Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model

Accurately retrieving and describing subsurface temperature on a large scale can provide valuable information that can be used for subsurface dynamic and variability studies. This study develops a new satellite‐based geographically weighted regression (GWR) model to estimate a subsurface temperature anomaly (STA) in the upper 2,000 m of the Indian Ocean by combining satellite observations (sea surface height, sea surface temperature, sea surface salinity, and sea surface wind) and Argo in situ data (STA). This model improves the estimation accuracy by considering the significant spatial nonstationarity feature between the surface and subsurface parameters in the ocean. The performance of the GWR model is measured by using Akaike Information Criterion combined with root‐mean‐square error and R 2 . The results showed that the proposed GWR model can easily retrieve the STA and outperform the ordinary least squares model. The GWR model can also explain the contribution from each variable via a local regression coefficient distribution. The sea surface height from altimetry is the most significant variable for GWR estimation. This study demonstrates the great potential and advantage of the GWR model for large‐scale subsurface modeling and information retrieving. Thus, we have developed a novel approach for investigating subsurface thermal anomaly and variability from satellite observations.