Novel Spectra-Derived Features for Empirical Retrieval of Water Quality Parameters: Demonstrations for OLI, MSI, and OLCI Sensors

The empirical (regression-based) methods for estimation of water quality parameters are mostly built upon the features derived from the original feature space of optical imagery (e.g., band ratios). This article aims at examining novel features to retrieve in-water constituents including chlorophyll-a (Chl-a), total suspended solids (TSS), and colored dissolved organic matter (CDOM). In this article, direction cosines and transformation of either color space or the coordinate system are applied to the original feature space in order to derive new features. A full-search approach is exploited to identify the optimal band combination for a given type of feature. The proposed analysis seeks for a band combination among all the possible ones that yield the strongest correlation through regressing a given feature against the concentration of the constituent of interest. The effectiveness of the proposed features is examined against standard ones using radiative transfer simulations, in situ measurements, and satellite imagery in a wide range of in-water optical conditions. The simulated and in situ data enabled in-depth analyses on the efficacy of recent satellite sensors with the primary focus of the aquatic science community [Operational Land Imager (OLI), MutiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI)] for retrieval of in-water constituents. TSS and Chl-a concentration of two alpine lakes (Lake Constance and Lake Lucerne) are also mapped using a real OLI image. The results suggest the effectiveness of the proposed features that can be leveraged to estimate the constituents in inland/coastal waters. OLI-based retrievals of in-water constituents proved difficulties in optically complex waters, whereas enhanced spectral resolution of MSI and OLCI permitted accurate estimates.