Abstract
To improve the effectiveness of thermal sharpening in mountainous regions, paying more attention to the laws of land surface energy balance, a thermal sharpening method based on the geographical statistical model (GSM) is proposed. Explanatory variables were selected from the processes of land surface energy budget and thermal infrared electromagnetic radiation transmission, then high spatial resolution (57 m) raster layers were generated for these variables through spatially simulating or using other raster data as proxies. Based on this, the local adaptation statistical relationship between brightness temperature (BT) and the explanatory variables, i.e., the GSM, was built at 1026-m resolution using the method of multivariate adaptive regression splines. Finally, the GSM was applied to the high-resolution (57-m) explanatory variables; thus, the high-resolution (57-m) BT image was obtained. This method produced a sharpening result with low error and good visual effect. The method can avoid the blind choice of explanatory variables and remove the dependence on synchronous imagery at visible and near-infrared bands. The influences of the explanatory variable combination, sampling method, and the residual error correction on sharpening results were analyzed deliberately, and their influence mechanisms are reported herein.
Highlights
Satellite-based thermal infrared remote sensing is the main method used to acquire distributed land surface thermal information
The spatial resolution of thermal infrared imageries collected by the moderate resolution imaging spectroradiometer (MODIS) and the advanced very high resolution radiometer (AVHRR) is ∼1000 m, and the resolution of thermal infrared images collected by the Geostationary Operational Environmental Satellite Imager is 4000 m
From May 2003 to the present, about a quarter of the data in ETM+ images are missing because of a scan line corrector (SLC) failure, but the remaining three quarters are accurate. Using these data to evaluate the thermal image sharpening effect creates a new application of these data. (As a basic study, this paper used the ETM+ data before the SLC failure; the approach developed in such a way is likewise suitable for postSLC-failure data)
Summary
Satellite-based thermal infrared remote sensing is the main method used to acquire distributed land surface thermal information. The spatial resolution of thermal infrared imageries collected by the moderate resolution imaging spectroradiometer (MODIS) and the advanced very high resolution radiometer (AVHRR) is ∼1000 m, and the resolution of thermal infrared images collected by the Geostationary Operational Environmental Satellite Imager is 4000 m. Such low spatial resolution significantly restricts the usefulness of these images in drought monitoring and other agricultural and forestry practices,[1,2,3,4] in mountainous regions with complex landscape. Sharpening is an effective method used to solve the problem of low spatial
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