Understanding global climate change patterns and high-risk areas is vital for effective monitoring and forecasting. This study focuses on the assessment of Sea Surface Temperature (SST), a critical factor in climate dynamics, particularly in the delicate Indian Ocean region, which significantly influences the global climate system. We analyze the spatial and temporal patterns of SST from August 2002 to April 2020, encompassing the Arabian Sea to the central Indian Ocean. Utilizing MODIS Aqua Monthly SST data from the Ocean Color platform, we examine seasonal, annual, and intra-annual SST variations. Our analysis includes evaluating anomalies, standard anomalies, coefficients of variation, and time series, employing seasonal autoregressive integrated moving average (SARIMA) modeling for short-term forecasting. Results indicate an overall upward trend in SST, characterized by a bi-modal pattern annually and notable variations in monthly averages. The SARIMA model has effectively predicted SST values up to April 2023. This research addresses five primary concerns: estimating spatio-temporal SST patterns in the Indian Ocean, analyzing normal and standardized anomalies, assessing monthly and yearly variations, applying SARIMA for SST prediction, and detecting climate change signatures through decadal rising patterns. These findings highlight the value of satellite data for monitoring marine climate and supporting decisions in environmental management and fisheries. Regional SST analysis is key to understanding local warming and mitigating ecological risks.

Keywords