Abstract
Recent studies have shown that the Land Surface Temperature (LST) data measured by Moderate Resolution Imaging Spectro-Radiometer (MODIS) from both the Terra and Aqua platforms can be successfully used for linear regression estimates of daily maximum and minimum air temperatures at a local scale. Incorporation of these estimates into spatial interpolation schemes results in accuracy improvement of the surface air temperature, provided that the correlation coefficient (R) between the air temperature and LST is rather high. The purpose of this work was to examine the importance of pixel resolution (1.0 and 5.0 km2), satellite overpass time, season, land cover type, and the vegetation fraction (depending on the view zenith angle of the MODIS instrument) in controlling the observed level of R. The relative contribution of these factors in producing R variations has been assessed over the state of Mississippi during 2000-2004. Similarly, the sensitivity analysis of the difference between daily maximum and minimum air temperatures and LST to the same factors was performed. Results from these analyses have shown that R and the average difference between temperatures exhibited rather consistent variations depending on the above factors. The difference between maximum air temperature and LST increased linearly with the view angle (having typical range of 1-2°C for angle changes from 0° to ±65°) and remained constant or slightly decreased for daily air minimum temperatures. Both Terra and Aqua 1.0 km2 LST exhibited a small but persistent increase of R between the air temperature and LST as compared with that of using 5.0 km2 LST. Changing from Terra to Aqua LST did not alter significantly estimated values of R. This result suggested that the time difference between the moment of the satellite overpass and the time when maximum or minimum air temperature was observed was not critical for controlling the R value between the air temperature and LST at the involved spatial scales.
Published Version
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