Presented at the 2017 International Geoscience and Remote Sensing SymposiumAlbacore tuna (Thunnus alalunga) is one of the important commercial species of the longline fishery in the southern Indian Ocean (SIO). The satellite-based oceanographic data of net primary production (NPP), sea-surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), sea-surface height (SSH) and eddy kinetic energy (EKE), were used to evaluate the effects of oceanographic conditions on the hotspot habitat for Albacore tuna and to explore the spatial variability of these features in the SIO using the generalized additive model (GAM) and maximum entropy models (MaxEnt). The results from the Maxent and GAM revealed its potential for predicting the spatial distribution of Albacore tuna and highlight the use of multispectral satellite images for describing habitats. In these two models, the spatial habitat patterns were explained predominantly by SST (17-21°C) and indicated that SST is the most influential factor in the geographic distribution of Albacore tuna. Hoptspot habitat formation were also possibly related to the MLD (60-120 m), NPP (250-450 mg C/m2d1) and SSH (0.4-0.6 m).