| Sea surface temperature is an important parameter in the study of climate change and has great research significance. In order to select the suitable temperature inversion algorithm for the study of offshore waters, this paper compares and analyzes the inversion of sea surface temperature inversion algorithms including the radiative transfer model (RTM), the mono-window model (MW), the single channel model (SC), the linear split window model (SW1) and the non-linear split window model (SW2)based on Landsat8 satellite remote sensing data, using the Beibu Gulf waters as the study area. The sensitivity analysis was also performed. The split-window covariance-covariance ratio method（SWCVR）is also used to invert the atmospheric water vapour content data, reducing the dependence on external data in the temperature inversion process. The results show that: 1) The SWCVR method based on Landsat8 TIRS data for atmospheric water vapor content inversion is better, with an error of about 0.5 g/cm2 ；2)the accuracy of the SW2 and SC algorithms is higher compared with the measured SST data, with an error of about 0.6K; the RTM and SW1 algorithms are second, with an error of about 1.6K and 1.9K; the MW algorithm is less accurate, with an error of about 2.5K; 3) the accuracy of the two split-window algorithms is higher compared with the AVHRR SST product, with an error of about 1K and 1.3K; the accuracy of SC algorithm is slightly lower than that of split-window algorithm, with errors of about 1.4K, and the RTM and MW algorithms are less accurate, with errors of about 2K and 3K; 4) The SW2 algorithm has the lowest sensitivity to parameters followed by the SC algorithm, SW1 algorithm and MW algorithm, and the RTM algorithm has the highest sensitivity. |
