Enhanced remote sensing of surface water Chlorophyll-a: Coupling dynamic algae vertical movement modeling with multi-spectral satellite images

Remote sensing plays an increasingly critical role in water quality monitoring due to its capacity for consistent observations on both large and small water bodies. However, current remote sensing approaches face limitations in aligning satellite observations with in-situ measurements, largely due to the dynamic vertical behavior of algae and the temporal constraints of satellite overpasses. Consequently, many studies rely on large water bodies, space–time substitution, or opportunistic imaging of blooms, which restricts the applicability of remote sensing for routine monitoring tasks such as periodic chlorophyll-a (Chl-a) estimation. With near-daily global coverage, PlanetScope imagery presents new opportunities to overcome these constraints. In this study, we propose a novel field-sampling augmentation framework that integrates satellite observations with in-situ data by modeling the diurnal vertical migration of algae through an Algal Behavior Function (ABF). This function enables the temporal adjustment of in-situ measurements, generating refined field-to-satellite matchups that enhance the robustness of Chl-a estimation models. We applied this method using PlanetScope imagery from 2022 to 2023 and co-located sonde measurements, incorporating vertical profile and timestamp information to correct for field-to-satellite temporal mismatches at two lakes in Ohio (Grand Lake St. Marys, samples = 84, Del-Co reservoirs, samples = 333). The augmented model improved Chl-a prediction accuracy (RMSE reduce) by 5.8%-18.0% compared to baseline models without refinement, with notable gains during non-bloom periods, offering potential for earlier bloom detection. Furthermore, the ABF demonstrated moderate geographic transferability: models using ABFs derived from a reservoir successfully improved Chl-a predictions at two additional lakes located 156 km (western Lake Erie) and 383 km (Saginaw Bay, Lake Huron) away, with accuracy gains (RMSE reduce) of 28.5%-35.3%. Collectively, these results position ABF as a practical, sensor-agnostic pre-processing step that can be embedded in operational workflows to improve high-resolution Chl-a retrievals, enable earlier harmful algal bloom alerts, and support cross-basin trend analyses for management.