Optimal Site Selection for Aquaculture Using Spatial Analysis of Satellite Data (Case Study: Persian Gulf and Gulf of Oman)

Aquaculture has become a key strategy in meeting the growing global demand for animal protein, especially as natural fish stocks face overexploitation and ecological degradation. Among modern aquaculture methods, cage farming stands out for its cost-effectiveness, environmental sustainability, and potential to boost regional economies. In this context, the current study focuses on identifying optimal sites for the development of sea bass (Lates calcarifer) cage aquaculture in the southern coastal waters of Iran, particularly in the Persian Gulf and the Gulf of Oman. The study employs satellite remote sensing data, geospatial analysis, and spatial multi-criteria evaluation (SMCE) to propose a systematic approach for aquaculture site selection. To achieve this objective, data were collected from environmental and oceanographic multiple satellite and modeling sources, including MODIS (for temperature and dissolved oxygen), SMOS (for salinity), GEBCO (for bathymetric depth), and CMEMS (for variables such as chlorophyll concentration, pH, nitrate, phosphate, sea surface height, and wind speed). All data were obtained for the year 2024 and processed using the Kriging interpolation method to create continuous raster layers with a spatial resolution of one kilometer. The study identifies ten criteria, crucial for sea bass aquaculture: depth, water temperature and salinity, wind speed, sea surface height, chlorophyll-a concentration, pH, dissolved oxygen, nitrate, and phosphate levels. These variables were selected based on biological needs of sea bass and insights from prior studies. For each parameter, suitable ranges were defined—such as water depth between 20–50 meters, salinity between 8.8–39 ppt, and surface temperature between 13–28°C—ensuring conditions that support optimal growth and health of farmed fish. Each criterion was classified into three suitability levels (high, moderate, and low) and then standardized using a ranking method. The relative importance of each criterion was determined using the Analytic Hierarchy Process (AHP), a well-established decision-making framework. Through pairwise comparisons and consistency checks (with a consistency ratio of 0.076), the study assigned the highest weight to depth (0.3342), followed by temperature (0.2061) and salinity (0.1216), while phosphate received the lowest weight (0.0277). Using a Weighted Linear Combination (WLC) approach, the standardized criteria layers were integrated to generate a final suitability map. The results were categorized into three classes—highly suitable, moderately suitable, and unsuitable—for sea bass cage farming. Spatial analysis revealed that 15 counties along the southern coast of Iran hold significant potential for sea bass cage aquaculture. Among them, Gachsaran, Bushehr, Tangestan, Ganaveh, Qeshm, Bandar Abbas, and Minab showed the highest suitability scores. To validate the model, the study compared the identified suitable zones with actual cage farming sites reported by Iran’s Fisheries Organization and the species,’ recorded habitat range from the OBIS (Ocean Biodiversity Information System) database. The overlap between current farms and high-scoring zones confirmed the reliability of the methodology. Furthermore, areas with high suitability also aligned well with recorded sea bass presence in global biodiversity datasets. This research underscores the significant potential of remote sensing and geospatial analysis in aquaculture planning. By leveraging freely available satellite datasets and integrating them into a structured spatial decision-making framework, the study presents a replicable and scalable method for aquaculture site selection. The approach significantly reduces the need for time-consuming and costly field surveys, while also enabling wide-area assessments that account for environmental variability. In addition to environmental considerations, the study also highlights economic and operational benefits. The proposed locations support optimal growth conditions, reducing the risks of fish mortality, disease, or operational failure. Moreover, by identifying regions with low concentrations of pollutants (e.g., nitrate and phosphate) and stable physical conditions (e.g., low wind and wave height), the model aids in promoting sustainable aquaculture practices. In conclusion, the integration of satellite-based environmental data, spatial interpolation techniques, and multi-criteria decision-making tools offers a robust solution for site selection in marine aquaculture. Given the increasing global emphasis on food security, environmental sustainability, and efficient resource use, such methodologies can play a pivotal role in guiding policy and investment in the aquaculture sector—especially in coastal regions like southern Iran, where natural conditions are favorable for fish farming.