George Paul

COMPARING SEBAL ET WITH LYSIMETER DATA IN THE SEMI-ARID TEXAS HIGH PLAINS

SEBAL (Surface Energy Balance Algorithm for Land), a spatial evapotranspiration (ET) estimation method, has been applied with Landsat Thematic Mapper (TM) data throughout the world. However, it has never been tested for semiarid conditions of the Texas High Plains. In this study, SEBAL algorithm was applied to a Landsat TM image acquired on July 10, 2007 covering a major portion of the Texas High Plains. Performance of SEBAL was evaluated by comparing estimated ET with measured ET data on four large monolithic lysimeters at the USDA-ARS Conservation and Production Research Laboratory, Bushland, TX. Comparison of SEBAL-estimated instantaneous ET values with lysimetric measurements indicated that SEBAL may provide better ET estimates for irrigated fields. However, it performed poorly in predicting ET for fields under dryland management. This result may indicate that SEBAL might be sensitive to errors in the selection of the hot/dry pixel. Overall, SEBAL is a promising tool for mapping ET in the extensively irrigated Texas High Plains. However, more evaluation is needed for different agroclimatological conditions in the region.

Evaluating Surface Energy Balance System (SEBS) using Aircraft data collected during BEAREX07

Evapotranspiration (ET) is an essential component of the water balance and a major consumptive use of irrigation water and precipitation on cropland. Remote sensing based surface energy balance algorithms are now capable of providing accurate estimates of spatial-temporal ET. Uses of these spatial ET estimates are innumerable including hydrological modeling, irrigation scheduling, drought and flood monitoring and global climate change studies. The objective of this study was to evaluate the ability of the Surface Energy Balance System (SEBS) to estimate hourly ET fluxes using very high resolution (0.5-1.8 m) aircraft images acquired during the BEAREX07 (Bushland ET and Agricultural Remote Sensing Experiment 2007). Accuracy of the predicted ET fluxes were investigated using observed data from 4 large weighing lysimeters, each located at the center of 4.7 ha field in the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. The uniqueness and the strength of this study come from the fact that it evaluates the SEBS for irrigated and dryland conditions simultaneously with each lysimeter field planted to irrigated forage sorghum, irrigated forage corn, dryland clumped-grain sorghum, and dryland row-grain sorghum. Eleven images acquired during early and mid cropping seasons (June 24 - July 27) were used in the study. SEBS algorithm performed equally well for both irrigated and dryland conditions in estimating the hourly ET with overall mean bias error and root mean square of -0.01 and 0.11 mm h 1 (-1.53% and 20.27%), respectively.

Downscaling Surface Temperature Image with TsHARP

Daily evapotranspiration (ET) maps would significantly improve assessing crop water requirements especially in Texas High Plains (THP) where supply of irrigation water is limited. ET maps derived from satellite data with daily coverage such as MODIS and GOES sensors are inadequate, because their thermal pixel size is larger than individual agricultural fields. However, there exists an opportunity to use simultaneously acquired high resolution visible, near-infrared, and shortwave-infrared images from MODIS, and thermal-infrared images from other high resolutions sensors such as LANDSAT 5 Thematic Mapper (TM) or ASTER to improve spatial and temporal resolution of ET maps. Image downscaling methods are useful to improve spatial resolution by examining relationships between simultaneously acquired coarser thermal and finer non-thermal datasets. In this study, the TsHARP, an image downscaling technique, was evaluated for its capability to downscale land surface temperature (LST) images for ET mapping. LANDSAT 5 TM images taken from a southern part of the THP area were utilized to implement TsHARP. For this purpose, we developed a synthetic image with a spatial resolution of 960x960 m using TM based 120x120 m LST image. The 960x960 m resolution was used to mimic a LST image derived from MODIS thermal data. TsHARP was implemented to develop a LST image at 120x120 m resolution using a statistical relationship between LST and normalized difference vegetation index (NDVI). Comparison of downscaled 120x120 m LST image against original LST image from TM data yielded a correlation coefficient of 0.93.Results indicate that TsHARP has the potential to be used to downscale LST images with simultaneously acquired high resolution NDVI image derived from MODIS data.

Surface Energy Balance System for Estimating Daily Evapotranspiration Rates in the Texas High Plains

Numerous energy balance (EB) algorithms have been developed to use remote sensing data for mapping evapotranspiration (ET) on a regional basis. Adopting any single or a combination of these models for an operational ET remote sensing program requires thorough evaluation. The Surface Energy Balance System (SEBS) was evaluated for its ability to estimate daily ET rates of summer crops grown in the Texas High Plains using Landsat 5 Thematic Mapper data acquired on 10 July 2007. Performance of SEBS was evaluated by comparing estimated daily ET with measured weighing lysimeter ET data from four large lysimeters at the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. SEBS estimated daily ET values measured with the weighing lysimeter measurements indicated that the SEBS model may provide good ET estimates for both irrigated and dryland fields.