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- W2053683629 abstract "A computational framework to generate dai- ly temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measure- ments 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, eleva- tion, 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 spatio-temporal auto-correlation; sum-metric sepa- rable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical com- puting. The results show that the space-time regression" @default.
- W2053683629 created "2016-06-24" @default.
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- W2053683629 date "2011-07-08" @default.
- W2053683629 modified "2023-10-01" @default.
- W2053683629 title "Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images" @default.
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- W2053683629 doi "https://doi.org/10.1007/s00704-011-0464-2" @default.
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