A Robust Framework for Resolution Enhancement of Land Surface Temperature by Combining Spatial Downscaling and Spatiotemporal Fusion Methods
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Abstract:
Land surface temperature (LST) products with high spatial resolution and short revisiting cycles are crucial for environmental studies. However, due to the tradeoff between spatial and temporal resolutions of satellite observations, such data are not directly available. Spatial downscaling and spatiotemporal fusion methods are existing solutions for this problem, but their robustness is limited under different surface conditions. Here, we propose a Robust Framework for Combining Downscaling and spatiotemporal Fusion methods (RFCDF) to generate the synthesized daily high-resolution LST with high accuracy in different landscapes. RFCDF introduces a novel weighting strategy that determines pixel-level weights using an empirical function under the constraint of the image-level weights of two predictions. We implement the framework using the thermal sharpening algorithm (TsHARP) and Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Moderate Resolution Imaging Spectroradiometer (MODIS) data in Beijing and Baotou, as well as Landsat 8 and simulated coarse resolution imagery in nine sub-regions with different surface landscapes in Beijing. Our results demonstrate that RFCDF can generate more accurate estimations and preserve more spatial details than either individual or combination methods, improving accuracy by 0.1–0.6 K and 0.4–1.3 K in the two study areas, respectively. Moreover, the proposed framework is robust, reducing the root mean square error of estimations by 8-24% under different surface conditions. RFCDF can also generate dense high-resolution LST time series, which is crucial for studying the surface thermal environment at a finer scale.Keywords:
Moderate-resolution imaging spectroradiometer
Robustness
Sensor Fusion
Atmospheric correction
Radiometry
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The purpose of this study is to evaluate the potential of satellite-derived emissivity in middle-wave infrared (MWIR) and long-wave infrared (LWIR) for land surface characterization. We compared emissivities derived from advanced spaceborne thermal emission and reflection radiometer (ASTER; temperature emissivity separation (TES) algorithm; validated data V003) and moderate resolution imaging spectroradiometer (MODIS; two different algorithms, classification-based emissivity method and day–night land surface temperature algorithm; provisional data V003) images acquired over northern Canadian regions. We observed disparities in emissivity dynamic range between each algorithm, and a bias also exists for the MODIS day–night algorithm (–0.02 versus ASTER). Lastly, we related MODIS and ASTER emissivity images with land cover type data derived from MODIS visible and near-infrared observations. Emissivity characteristics were determined for each class encountered. However, we generally observed a significant emissivity spatial heterogeneity inside a single land cover class.
Moderate-resolution imaging spectroradiometer
Spectroradiometer
Land Cover
Radiometry
Atmospheric correction
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The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a high-spatial-resolution multispectral imager on the Terra satellite launched in December 1999. The ASTER thermal infrared (TIR) subsystem has five spectral bands with a spatial resolution of 90 m in the TIR spectral region, which are used for generation of the standard products of surface temperature and surface spectral emissivity. High-resolution surface emissivity at five spectral bands is unique, and is particularly useful for geological mapping. However, the emissivity product is not always easy to use, because (1) its image size is about 60 km square which is not large enough for regional-scale studies, (2) its imaged area is not fixed to the world reference system (WRS) due to a flexible pointing system, and (3) standard atmospheric correction often fails under humid conditions. Thus, in order to improve the usability of the ASTER emissivity product, we are generating land surface emissivity maps in a regional scale by applying improved retrieval algorithms and stack/mosaic processing to an ASTER orthogonal projection dataset which have been produced from the ASTER data archives by the Advanced Industrial Science and Technology (AIST), Japan. In the present paper, we introduce East-Asia land surface emissivity maps as the first result of this project. A comparison study with MODIS monthly emissivity products (MOD11C3) indicates that the generated maps give more reasonable emissivity spectra with higher spatial resolution than the MODIS emissivity products, though the maps have missing pixels in high latitude areas and humid areas.
Radiometry
Spectral bands
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Atmospheric correction
Radiometry
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