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United States Department of Agriculture

Agricultural Research Service

Research Project: IMPROVING WATER PRODUCTIVITY AND NEW WATER MANAGEMENT TECHNOLOGIES TO SUSTAIN RURAL ECONOMIES

Location: Soil and Water Management Research

Title: Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

Authors
item Cemek, Bilal -
item Koksal, Selim -
item Cetin, Sakine -
item Howell, Terry
item Gowda, Prasanna

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: June 28, 2012
Publication Date: October 23, 2012
Citation: Cemek, B., Koksal, S.E., Cetin, S., Howell, T.A., Gowda, P. 2012. Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. 2012 CDROM. Page No. 204-12.

Technical Abstract: Remote sensing based evapotranspiration (ET) mapping is an important improvement for water resources management. Hourly climatic data and reference ET are crucial for implementing remote sensing based ET models such as METRIC and SEBAL. In Turkey, data on all climatic variables may not be available for each hour at all locations either due to cost constraints or due to equipment malfunctions. In this study, the Artificial Neural Network (ANN) technique was used to estimate missing hourly climatic data and reference ET for the semi-humid Bafra Plains, located in northern Turkey. Modeled and measured climatic and reference ET were used to derive ET maps from Landsat Thematic Mapper data acquired on February 09, 2009 and April 08, 2010. Results indicated that the climatic data and reference ET estimated through ANN could be useful for mapping ET where climatic data was missing or not available.

Last Modified: 12/20/2014
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