A Synergistic Approach for High-Precision Lake Turbidity Retrieval Using ICESat-2 and Sentinel-2 Data

Abstract

Reliable monitoring of lake turbidity is often constrained by the trade-off between the spatial coverage of passive optical imagery and the vertical profiling capability of active LiDAR observations. This study proposes a multi-source retrieval framework integrating ICESat-2 ATL03 photon data with Sentinel-2 multispectral imagery for large-scale turbidity mapping in Lake Erie. An adaptive quadtree pruning strategy combined with Otsu thresholding was applied to isolate high-confidence surface water photons. Vertical distribution descriptors, including penetration depth and attenuation-related photon metrics, were quantified along 500-m segments. A Random Forest inversion model was established using these LiDAR-derived features, achieving an RMSE of 2.67 NTU. To overcome the spatial discontinuity of ICESat-2 tracks, the LiDAR-derived turbidity estimates were incorporated as virtual buoy constraints to calibrate temporally matched Sentinel-2 reflectance products. A Bayesian-optimized fusion framework was subsequently developed to generate spatially continuous turbidity fields. Validation results indicate that the synergistic model achieved an RMSE of 2.95 NTU, representing a 39% improvement over conventional optical-only retrieval methods. The proposed framework demonstrates the potential of cross-modal remote sensing synergy for large-scale inland water quality monitoring.