Assessment of Atmospheric Correction Algorithms for Landsat-8/9 Operational Land Imager over Inland and Coastal Waters

Abstract

Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, we systematically evaluated six state-of-the-art AC algorithms—ACOLITE, C2RCC, iCOR, L2GEN, OC-SMART, and POLYMER—using Landsat-8/9 OLI data. This study leverages 440 high-quality in situ radiometric matchups spanning a wide range of aquatic environments, including inland lakes from China’s Satellite-Ground Synchronous Campaign and coastal waters from the globally distributed GLORIA dataset. These complementary datasets provide a robust benchmark for evaluating AC algorithm performance. A unified Optical Water Type (OWT) classification framework ensured consistency across environmental conditions. Results highlight significant variability in algorithm performance based on water type. In coastal waters, L2GEN demonstrated the lowest errors in visible bands, whereas OC-SMART achieved superior overall accuracy in inland waters. Notably, ACOLITE exhibited better performance than other algorithms in the blue spectral region (443 and 482 nm) for inland waters. OWT-specific analysis showed that OC-SMART maintained robust accuracy across the turbidity gradient, while ACOLITE and iCOR excelled in highly turbid waters (OWTs 5–6). In contrast, L2GEN, C2RCC, and POLYMER were more effective in clearer waters (OWTs 3–4). The study further discusses the applicability of each algorithm and offers recommendations for mitigating adjacency effects (AE) to improve AC accuracy. These findings provide valuable guidance for selecting and optimizing AC strategies for inland and coastal water monitoring.

Topics & Concepts