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Geospatial approach for estimating l...

The University of Nebraska - Lincoln., Agricultural & Biological Systems Engineering.

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Geospatial approach for estimating land surface evapotranspiration.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Geospatial approach for estimating land surface evapotranspiration./
作者:	Singh, Ramesh K.
面頁冊數:	233 p.
附註:	Adviser: Ayse Irmak.
Contained By:	Dissertation Abstracts International70-03B.
標題:	Engineering, Agricultural. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3350377
ISBN:	9781109082036

Geospatial approach for estimating land surface evapotranspiration.
Singh, Ramesh K.

Geospatial approach for estimating land surface evapotranspiration. - 233 p.

Adviser: Ayse Irmak.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2009.

Reliably and accurately quantifying evapotranspiration (ET) in a spatial and temporal domain is important in water management at the local, regional, and global scales. With advances in image processing and hardware computational ability, energy balance models which utilize remote sensing images are being increasingly utilized for quantifying ET and used as inputs in hydrologic modeling. The objectives of this research were to evaluate and improve some of the energy balance models for estimating land surface ET, and develop a framework for estimating seasonal ET from temporal satellite images. Surface Energy Balance Algorithm for Land (SEBAL) model was used to estimate energy fluxes for south-central Nebraska using Landsat images. Results were compared with Bowen Ratio Energy Balance System (BREBS) field measurements. SEBAL estimated ET images were also used for computing crop coefficients (K c) for maize, soybean, sorghum, and alfalfa under irrigated and dryland conditions. Performances of four remote sensing based models for estimating soil heat flux (G) were analyzed. A new model was developed for remotely estimating G. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model was also used for estimating energy fluxes using Landsat images. The METRIC model was modified by incorporating the Priestley-Taylor (PT) approach. The SEBAL model estimated net radiation (Rn) with a root mean square error (RMSE) of 65 W m-2 (r2 = 0.76). Calibrating G locally reduced RMSE from 80 W m-2 to 20 W m-2. The SEBAL model yielded sensible heat flux (H) with RMSE of 108 W m -2 (r2=0.23), and ET with an RMSE of 1.04 mm day -1(r2 = 0.73). Validation of Kc regression for irrigated maize resulted in RMSE of 0.21 (r2=0.74). The METRIC model estimated Rn, G, and H with RMSE values of 45 W m -2 (r2=0.85), 19 W m-2 (r2=0.85), and 113 W m-2 (r2=0.50), respectively. The modified METRIC model reduced the RMSE of H from 113 W m-2 to 91 W m -2 and that for ETc reduced from 1.3 mm day-1 to 1 mm day-1. SEBAL and METRIC models have limitations if the images are acquired following heavy rain. The modified METRIC model is better suited for estimating ET for high residual moisture and non-advective conditions. The statistical comparison of seasonal ET based on three interpolation methods showed that no interpolation method is the best suited for all conditions.

ISBN: 9781109082036Subjects--Topical Terms:

1019504
Engineering, Agricultural.

Geospatial approach for estimating land surface evapotranspiration.
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502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2009.
520 $a Reliably and accurately quantifying evapotranspiration (ET) in a spatial and temporal domain is important in water management at the local, regional, and global scales. With advances in image processing and hardware computational ability, energy balance models which utilize remote sensing images are being increasingly utilized for quantifying ET and used as inputs in hydrologic modeling. The objectives of this research were to evaluate and improve some of the energy balance models for estimating land surface ET, and develop a framework for estimating seasonal ET from temporal satellite images. Surface Energy Balance Algorithm for Land (SEBAL) model was used to estimate energy fluxes for south-central Nebraska using Landsat images. Results were compared with Bowen Ratio Energy Balance System (BREBS) field measurements. SEBAL estimated ET images were also used for computing crop coefficients (K c) for maize, soybean, sorghum, and alfalfa under irrigated and dryland conditions. Performances of four remote sensing based models for estimating soil heat flux (G) were analyzed. A new model was developed for remotely estimating G. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model was also used for estimating energy fluxes using Landsat images. The METRIC model was modified by incorporating the Priestley-Taylor (PT) approach. The SEBAL model estimated net radiation (Rn) with a root mean square error (RMSE) of 65 W m-2 (r2 = 0.76). Calibrating G locally reduced RMSE from 80 W m-2 to 20 W m-2. The SEBAL model yielded sensible heat flux (H) with RMSE of 108 W m -2 (r2=0.23), and ET with an RMSE of 1.04 mm day -1(r2 = 0.73). Validation of Kc regression for irrigated maize resulted in RMSE of 0.21 (r2=0.74). The METRIC model estimated Rn, G, and H with RMSE values of 45 W m -2 (r2=0.85), 19 W m-2 (r2=0.85), and 113 W m-2 (r2=0.50), respectively. The modified METRIC model reduced the RMSE of H from 113 W m-2 to 91 W m -2 and that for ETc reduced from 1.3 mm day-1 to 1 mm day-1. SEBAL and METRIC models have limitations if the images are acquired following heavy rain. The modified METRIC model is better suited for estimating ET for high residual moisture and non-advective conditions. The statistical comparison of seasonal ET based on three interpolation methods showed that no interpolation method is the best suited for all conditions.
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