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Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data |
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| Authors: | Jetse D Kalma Tim R McVicar Matthew F McCabe |
| Institution: | 1. School of Engineering, University of Newcastle, Callaghan, NSW, 2308, Australia 2. CSIRO Land and Water and eWater CRC, GPO Box 1666, Canberra, ACT, 2601, Australia 3. School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW, 2052, Australia |
| Abstract: | This paper reviews methods for estimating evaporation from landscapes, regions and larger geographic extents, with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Particular attention is given to the validation of such approaches against ground based flux measurements. An assessment of some 30 published validations shows an average root mean squared error value of about 50 W m?2 and relative errors of 15–30%. The comparison also shows that more complex physical and analytical methods are not necessarily more accurate than empirical and statistical approaches. While some of the methods were developed for specific land covers (e.g. irrigation areas only) we also review methods developed for other disciplines, such as hydrology and meteorology, where continuous estimates in space and in time are needed, thereby focusing on physical and analytical methods as empirical methods are usually limited by in situ training data. This review also provides a discussion of temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evaporation. Improved temporal scaling procedures are required to extrapolate instantaneous estimates to daily and longer time periods and gap-filling procedures are needed when temporal scaling is affected by intermittent satellite coverage. It is also noted that analysis of multi-resolution data from different satellite/sensor systems (i.e. data fusion) will assist in the development of spatial scaling and aggregation approaches, and that several biological processes need to be better characterized in many current land surface models. |
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