model are evaluated with in situ measurements of energy fluxes and transmitted solar radiation. Results show that the modifications to the TSEB resulted in closer agreement with the ground measurements compared to the original TSEB model formulations. The significant advantages in using very high resolution remote sensing data for ET monitoring in agricultural regions having strongly clumped vegetation will be discussed.