Unsupervised Classification of Sea Surface Temperature (SST) in the Tropical Atlantic Using Spatial and Functional Data Analysis

In this study, we employ a spatial unsupervised classification technique to analyze the spatio-temporal variability of Sea Surface Temperature (SST) in the tropical African zone. The methodology we propose considers both the spatial dimensions of the data and their functional characteristics, distinguishing it from conventional approaches. The results demonstrate noteworthy fluctuations in SST across spatial and temporal scales. This variability signifies a detected anomaly in SST within the study area, which can be attributed to the impacts of climate change.