Optical water typing in optically complex waters: A case study of Chesapeake Bay

Abstract

Optical water typing has been widely used in aquatic research to classify water bodies based on their inherent optical properties as perceived through satellite-based measures of water color. While optical water type (OWT) classifications have primarily been used to better understand water color dynamics and improve satellite-based estimates of water clarity, chlorophyll a, and other optically active constituents, its potential for broader water quality assessment has received less attention. In this study, we examine the relationships between a suite of water quality parameters, including nutrient concentrations, and OWTs in Chesapeake Bay, an optically complex temperate estuary with an extensive water quality monitoring program. Using machine learning, we grouped R_rs data into ten dominant OWTs; the optimum number of clusters identified from a statistical within-cluster dispersion test. These OWTs ranged from brown to blue/green estuarine waters and emerged with high spatial contiguity. By analyzing synchronously measured discrete water quality variables grouped by corresponding OWTs, unexpected patterns became evident. Notably, total nitrogen concentration emerged as having statistically significant differences between OWTs, suggesting our approach can enhance understanding of nutrient pollution at the scale of a large optically complex estuary, especially in times of reduced fixed sampling routines (e.g., winter). This study aids in the interpretation of Bay-wide water quality trends, can assist in the dynamic selection of water quality retrieval algorithms, and provides high resolution data to identify regions of water quality impairment.