A Practical Landsat-9 Land Surface Temperature Sharpen Method Combining Top-of-Atmosphere Multispectral and Panchromatic Reflectance

The thermal infrared (TIR) band of the Landsat-9 satellite has a spatial resolution of 100 meters, which is coarser than the optical multispectral bands of the 30-meter resolution and the panchromatic band of the 15-meter resolution. Existing image fusion algorithm studies primarily focuses on multispectral and panchromatic bands. For the TIR band with the lowest spatial resolution, although Landsat-9 land surface temperature (LST) products are resampled to 30m using the Cubic algorithm to match the multispectral bands, the physical mechanism of sharpening methods remains unclear, and they fail to fully leverage the higher spatial resolution advantage provided by the panchromatic band. This study proposes a practical LST sharpening method (TOA-MsPS) by integrating the correlation between top-of-atmosphere TIR brightness temperature (BT) and the fused reflectance of Multispectral and Panchromatic bands. The TOA-MsPS method comprises three steps: panchromatic and multispectral band fusion, correlation modeling of reflectance and BT, and LST end-to-end retrieval. Compared to existing methods, the TOA-MsPS method sharpens the spatial resolution of both TIR band BT images to 15 m without relying on external parameters, simultaneously deriving the LST data. All input data for the method consists of remote sensing observations at the TOA, reducing external parameter uncertainties and error propagation inherent in the preprocessing steps of current LST retrieval algorithms, such as atmospheric correction, resampling, and emissivity estimation. Qualitative visual interpretation based on visual inspection indicate that TOA-MsPS derived LST images exhibit significantly enhanced detail and reasonable local spatial distribution. Quantitative validations using ground site measurements demonstrate that the sharpened LST images achieve comparable accuracy to Landsat-9 LST products while substantially improving spatial resolution, with root mean square errors of 2.45K (LST product) and 2.35K (LST sharpen), respectively. Furthermore, the TOA-MsPS method can be directly applied to remote sensing data from multiple other remote sensing data sources, further enhancing the precision of long-term land surface thermal radiance monitoring.