Location: Soil and Water Management ResearchTitle: Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications Author
Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 6/28/2012
Publication Date: 10/23/2012
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. Interpretive Summary:
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.