Sensible heat flux, a key component of the surface energy balance, is difficult to estimate in practice. This study was conducted to see if backpropagation neural networks could estimate sensible heat flux by using horizontal wind speed, air temperature, radiometric surface temperature, net radiation, and time as input. Ground measurements from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE), collected in 1987 and 1989 over a prairie grassland in Kansas, were used for network training and validation. Networks trained on part of the data from a narrow range of space-time coordinates performed well over the other part, with error (root mean square error divided by mean of observations) values as low as 0.24. This indicates the potential in neural networks for linking sensible heat flux to routinely measured meteorological variables and variables amenable to remote sensing. When the networks were tested with data from other space-times, performance varied from good to poor, with average error values around 1.26. This was mainly due to lack of input variables parameterizing canopy morphology and soil moisture, indicating that such variables should be incorporated in the design of future networks intended for large scale applications.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.23765 |
| Date | January 1996 |
| Creators | Abareshi, Behzad |
| Contributors | Schuepp, Peter (advisor) |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Master of Science (Department of Natural Resource Sciences.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001505822, proquestno: MM12152, Theses scanned by UMI/ProQuest. |
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