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United States Department of Agriculture

Agricultural Research Service

Research Project: Crop Evapotranspiration Determination Using Eddy Covariance Fluxes, High Resolution Remote Sensing Imagery & a Surface Temperature Approach

Location: Soil and Water Management Research

Project Number: 6209-13000-014-19
Project Type: Specific Cooperative Agreement

Start Date: Sep 01, 2009
End Date: Aug 31, 2014

Objective:
1. Evaluate eddy covariance (EC) latent (LE) and sensible (H) heat fluxes using large monolithic weighing lysimeters' data; 2. Develop and evaluate algorithms to estimate surface aerodynamic temperature (SAT) and aerodynamic surface roughness lengths [for momentum (Zom) and heat transfer (Zoh)] using wind/temperature profiles, high resolution remote sensing (RS) imagery, and EC data; 3. Evaluate the surface renewal (SR) model for its capability to estimate crop and native grassland actual ET using wind/temperature profiles, scintillometer, and EC data; 4. Develop and evaluate a RS-SAT-based single source energy balance (EB) model, to accurately estimate spatially distributed ET, using measured evapotranspiration (ET) by large monolithic weighing lysimeters and a network of EC EB systems; and 5. Evaluate a modified RS-based two-source EB model (M-TSM) using large monolithic weighing lysimeters.

Approach:
This agreement refers to activities involving the processing and analysis of data collected during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment of 2008 (BEAREX08) over an area within the limits of the USDA-ARS, Conservation and Production Research Laboratory (CPRL), at Bushland, Texas. The CPRL is located 1170 m above mean sea level at 35 degrees 11' N, 102 degrees 06' W, in the Texas High Plains. Objective 1: The EC raw data (i.e., times series high frequency data) will be processed using the micrometeorological applications software EdiRe. Different levels of data processing will be performed and resulting LE and H values will be compared to lysimetric data. Objective 2: In mapping ET, LE can be spatially estimated as an EB residual for land surfaces using remote sensing inputs. The EB equation requires the estimation of net radiation (Rn), soil heat flux (G), and H. Rn and G can be estimated with acceptable accuracy. However, H may be under estimated when the radiometric surface temperature (RST) is used rather than the surface aerodynamic temperature (SAT) in the aerodynamic resistance equation. The objective is to model SAT to improve the estimation of H and consequently ET for the semi-arid, advective environment of the Texas High Plains. Objective 3: High frequency (1 Hz) wind, air temperature and relative humidity data (profile) recorded at six heights, over a native grassland/rangeland during the fall of 2007 and winter of 2008, will be used in surface renewal (SR) analysis in order to estimate H. Estimates of H will be compared to measured H by a large aperture scintillometer (LAS). H will be computed using different combinations of heights (i.e., of U and Ta recorded at given heights). Similarly, for a dryland cotton crop (2008 data), H will be estimated using the SR approach; however, resulting H values will be compared to measured H by an EC system. Objective 4: Instantaneous, daily, weekly, monthly, and seasonal estimates of spatially distributed ET will be mapped using very high resolution airborne surface reflectance and radiometric surface temperature images along with weather data in the newly developed SAT-based single source EB model. Distributed estimates of Rn, G, and H will be compared to measured values. In the case of H (as for LE) a footprint analysis will be used. Objective 5: Instantaneous and daily ET maps will be produced using a modified two-source EB model (M-TSM), airborne-based surface reflectance/temperature images, and ground-based agroclimatological data. Data obtained during the 2007 BEAREX campaigned will be used in the parameterization process and data collected during BEAREX08 will be used in the application and verification of the M-TSM. Model verification will be performed using data from the large weighing lysimeters.

Last Modified: 7/31/2014
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