Determining LST in Remote Sensing Images and Increasing its Accuracy Using the Fusion of Different Algorithms and Multi-Criteria Decision-Making Methods

Land surface temperature (LST) is one of the important criteria in applications, and its accurate time and place monitoring is considered essential for environmental studies and management as well as planning. Considering the limitations that exist in meteorological stations to determine this necessary parameter, with the help of different algorithms and sensors containing thermal infrared bands, this parameter can be determined on a wide scale. The accuracy of different algorithms for determining the LST using remote sensing images varies in different regions, using different sensors, and so far, no specific algorithm with high accuracy has been considered for all regions. In this article, the aim is to determine the temperature of the LST by using Single channel, Split window, Planck, Mono Window and RTE algorithms, as well as using the fusion method of LST determination algorithms in a weighted and simple way. In the weighted method, the weight of each method is determined with the help of TOPSIS and SAW multi-criteria decision-making algorithms. At the same time as the Landsat 8 satellite passes through the study area, the LST is taken for 25 points. To evaluate the performance of the proposed method of fusion of the LST determination algorithms, the root mean square error (RMSE) statistical criterion is used to make a comparison between the ground measurements and the values calculated by the algorithms. The results show that the algorithm fusion method, where the coefficient of each algorithm is calculated using the TOPSIS multi-criteria decision-making method, has the highest accuracy (RMSE=0.552oK). By using this combined algorithm, more weight is given to more accurate methods. Among the five algorithms separately, the single-channel algorithm has the best accuracy (RMSE=0.5623oK) and the single-window algorithm has the lowest accuracy (RMSE=1.0046ok).