Accurate high-resolution Marine environment data, especially in the area of low-density sea surface survey devices, is crucial for the planning and management of fishery resources, and can improve the accuracy of fishing situation simulation and prediction. In this study, two classical methods, optimal interpolation (IO) and successive correction (SCM), were used to combine Marine environment data from satellite remote sensing and ocean buoys. Four different algorithms were used to evaluate the deviation correction in remote sensing Marine environment data :(1) mean deviation correction, (2) regression equation, (3) distribution transformation, and (4) spatial transformation. The Marine environment data is provided by NMSDC (national Marine science data center), covering the south China sea. The sea surface temperature data collected from January 2009 to December 2018 were statistically analyzed, and the performance of the two data merging techniques was visually examined and qualitatively compared.