Estimation of Spatially Distributed Evapotranspiration Using Remote Sensing and a Relevance Vector Machine
Publication: Journal of Irrigation and Drainage Engineering
Volume 140, Issue 8
Abstract
With the development of surface energy balance analyses, remote sensing has become a spatially explicit and quantitative methodology for understanding evapotranspiration (ET), a critical requirement for water resources planning and management. Limited temporal resolution of satellite images and cloudy skies present major limitations that impede continuous estimates of ET. This study introduces a practical approach that overcomes (in part) the previous limitations by implementing machine learning techniques that are accurate and robust. The analysis was applied to the Canal B service area of the Delta Canal Company in central Utah using data from the 2009–2011 growing seasons. Actual ET was calculated by an algorithm using data from satellite images. A relevance vector machine (RVM), which is a sparse Bayesian regression, was used to build a spatial model for ET. The RVM was trained with a set of inputs consisting of vegetation indexes, crops, and weather data. ET estimated via the algorithm was used as an output. The developed RVM model provided an accurate estimation of spatial ET based on a Nash-Sutcliffe coefficient () of 0.84 and a root-mean-squared error (RMSE) of . This methodology lays the groundwork for estimating ET at a spatial scale for the days when a satellite image is not available. It could also be used to forecast daily spatial ET if the vegetation indexes model inputs are extrapolated in time and the reference ET is forecasted accurately.
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Acknowledgments
Computational, storage, and other resources from the Division of Research Computing in the Office of Research (DoRC) and Graduate Studies at Utah State University (USU) are gratefully acknowledged. The authors would like to thank Mr. John Hanks, director of the DoRC at USU, for his help answering questions concerning cluster computing. This research could not have been done without the invaluable support of the Utah Water Research Laboratory (UWRL), as well as information and data from the USGS Landsat Project at the USGS Earth Observation and Science (EROS) Center, Utah Automated Geographic Reference Center. Weather data were accessed through the Community Environmental Monitoring Program monitored by the Desert Research Institute (DRI) of the Nevada System of Higher Education. The authors would also like to acknowledge the Center of Teaching, Research and Learning at the American University (Washington, DC) for providing access to their high-performance computing system (NSF grant BCS-1039497). Appreciation goes to Mrs. Carri Richards for her timely help in editing the paper. The authors thank the editor and the two anonymous reviewers for their valuable comments that helped improving this paper.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 140 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Sep 8, 2013
Accepted: Apr 1, 2014
Published online: Apr 25, 2014
Published in print: Aug 1, 2014
Discussion open until: Sep 25, 2014
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