Abstract
Abstract Microplastics (MPs) are considered enormous threats to the environment on a global scale, yet their monitoring in oceanic regions remains challenging due to limited sampling and complex circulation patterns. This study leverages 12 remotely sensed oceanographic variables to predict microplastic abundance (MPA) in the Indian Ocean by developing machine learning (ML) models. Random forest, gradient boosting regression (GBR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbors (KNN), and multiple linear regression (MLR) were the ML models used to train by utilizing 346 georeferenced data. Among the evaluated models, the GBR model demonstrated superior performance during cross-validation, achieving the highest predictive accuracy and model stability (CV R2 = 0.8434, CV RMSE = 0.2424). SHAP analysis revealed that sea surface salinity, surface roughness, dissolved oxygen, distance from the nearest coast, nitrate concentration, and zonal wind stress were among the most influential predictors associated with microplastic distribution, highlighting the importance of hydrodynamic transport, coastal influence, and biogeochemical processes in regulating marine microplastic accumulation. The findings demonstrate that remotely sensed and reanalysis-derived oceanographic variables can provide environmentally informed estimates of MPA across data-limited marine environments. Therefore, this integrated ML-remote sensing approach provides a scalable, cost-effective pollution monitoring system, supporting focused management and policy changes in areas with limited data. Graphical abstract