American Journal of Environmental Science and Engineering 2017; 1(4): 103-109 http://www.sciencepublishinggroup.com/j/ajese doi: 10.11648/j.ajese.20170104.11 Geostatistical Modeling of Air Temperature Using Thermal Remote Sensing Masoud Minaei 1, * , Foad Minaei 2 1 Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran 2 Faculty of Geography, University of Tehran, Tehran, Iran Email address: m.minaei@um.ac.ir (M. Minaei) * Corresponding author To cite this article: Masoud Minaei, Foad Minaei. Geostatistical Modeling of Air Temperature Using Thermal Remote Sensing. American Journal of Environmental Science and Engineering. Vol. 1, No. 4, 2017, pp. 103-109. doi: 10.11648/j.ajese.20170104.11 Received: March 15, 2017; Accepted: May 15, 2017; Published: July 12, 2017 Abstract: Geographic Information Systems and spatial interpolation are the most often used geographic sciences for spatial analysis and visualization of temperature to use in hydrological studies. According to dependency of nature of thermal bands data to temperature, using thermal remote sensing images as auxiliary data can be useful in air temperature spatial interpolation. In light of these considerations, we used Landsat thermal bands together with Kriging and Co-kriging geostatistical methods for four seasons to interpolate mean temperature in Northeast of Iran as a region with low density of gauge distribution. Using Landsat (instead of for instance MODIS) is firstly to provide requirement of mentioned science. Secondly, help to provide deeper understand in case of “climatic neighborhood” concept. To assess the efficiency of the method cross validation indicators were used. Thermal images used in this study increase the accuracy for the winter and autumn in comparison to unused outputs. The provided results for spring and summer were good too. Also, the spatial impacts of thermal images on the results of autumn and spring are significant. This research indicated that using thermal images as auxiliary data have potential to improve spatial prediction accuracy and quality. At the end, we know that number of our observation stations are too low and considering the Kriging requirements like normal distribution and stationarity is toilsome but we should consider that this problem exist in the regions with low density of gauges and should find a way to enhance the air temperature interpolation in these cases. Keywords: Interpolation, Kriging, Thermal Co-Kriging, Golestan, Environmental Studies 1. Introduction A valuable source of information can be provided by remotely sensed data that helping to understand spatial facts and providing authorities and scholars with genuine data sources for better decision making [7]. “Thermal remote sensing is the branch of remote sensing that deals with the acquisition, processing and interpretation of data acquired primarily in the thermal infrared (TIR) region of the electromagnetic (EM) spectrum” [16]. One of the most often used geographic techniques is spatial interpolation. It used for spatial query of properties, spatial data visualization and help to spatial decision-making processes in geography, earth sciences, and environmental science [12]. Indeed, spatial interpolation is often used to calculate a value of an unknown quantity at unmeasured locations with the available measurements at sampled sites” [9], [12]. Moreover, the spatial interpolation also applies for temperature mapping. For land evaluation and characterization systems in hydrological and ecological models, air temperature is one of the input variables [2]. Benavides et al. 2007 and some others e.g. [10] believe that modeling air temperature in topographically rough regions is a challenge and it is strict to obtain accurate climatic maps. Different spatial interpolation methods have been used to model air temperature; the more recently geostatistical models in continuation of former methods like regression analysis, thin-plate smoothing splines (ANUSPLIN), inverse distance interpolation weighting or Voronoi tessellation [2], [17]. The addition of auxiliary variables is often believed to