

Hengl, T; Heuvelink, GBM; Tadic, MP; Pebesma, EJ (2012). Spatiotemporal prediction of daily temperatures using timeseries of MODIS LST images. THEORETICAL AND APPLIED CLIMATOLOGY, 107(2Jan), 265277. Abstract A computational framework to generate daily temperature maps using timeseries of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatiotemporal autocorrelation; summetric separable variograms were fitted to account for zonal and geometric spacetime anisotropy. The final predictions were generated for timeslices of a 3D spacetime cube, constructed in the R environment for statistical computing. The results show that the spacetime regression model can explain a significant part of the variation in stationdata (84%). MODIS LST 8day (cloudfree) images are unbiased estimator of the daily temperature, but with relatively low precision (+/ 4.1A degrees C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10fold crossvalidation show that use of spatiotemporal regressionkriging and incorporation of timeseries of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was +/ 2.4A degrees C. The regressionkriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancementinteractive spacetime variogram exploration and automated retrieval, resampling and filtering of MODIS imagesare anticipated. DOI: 0177798X ISSN: 10.1007/s0070401104642 