Downscaling remotely sensed land surface temperatures: A comparison of typical methods
遥感地表温度降尺度方法比较————性能对比及适应性评价

Remotely sensed Land Surface Temperatures (LSTs) usually have low spatial resolutions. Downscaling is an effective technique to enhance the spatial resolutions. Current methods for downscaling remotely sensed LSTs were summarized. Using satellite data, we made an inter-comparison among three typical methods, including the Normalized Difference Vegetation Index (NDVI) method, the Pixel Block Intensity Modulation (PBIM) method, and the Linear Spectral Mixture Model (LSMM) method. We further designed an index, Co-Occurrence Root Mean Square Error (CO-RMSE), for measuring the textural similarity in inter-comparisons. Results indicate that (1) the performance of the NDVI method is most affected by the season, followed by the PBIM method; (2) the performance of the LSMM method is most influenced by the spatial resolution; the NDVI method has an advantage over the PBIM method at high resolutions, while at low resolutions, the performance of the PBIM method is better than that of the NDVI method; (3) these three methods are suitable for areas with combination of vegetation and bare ground, areas with varied topography and albedo, and areas with distinct LST differences in different classes, respectively; (4) the NDVI method is the easiest to implement, while the LSMM method is the most difficult. Further analysis showed that scale factor is the key issue to the LST downscaling and it needs to be carefully selected regarding the season, spatial resolution, land cover, application and the operability.