Feasibility of an open-source algorithm for predicting sea surface temperature based on three multi-resolution data sources

The utilization of space borne platforms for the quantification of Sea Surface Temperature (SST) has brought about a major shift in the collection of global information. However, the acquisition of SST through satellite images is limited by its coarse spatial resolution. To address this issue, downscaling algorithms can be implemented to generate matrices with higher spatial resolution. Current research used the SST data source from the MODIS-Aqua sensor at three distinct spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California, Mexico. The original SST images were then downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales using an open-source algorithm. Results indicate a robust linear correlation between the original SST-MODIS data and the modelled data for all spatial resolutions. This study demonstrates the feasibility of utilizing an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.