Estimating air surface temperature in Portugal using MODIS LST data A. Benali a, d, ⁎, A.C. Carvalho a , J.P. Nunes b , N. Carvalhais a, c , A. Santos a a CENSE, Faculty of Science and Technology, New University of Lisbon, Portugal b CESAM & Dept. Environment and Planning, University of Aveiro, Portugal c Max Planck Inst Biogeochem, D-07701 Jena, Germany d Forest Research Centre, School of Agriculture, Technical University of Lisbon, Portugal abstract article info Article history: Received 12 October 2011 Received in revised form 26 April 2012 Accepted 28 April 2012 Available online 6 June 2012 Keywords: Average air temperature MODIS Remote sensing Land surface temperature LST Statistical modeling Bootstrap Jackknife Portugal Air surface temperature (T air ) is an important parameter for a wide range of applications such as vector- borne disease bionomics, hydrology and climate change studies. Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of T air at both regional and global scales. Some studies have tried to derive maximum (T max ), minimum (T min ) and average air temper- ature (T avg ) using different methods, with variable estimation accuracy; errors generally fall in the 2–3 °C range while the level of precision generally considered as accurate is 1–2 °C. The main objective of this study was to accurately estimate T max ,T min and T avg for a 10 year period based on remote sensing—Land Surface Temperature (LST) data obtained from MODIS—and auxiliary data using a statistical approach. An optimization procedure with a mixed bootstrap and jackknife resampling was employed. The statistical models estimated Tavg with a MEF (Model Efficiency Index) of 0.941 and a RMSE of 1.33 °C. Regarding T max and T min , the best MEF achieved was 0.919 and 0.871, respectively, with a 1.83 and 1.74 °C RMSE. The developed datasets provided weekly 1 km estimations and accurately described both the intra and inter annual temporal and spatial patterns of T air . Potential sources of uncertainty and error were also analyzed and identified. The most promising developments were proposed with the aim of developing accurate T air estimations at a larger scale in the future. © 2012 Elsevier Inc. All rights reserved. 1. Introduction Air temperature, typically measured at the shelter height 2 m above ground (hereafter T air ), is a key variable in a wide range of environmen- tal applications including vector-borne disease bionomics (e.g. Kuhn et al., 2002), terrestrial hydrology (e.g. Chow et al., 1988), biosphere processes (e.g. Prince & Goward, 1995) and climate change (e.g. IPCC, 2007). Depending on the scale, the spatiotemporal T air patterns can be highly variable and complex due to the heterogeneity of the environmental factors that control the energy balance of the land–atmosphere system. Solar radiation is the main energy influx and the total amount reaching the Earth's surface is determined by factors, such as, (i) latitude, which determines the relative position of the sun influencing day length, thus, the distribution of total incoming solar radiation throughout the year; (ii) cloud cover and (iii) particulate matter in the atmosphere (e.g. Jacobson, 2000). The energy of the earth–atmosphere system is balanced by the absorption of incoming solar radiation, in the shortwave part of the light spectrum, and the emission of infrared long‐wave radiation and the sensible and latent heat loss fluxes (Jin & Dickinson, 2010; Prihodko & Goward, 1997). These processes impose the surface heating and cooling process which are the main modulators of the T air daily cycle (Ahrens, 2003). Meteorological measurements provide accurate temporally dis- crete T air information but have limited ability to describe its spatial heterogeneity over large areas of the Earth. T air measurements are frequently interpolated with significant errors associated and often lead to unrepresentative spatial patterns (Willmott & Robeson, 1995). Furthermore, the complexity associated with the correct esti- mation of T air patterns increases with increased temporal resolution and when temperature extremes are the target objectives (Geiger, 1965; Vogt et al., 1997). Regardless of the method, interpolation accu- racy is highly dependent on station network density and the scale of spatial and temporal variability of the parameter (Vancutsem et al., 2010; Vogt et al., 1997). Interpolation errors generally range from 1 to 3 K depending on the spatial and temporal scale, the temperature parameter and the techniques employed (Anderson, 2002; Mostovoy et al., 2006; Vogt et al., 1997). The use of remote sensing data can greatly improve the estimation of T air spatiotemporal patterns thus improving the knowledge of both Remote Sensing of Environment 124 (2012) 108–121 ⁎ Corresponding author at: Forest Research Centre, Tapada da Ajuda 1349-017 Lisboa, Portugal. Tel.: + 351 964291869; fax: + 351 213653338. E-mail address: aklibenali@gmail.com (A. Benali). 0034-4257/$ – see front matter © 2012 Elsevier Inc. 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