A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain


  • Alfonso Galdón-Ruíz Universidad Católica San Antonio de Murcia image/svg+xml
  • Guillermo Fuentes-Jaque Universidad de Chile
  • Jesús Soto Universidad Católica San Antonio de Murcia image/svg+xml
  • Luis Morales-Salinas Universidad de Chile




MODIS, land surface temperature, topoclimate, spatial regression models, geographically weighted regression, geostatistical interpolations


Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p<0.01).


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Author Biographies

Alfonso Galdón-Ruíz, Universidad Católica San Antonio de Murcia

Computer and Environmental Engineering Technology Doctorate Program

Guillermo Fuentes-Jaque, Universidad de Chile

Laboratory for Research in Environmental Sciences (LARES), Faculty of Agricultural Sciences ; Master in Territorial Management of Natural Resources, Post-graduate School, Faculty of Agricultural Sciences

Jesús Soto, Universidad Católica San Antonio de Murcia

IT Department

Luis Morales-Salinas, Universidad de Chile

Laboratory for Research in Environmental Sciences (LARES), Faculty of Agricultural Sciences


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