ORIGINAL PAPER Estimating mean air temperature using MODIS day and night land surface temperatures Hao Sun & Yunhao Chen & Adu Gong & Xiang Zhao & Wenfeng Zhan & Mengjie Wang Received: 11 January 2013 /Accepted: 13 October 2013 /Published online: 28 November 2013 # Springer-Verlag Wien 2013 Abstract Near surface air temperature (Ta) values measured by weather stations provide limited information about spatial patterns over a wide area. Remote sensing is a promising technology for providing a more accurate description of spatial variations in the Ta on both a regional and global scale. This paper presents a new approach for estimating mean Ta using a combination of MODIS day–night land surface temperatures (Ts) and enhanced vegetation index (EVI) data (called DTVX method). The advantages of the DTVX method include complete independence of ancillary data and non- contextual characteristics that circumvent the limitations inherent to the contextual temperature–vegetation index (TVX) method. Three land areas covered by the MODIS sinusoidal tile h26v04, h26v05, and h27v05 were selected as test areas because their terrain elevations vary in the range of several decameters to several kilometers. Based on the DTVX method, Terra MODIS 8-day day–night Ts and 16-day EVI products were used to obtain an 8-day average Ta from May 2010 to June 2011 (a total of 51 8- day periods). The daily average Ta values measured at 314 weather stations in the three tiles were utilized as the in-situ reference data. An RMSE value of 1.84 K with R 2 of 0.97 was observed for the Ta estimation in the h27v05 tile (plain area). In the case of the h26v05 tile (high mountain area), an RMSE of 2.45 K with R 2 of 0.96 was observed, whereas the h26v04 tile (mountain area) exhibited an RMSE of 2.34 K with an R 2 of 0.989. The RMSE of the three tiles was 2.23 K with an R 2 of 0.977. Sensitivity analysis indicates that the method developed herein is elastic with respect to the accuracy of day–night Ts and EVI data in areas with a greater EVI and day–night Ts difference, such as dense forests and high mountain areas. 1 Introduction Accurate estimation of spatially distributed near surface air temperature (Ta) is important for studying the land surface energy balance (House-Peters and Chang 2011; Savage et al. 2009), terrestrial evapotranspiration (Li et al. 2009; Wang and Dickinson 2012), climate change (IPCC 2007), disease spread (DeVisser et al. 2010), and living environments (Liu and Weng 2012). Generally, Ta data are recorded by weather stations located approximately 2 m above ground. The weather station measurements exhibit high temporal resolution and accuracy and are thus among the most significant resource data for studying climate change. However, due to the heterogeneity inherent to various environmental factors that regulate the H. Sun : Y. Chen (*) : A. Gong : M. Wang State Key Laboratory of Earth Surface Processes and Resource Ecology (Beijing Normal University), College of Resources Science & Technology, Beijing Normal University, No.19, Xinjiekouwai Street, Beijing 100875, China e-mail: cyh@bnu.edu.cn A. Gong Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China X. Zhao College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China W. Zhan Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute of Earth System Science, Nanjing University, Nanjing 210093, China W. Zhan State Key Laboratory for Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China Theor Appl Climatol (2014) 118:81–92 DOI 10.1007/s00704-013-1033-7