Multicriteria Decision-Making Approach to Enhance Automated Anchor Pixel Selection Algorithm for Arid and Semi-Arid Regions
Publication: Journal of Hydrologic Engineering
Volume 25, Issue 11
Abstract
Finding the precise value of pixel-scale evapotranspiration (ET) for an entire basin is a major challenge to hydrologists. Many efforts have been made to conquer this challenge, among which remote sensing methods are the most promising ones. The surface energy balance algorithm for land (SEBAL) is one of the several established remote sensing methods to estimate ET. The anchor pixel selection process (e.g., hot and cold pixels) is one of the critical steps in the SEBAL model that also determines the accuracy of the model outputs. Several researchers have improved anchor pixel selection by an automated fashion. In the current study, a new simple method has been proposed to seek the best anchor pixels. Then, the daily ET outputs were assessed using observed data from the Eddy covariance (EC) tower data at Santa Cruz River Watershed for 2014–2015. The results showed that the daily ET with measured data at the study site confirmed our automated anchor pixel selection method to be reliable in terms of selecting appropriate hot and cold pixels under dry conditions by producing ET maps with reasonable accuracies ( and ). Our study suggests that considerations of simple image-derived parameters, such as temperature difference between hot and cold pixels, distance from a representative station, and elevation differences could improve the automatic selection of anchor pixels and the implementation of the SEBAL model under dry conditions. The machine learning technique could be combined with the proposed automated algorithm to map ET in different climates (not only arid and semiarid) faster and with more accuracy.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
Our special thanks go to USGS, FLUXNET community and National Climatic Data Center for providing us with the Landsat 8 dataset, eddy covariance, and synoptic station data. Also, we thank Dr. Nishan Bhattarai University of Michigan for helping us to develop the algorithm.
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Journal of Hydrologic Engineering
Volume 25 • Issue 11 • November 2020
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© 2020 American Society of Civil Engineers.
History
Received: Sep 21, 2019
Accepted: Jun 16, 2020
Published online: Sep 11, 2020
Published in print: Nov 1, 2020
Discussion open until: Feb 11, 2021
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