Abstract. This study utilizes remote sensing and geospatial techniques to model the habitat suitability of seagrass ecosystems on Santiago Island, Pangasinan, Philippines. Sea surface temperature (SST), salinity, and bathymetry were derived from Landsat 8, Landsat 9, and Sentinel-2 images using various techniques and were used as input for seagrass habitat suitability modeling. Results showed that seagrasses thrive best at depths of 9–23 m, with suitability decreasing in shallower (0–1 m) and deeper waters (>30 m). Optimal salinity was between 17.5–22.5 PSU (Practical Salinity Unit), while SST of 25.3°C or lower supports seagrass growth. The habitat suitability model classified only 1.38% of the area as highly suitable and 20.57% as suitable, while 5.32% and 4.66% were less suitable and moderately suitable, respectively, with the majority (68.06%) falling under the least and not suitable categories. Validation using reference points and field data showed that the model shows moderate reliability. Accuracy reached 62.55% using 2013 seagrass occurrence data, and 63.45% using 2023 data. This improved to 76.71% and 67.75% when moderately suitable areas (suitability score of 50) were included. Overall, the findings highlight the ecological importance of seagrass meadows and demonstrate that remote sensing offers a scalable, cost-efficient approach for monitoring seagrass ecosystems, supporting conservation and policy development in the Philippines.
Amolato et al. (2026) studied this question.