Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation Marina Stathopoulou ⁎, Constantinos Cartalis Department of Physics, Division of Environmental Physics and Meteorology, University of Athens, University Campus, Building PHYS-V, GR 157 84, Athens, Greece abstract article info Article history: Received 19 March 2009 Received in revised form 23 June 2009 Accepted 20 July 2009 Keywords: AVHRR Downscaling LST Urban area SUHI intensity Surface urban heat island (SUHI) is a phenomenon of both high spatial and temporal variability. In this context, studying and monitoring the SUHIs of urban areas through the satellite remote sensing technology, requires land surface temperature (LST) image data from satellite-borne thermal sensors of high spatial resolution as well as temporal resolution. However, due to technical constrains, satellite-borne thermal sensors yield a trade-off between their spatial and temporal resolution; a high spatial resolution is associated with a low temporal resolution and vice versa. To resolve this drawback, we applied in this study four downscaling techniques using different scaling factors to downscale 1-km LST image data provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor, given that AVHRR can offer the highest temporal resolution currently available. The city of Athens in Greece was used as the application site. Downscaled 120-m AVHRR LSTs simulated by the downscaling techniques, were then used for SUHI intensity estimation based on LST differences observed between the main urban land covers of Athens and the city's rural background. For the needs of the study, land cover information for Athens was obtained from the Corine Land Cover (CLC) 2000 database for Greece. Validation of the downscaled 120-m AVHRR LSTs as well of the retrieved SUHI intensities was performed by comparative analysis with time-coincident observations of 120- m LST and SUHI intensities generated from the band 6 of the Thermal Mapper (TM) sensor onboard the Landsat 5 platform. The spatial pattern of the downscaled AVHRR LST was found to be visually improved when compared to that of the original AVHRR LST and to resemble more that of TM6 LST. Statistical results indicated that, when compared to 120-m TM6 LST, the root mean square error (RMSE) in 120-m AVHRR LST generated by the downscaling techniques ranged from 4.9 to 5.3 °C. However, the accuracy in SUHI intensity was found to have significantly improved, with a RMSE value decreasing from 2.4 °C when the original AVHRR LST was utilized, down to 0.94 °C in case that downscaling was applied. © 2009 Elsevier Inc. All rights reserved. 1. Introduction When referring to land observation from space, LST is the physical parameter of prime importance to be estimated, as it is required for a wide variety of scientific studies. In urban climatology, LST is the key input parameter used in algorithms to make estimates of net radiation, sensible and latent heat flux, thermal inertia, SUHI intensity, precipitable water, evapotranspiration, urban-induced surface runoff and surface moisture. Related studies are reviewed in Voogt and Oke (2003), Arnfield (2003), Gamba et al. (2005) and Stathopoulou and Cartalis (2007a). Satellite observations of LST have also been used successfully for urban landscape management (Quattrochi et al., 2000), urban environmental quality monitoring (Nichol & Wong, 2005) and urban risk analysis (Dousset et al., 2007). To gain knowledge about LST over urban areas from space, satellite- based sensors operating in the Thermal InfraRed (TIR) spectral region are used as they provide LST image data at various spatial and temporal scales. However, due to technical constrains, satellite thermal sensors are unable to supply both spatially and temporally dense LST image data. The reason for this is that the spatial and temporal resolutions of a satellite thermal sensor are anti-correlated, meaning that a high spatial resolution is related with a low temporal resolution and vise versa. Thus, while some of the current satellite-borne thermal sensors (Table 1), such as the Landsat 7 ETM+, the TERRA ASTER and the Landsat 5 TM, can provide LST at a high spatial resolution (≤120 m), their utilization in urban climate studies is restricted because of limited available night- time image data and low temporal resolution. On the other hand, among the most commonly used operational satellite thermal sensors with a low spatial resolution (≥1 km), such as the AVHRR, the MODIS, the AATSR and the ATSR, AVHRR is the only one that offers the highest temporal resolution, providing temporally dense LST image data of an observed area twice daily. In fact, this revisit time can be further increased up to 4 times per day by acquiring AVHRR images from the pair of NOAA satellites that orbit the Earth. Due to resolution trade-off, the spatially dense LST observations needed for many urban applications can only be provided at large time Remote Sensing of Environment 113 (2009) 2592–2605 ⁎ Corresponding author. Tel.: +30 210 7276843; fax: +30 210 7276774. E-mail address: mstathop@phys.uoa.gr (M. Stathopoulou). 0034-4257/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2009.07.017 Contents lists available at ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse