Abstract
Many researchers have developed a variety of land surface temperature (LST) inversion algorithms based on satellite data. The main LST inversion algorithms include Radiative Transfer Equation (RTE), Single Channel (SC) algorithm, Mono Window (MW) algorithm, and Split Window (SW) algorithm. In this study, nine LST inversion algorithms were designed using Landsat-8 data and meteorological station data to test the inversion efficiency of different algorithms in different seasons and different locations. The results show that the error of various LST inversion algorithms will increase with the rise of LST. R2 of the inversion results of each LST algorithm and the measured data are all greater than 0.73°C in winter and about 0.5°C in the other seasons. By analyzing the stability of various algorithms inside and outside the city, it is found that the stability of each LST inversion algorithm inside the city is better than that outside the city. For the same surface features, the inversion temperature inside the city is 3–5°C higher than that outside the city. In addition, the sensitivity of various inversion algorithms to parameters was also analyzed. The influence of atmospheric transmittance on RTE, SC, and MW inversion algorithms is in logarithmic form. The effect of emissivity on each algorithm is linear. The influence of NDVI on the algorithms is mainly through the estimation of surface emissivity parameters to affect the inversion results. The effect of ascending radiation on SC (LST4 and LST5) is linear and on RTE (LST1 and LST2) is logarithmic. The effect of downslope radiation on SC and RTE is linear. The influence of atmospheric water vapor content on SW (LST7) is nonlinear.
Highlights
Land surface temperature (LST) is an essential parameter in many research fields, such as ecology, climatology, urban thermal environment, urban heat island, and hydrology [1,2,3]
In order to accurately evaluate the accuracy of the LST inversion algorithm, measured data from 20 meteorological stations in the study area were used to evaluate the stability of the inversion results of different LST inversion algorithms in different seasons
All LST inversion algorithms need these parameters to support the calculation. erefore, it is necessary to explore whether the inversion results of similar surface features with the same reflectivity in densely populated cities and outside cities close to the natural surface will be affected by various inversion algorithms and to what extent. e four selected ground objects (20 points of interest (POIs) for each ground object) were analyzed inside and outside the city
Summary
Land surface temperature (LST) is an essential parameter in many research fields, such as ecology, climatology, urban thermal environment, urban heat island, and hydrology [1,2,3]. E traditional LST measurement method is based on the temperature measurement data of meteorological stations for further spatial interpolation, which is a classical pointbased measurement method [17]. This method has high accuracy, the point-based temperature measurement method cannot represent the regional temperature. Since its launch on February 11, 2013, the newest generation of Landsat-8 has been in orbit for more than eight years It has sent back a large amount of Earth observation data at a rate of 550 images per day. Because of its advantages of Advances in Meteorology high spatial resolution, large scanning field of view, and accessible data, researchers have applied it to further scientific research and ecological construction
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