Preprint Short Note Version 1 Preserved in Portico This version is not peer-reviewed

Feasibility of an Open-Source Algorithm for Predicting Sea Surface Temperature Based on Three Multi-Resolution Data Sources

Version 1 : Received: 18 February 2022 / Approved: 22 February 2022 / Online: 22 February 2022 (16:07:26 CET)

How to cite: Rodríguez-Sobreyra, R.; Álvarez-Sánchez, L.F.; Flores-De-Santiago, F. Feasibility of an Open-Source Algorithm for Predicting Sea Surface Temperature Based on Three Multi-Resolution Data Sources. Preprints 2022, 2022020281 (doi: 10.20944/preprints202202.0281.v1). Rodríguez-Sobreyra, R.; Álvarez-Sánchez, L.F.; Flores-De-Santiago, F. Feasibility of an Open-Source Algorithm for Predicting Sea Surface Temperature Based on Three Multi-Resolution Data Sources. Preprints 2022, 2022020281 (doi: 10.20944/preprints202202.0281.v1).

Abstract

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Keywords

Sub-pixel mapping; Super-resolution mapping; Downscaling; Gulf of California

Subject

EARTH SCIENCES, Oceanography

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