Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment.

Hua Liu,Qihao Weng

doi:10.3390/s18114058

Abstract

There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations.

Highlights

Land surface temperature (LST) is a primary factor of land-atmosphere energy exchange and is an important variable of urban thermal behavior and dynamics [1]
Thermal infrared satellite imagery is an efficient source of land surface temperatures (LST) retrieval and numerous algorithms have been developed based on satellite and airborne sensors, e.g., Landsat Enhanced Thematic Mapper Plus (Landsat ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Very High
There are some popular image fusion approaches: (1) Intensity-Hue-Saturation (IHS) method, which transfers a multi-band image from Red Green Blue (RGB) to IHS mode and creates an IHS fused new image [12]; (2) Principal Component Analysis (PCA) that converts correlated multispectral bands into uncorrelated components and generates fused panchromatic image with high resolution [13];(3) arithmetic algorithms, e.g., Brovey Transform integrating multispectral bands and high-resolution panchromatic channel with a set of multiplication and division operations [14]; (4) wavelet approach, which links high-resolution panchromatic data with low-resolution multispectral band based on a reverse wavelet conversion with specific wavelet coefficients [15]; and (5) statistics-based fusion that applies statistical approaches to assess the relationship among input spectral bands and evaluate the influences of individual bands to the final fused image [16,17,18,19]

Summary

Introduction

Land surface temperature (LST) is a primary factor of land-atmosphere energy exchange and is an important variable of urban thermal behavior and dynamics [1]. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is an original and typical example of statistics-based fusion algorithm that simulates shortwave surface reflectance images based on observed Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance images [16]. There is another model called Spatio-Temporal Adaptive

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Methods

Results