Split-Window Algorithm for Land Surface Temperature Retrieval From Landsat-9 Remote Sensing Images

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature (BT) at the top of the atmosphere in two adjacent TIR channels and, thus, is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides two-channel TIR images with the same 100-m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this letter, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 to 2.87 K in various regions.

Automated summary

Land surface temperature (LST) is a key parameter in the energy exchange between land surface and atmosphere. Thermal infrared remote sensing is an important method for obtaining LST over a large area. In this study, four split-window algorithms were developed and compared for their accuracy and noise sensitivity under different observation conditions. The proposed algorithm showed higher accuracy and similar spatial distribution compared to the existing Landsat-9 LST products.