Assessing the Impact of T-Mart Adjacency Effect Correction on Turbidity Retrieval from Landsat 8/9 and Sentinel-2 Imagery (Case Study: St. Lawrence River, Canada)

In inland waters, Atmospheric Correction (AC), including Adjacency Effect (AE) correction, is a major challenge for water quality retrieval using optical satellite data. This study evaluated three image pre-processing options for turbidity retrieval in the St. Lawrence River using Sentinel-2 (S2) and Landsat 8/9 (L8/9) imagery with the Light Gradient Boosting Machine (LightGBM) model: (1) No pre-processing, i.e., use of Top-of-Atmosphere (TOA) reflectance, (2) AC pre-processing, obtaining water-leaving reflectance (Rw) from AC for the Operational Land Imager lite (ACOLITE)’s Dark Spectrum Fitting (DSF) technique, and (3) AE pre-processing, correcting for the AE using T-Mart before obtaining Rw from DSF. Results demonstrated that AE pre-processing outperformed the other two options. For L8/9, AE pre-processing reduced the Root Mean Square Error (RMSE) and improved the median symmetric accuracy (ε) by 48.8% and 19.0%, respectively, compared with AC pre-processing, and by 48.5% and 50.7%, respectively, compared with No pre-processing. For S2, AE pre-processing performed better than AC pre-processing and also outperformed No pre-processing, reducing RMSE by 28.4% and ε by 50.8%. However, No pre-processing yielded the lowest absolute symmetric signed percentage bias (|β|) among all pre-processing options. Analysis indicated that AE pre-processing yielded superior performance within 0–300 m from shore than other options, where the AE influence is strongest. Turbidity maps generated using AE pre-processing were smoother and less noisy compared to the other pre-processing options, particularly in cloud-adjacent regions. Overall, our findings suggest that incorporating AE correction through T-Mart improves the performance of the LightGBM model for turbidity retrieval from both L8/9 and S2 imagery in the St. Lawrence River, compared to the alternative pre-processing options.