The study aimed to identify the most relevant variable from remote sensing data that may be utilized to forecast species-specific fish availability. Because of the schooling habits and reliance on surface productivity, the Indian mackerel, Rastrelliger kanagurta, has been suggested as a prospective species for such investigations in tropical waters. The current study showed how a geographic database can aid in focusing attention on key oceanic characteristics that can serve as the most dependable predictors of fish abundance. Generalized Additive Model (GAM) on the GIS platform was used to analyze satellite-derived Chlorophyll (Chl.) data, Sea Surface Temperature (SST) data and geo-referenced catch weights of Indian Mackerel. When comparing moderate and low catch weighting to high catch weighting, the Chl. content was highly significant. SST was important when the catch weighting was high, but not when the catch weighting was moderate or low.

Remote sensing, GIS, GAM Model, Indian mackerel, Rastrelliger kanagurta, species distribution analysis