Publications

Didari, S; Zand-Parsa, S (2018). Enhancing estimation accuracy of daily maximum, minimum, and mean air temperature using spatio-temporal ground-based and remote-sensing data in southern Iran. INTERNATIONAL JOURNAL OF REMOTE SENSING, 39(19), 6316-6339.

Abstract
In this research, a model was proposed for improving the estimation of air temperature (T-a), which enhanced computation accuracy by combining remote sensing, station data, and spatio-temporal interpolation methods. Stepwise linear regression model was used to find the relationship between daily mean, maximum, and minimum air temperature (T-mean, T-max, and T-min, respectively) with daytime and night-time land surface temperature (LST), normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer sensor, elevation, geometric temperature (T-geom, as a function of the day of the year and latitude), and solar radiation (R-s). Measured T-a from 75 stations in the Fars province in southern Iran were used during 2003-2012. The results of stepwise linear regression showed that among the considered variables daytime and night-time LST, NDVI, T-geom, R-s, and elevation were significant in the final model. For increasing the accuracy of estimation, four interpolation methods were considered and analysed for the residual errors of multiple linear regression model consisting regression kriging, spatio-temporal regression kriging (STRK), regression inverse distance weighting, and spatio-temporal regression inverse distance weighting (STRIDW). The result showed that the STRIDW method had the best accuracy among the considered methods and a significant improvement in the accuracy was achieved with this method comparing to the others. The accuracy of estimations was less than 2 degrees C for T-max, T-min, and T-mean (root mean square error = 1.6 degrees C, 1.84 degrees C, and 1.43 degrees C, respectively) for the validation year (2012). Finally, using the proposed models, it was possible to estimate daily air temperatures in the Fars province with 1 km resolution, which is higher than methods that used purely station-based or purely remote-sensing data.

DOI:
10.1080/01431161.2018.1460500

ISSN:
0143-1161