Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman.

Abstract

Air temperature records in remote deserts and inaccessible mountainous regions rely upon data acquired from the nearest meteorological stations, which could be at tens of kilometers apart. The present study provides a reliable approach to extract air temperatures for any distant region using thermal data of satellite images. The study postulates that if there is a strong correlation between land surface temperatures (LST) from satellite images and air temperature records from ground meteorological stations, hence, air temperatures (day/night) could be directly extrapolated from regression equations with high confidence results. Data utilized in this study were obtained from 12 meteorological stations settled and distributed upon different physiographic units of Oman. Satellite images were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product. Regression analysis of max and min air temperatures from weather stations was conducted versus day and night LST from MODIS Aqua LST (MYD11A2) images. Results showed that the regression coefficients for the selected locations are strong for the night/min (R2 = 0.81-0.94) and day/max (R2 = 0.72-0.92) correlations of the 12 stations. The root mean square errors (RMSE) of the statistical models are 0.97 and 1.98 for the night and day temperatures, respectively. Moreover, the association between each pair of the data is significant at the 99% level (p < 0.01). As MODIS data cover large geographic extents, it was possible to produce national diurnal and annual air temperature maps of accurate records with considering the variation of the physiographic setting.