Authors / CoAuthors
Li, F. | Jupp, D.L.B. | Thankappan, M. | Wang, L. | Sixsmith, J. | Lewis, A. | Held, A.
Abstract
Abstract: Land Surface Temperature (Ts) is an important boundary condition in many land surface modelling schemes. It is also important in other application areas such as, hydrology, urban environmental monitoring, agriculture, ecological and bushfire monitoring. Many studies have shown that it is possible to retrieve Ts on a global scale using thermal infrared data from satellites. Development of standard methodologies that generate Ts products routinely would be of broad benefit to the application of remote sensing data in areas such as hydrology and urban monitoring. AVHRR and MODIS datasets are routinely used to deliver Ts products. However, these data have 1km spatial resolution, which is too coarse to detect the detailed variation of land surface change of concern in many applications, especially in heterogeneous areas. Higher resolution thermal data from Landsat is a possible option in such cases. To derive Ts, two scientific problems need to be resolved: to remove the atmospheric effects and derive surface brightness temperature (TB) and to separate the emissivity and Ts effects in the surface brightness temperature (TB). To derive TB, for single thermal band sensors such as, Landsat 5, 7 and (due to a faulty dual-band thermal instrument) on Landsat-8, the split window methods, such as those used for NOAAAVHRR data (Becker & Li, 1990), and the day/night pairs of thermal infrared data in several bands, as used for MODIS (Wan et al., 2002) are not available for correcting atmospheric effects. The retrieval of surface brightness temperature TB from Landsat data therefore needs more care, as the accuracy of the TB retrieval depends critically on the ancillary data, such as atmospheric water vapour data (precipitable water). In this paper, a feasible operational method to remove the atmospheric effects and retrieve surface brightness temperature from Landsat data is presented. The method uses the MODTRAN 5 radiative transfer model and global atmospheric profile data sets, such as NASA MERRA (The Modern Era Retrospective-Analysis for Research and Applications) atmospheric profiles, NOAA NCEP (National Center for Environmental Prediction) reanalysis product and ECMWF (The European Centre for Medium-Range Weather Forecasts) to correct for the atmospheric effects. The results derived from the global atmospheric profiles are assessed against the TB product estimated by using (accurate) ground based radiosonde data (balloon data). The results from this study have found: The global data sets NCEP1, NCEP2, MERRA and ECMWF can all generally give satisfactory TB products and can meet the levels of accuracy demanded by many practitioners, such as 1º K. Among global data sets, ECMWF data set performs best. The root mean square difference (RMSD) for the 9 days and 3 test sites are all within 0.4º K when compared with the TB products estimated using ground radiosonde measurements.
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nonGeographicDataset
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83162
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- External PublicationConference Paper
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Environmental Sciences
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- Published_Internal
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2015-01-01T00:00:00
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