Estimating daily maximum and minimum air temperatures by remote sensing and GIS

Air temperature is an important meteorological factor widely used in the evaluation of global climate change, environmental analysis, and disaster early-warning. With advances in satellite remote sensing technology, air temperature estimation tends to utilize remote sensing data or a combination of remote sensing and GIS. MODIS land surface parameter products and air temperature data in 2013 from 36 automated meteorological stations were used to estimate daily maximum and minimum air temperatures in Zhejiang province, by multiple linear regression (MLR) (variables include land surface temperature, normalized difference vegetation index, surface albedo, longitude, latitudes, elevation), temperature-vegetation index (TVX) and multiple linear regression interpolation (MLRI). R2 of MLR, TVX and MLRI for maximum air temperature were found to be 0.96, 0.91, 0.97, RMSE were 1.84, 2.75, 1.49℃ respectively. R2 of MLR and MLRI for minimum air temperature were 0.87, 0.91, RMSE were 3.33, 2.93 ℃ respectively. MLRI performed the best. Spatial patterns indicated that the MLRI method could better reflect temperature differences due to topography in areas with large elevation ranges.