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SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part I: Development and Validation
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Remote Sens. 2015, 7(11), 15068-15081; doi:10.3390/rs71115068

SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part II: Test for Transferability

1
Civil and Environmental Engineering Department, Colorado State University, Fort Collins, CO 80523, USA
2
Soil and Crop Sciences Department, Colorado State University, Fort Collins, CO 80523, USA
3
United States Department of Agriculture, Agricultural Research Service, Fort Collins, CO 80526, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Senay, Alfredo R. Huete and Prasad S. Thenkabail
Received: 17 May 2015 / Revised: 16 September 2015 / Accepted: 18 September 2015 / Published: 10 November 2015
View Full-Text   |   Download PDF [837 KB, uploaded 13 November 2015]   |  

Abstract

Because the Surface Energy Balance Algorithm for Land (SEBAL) tends to underestimate ET when there is advection, the model was modified by incorporating an advection component as part of the energy usable for crop evapotranspiration (ET). The modification involved the estimation of advected energy, which required the development of a wind function. In Part I, the modified SEBAL model (SEBAL-A) was developed and validated on well-watered alfalfa of a standard height of 40–60 cm. In this Part II, SEBAL-A was tested on different crops and irrigation treatments in order to determine its performance under varying conditions. The crops used for the transferability test were beans (Phaseolus vulgaris L.), wheat (Triticum aestivum L.) and corn (Zea mays L.). The estimated ET using SEBAL-A was compared to actual ET measured using a Bowen Ratio Energy Balance (BREB) system. Results indicated that SEBAL-A estimated ET fairly well for beans and wheat, only showing some slight underestimation of a Mean Bias Error (MBE) of −0.7 mm·d−1 (−11.3%), a Root Mean Square Error (RMSE) of 0.82 mm·d−1 (13.9%) and a Nash Sutcliffe Coefficient of Efficiency (NSCE) of 0.64. On corn, SEBAL-A resulted in an ET estimation error MBE of −0.7 mm·d−1 (−9.9%), a RMSE of 1.59 mm·d−1 (23.1%) and NSCE = 0.24. This result shows an improvement on the original SEBAL model, which for the same data resulted in an ET MBE of −1.4 mm·d−1 (−20.4%), a RMSE of 1.97 mm·d−1 (28.8%) and a NSCE of −0.18. When SEBAL-A was tested on only fully irrigated corn, it performed well, resulting in no bias, i.e., MBE of 0.0 mm·d−1; RMSE of 0.78 mm·d−1 (10.7%) and NSCE of 0.82. The SEBAL-A model showed less or no improvement on corn that was either water-stressed or at early stages of growth. The errors incurred under these conditions were not due to advection not accounted for but rather were due to the nature of SEBAL and SEBAL-A being single-source energy balance models and, therefore, not performing well over heterogeneous surfaces. Therefore, it was concluded that SEBAL-A could be used on a wide range of crops if they are not water stressed. It is recommended that the SEBAL-A model be further studied to be able to accurately estimate ET under dry and sparse surface conditions. View Full-Text
Keywords: SEBAL; SEBAL-A; effective advection; surface roughness SEBAL; SEBAL-A; effective advection; surface roughness
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Mkhwanazi, M.; Chávez, J.L.; Andales, A.A.; DeJonge, K. SEBAL-A: A Remote Sensing ET Algorithm that Accounts for Advection with Limited Data. Part II: Test for Transferability. Remote Sens. 2015, 7, 15068-15081.

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