MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach

Aerosol Optical Depth (AOD) across various altitudes is crucial for gaining a comprehensive understanding of aerosol dynamics. However, current methodologies utilizing passive remote sensing and active sensors have limitations in providing precise vertical coverage. In our methodology, we introduce a Seasonal-Independent model employing Machine Learning (ML) algorithms to retrieve AOD values at both 1.5 km and 3 km layers. The propose approach is assessed the performance of various ML algorithms, including XGBoost, Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Remarkably, our study successfully overcame seasonal constraints, yielding impressive R2 values of 0.94, 0.93, 0.93, and 0.87 for the 1.5 km layer, and 0.83, 0.79, 0.82, and 0.78 for the 3 km layer across the mentioned models for 2017, 2018 and 2019 data. Evaluating the proposed Seasonal-Independent XGBoost model against CALIOP AOD values for the 2020 data, we observed substantial agreement with R2 values of 0.93 and 0.81, and minimal RMSE values of 0.002 and 0.004 for the AODs at 1.5 km and 3 km, respectively. Furthermore, a comparative analysis of trends between estimated and CALIOP AODs revealed a strong resemblance in both altitude layers. •Layering MAIAC AOD using ML: Affords challenges of low spatio-temporal CALIOP AOD.•Combined SVD-KFold: Significantly improves model structure, reduces feature dependencies.•‘Lon’ and ‘Lat’ inclusion: Influences ML model results significantly.•ML models: Effectively capture aerosol behavior over Persian Gulf.•Proposed ML models by accuracy: XGBoost > MLP > RF > SVR.