A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types

摘要

Numerous algorithms have been developed to retrieve chlorophyll-a (Chla) concentrations (mg m(-3)) from Earth observation (EO) data collected over optically complex waters. Retrieval accuracy is highly variable and often unsatisfactory where Chla co-occurs with other optically active constituents. Furthermore, the applicability and limitations of retrieval algorithms across different optical complex systems in space and time are often not considered. In the first instance, this paper provides an extensive performance assessment for 48 Chla retrieval algorithms of varying architectural design. The algorithms are tested in their original parametrisations and are then retuned using in-situ remote sensing reflectance (R-rs(lambda), sr(-1)) data (n = 2807) collected from 185 global inland and coastal aquatic systems encompassing 13 different optical water types (OWTs). The paper then demonstrates retrieval performance across the full dataset of observations and within individual OWTs to determine the most effective model(s) of those tested for retrieving Chla in waters with varying optical properties. The results revealed significant variability in retrieval performance when comparing model outputs to in-situ measured Chla for the full in-situ dataset in its entirety and within the 13 distinct OWTs. Importantly, retuning an algorithm to optimise its parameterisation for each individual OWT (i.e. one algorithm, multiple parameterisations) is found to improve the retrieval of Chla overall compared to simply calibrating the same algorithm using the complete in-situ dataset (i.e. one algorithm, one parameterisation). This resulted in a 25% improvement in retrieval accuracy based on relative percentage difference errors for the best performing Chla algorithm. Improved performance is further achieved by allowing model type and specific parameterisation to vary across OWTs (i.e. multiple algorithms, multiple parameterisations). This adaptive framework for the dynamic selection