Abstract
A comprehensive spectral-biogeochemical database was developed for the Wabash River and the Tippecanoe River in Indiana, United States. This database includes spectral measurements of river water, coincident in situ measurements of water quality parameters (chlorophyll (chl), non-algal particles (NAP), and colored dissolved organic matter (CDOM)), nutrients (total nitrogen (TN), total phosphorus (TP), and dissolved organic carbon (DOC)), water-column inherent optical properties (IOPs), water depths, substrate types, and bottom reflectance spectra collected in summer 2014. With this dataset, the temporal variability of water quality observations was first analyzed and studied. Second, radiative transfer models were inverted to retrieve water quality parameters using a look-up table (LUT) based spectrum matching methodology. Results found that the temporal variability of water quality parameters and nutrients in the Wabash River was closely associated with hydrologic conditions. Meanwhile, there were no significant correlations found between these parameters and streamflow for the Tippecanoe River, due to the two upstream reservoirs, which increase the settling of sediment and uptake of nutrients. The poor relationship between CDOM and DOC indicates that most DOC in the rivers was from human sources such as wastewater. It was also found that the source of water (surface runoff or combined sewer overflow (CSO)), water temperature, and nutrients were important factors controlling instream concentrations of phytoplankton. The LUT retrieved NAP concentrations were in good agreement with field measurements with slope close to 1.0 and the average estimation error was 4.1% of independently obtained lab measurements. The error for chl estimation was larger (37.7%), which is attributed to the fact that the specific absorption spectrum of chl was not well represented in this study. The LUT retrievals for CDOM experienced large variability, probably due to the small data range collected in this study and the insensitivity of Rrs to CDOM change. It is concluded that the success of the LUT method requires accurate spectral measurements and enough a priori information of the environment to construct a representative database for water quality retrieval. Therefore, future work will focus on continuing data collection in other seasons of the year and better characterization of the study area.
Highlights
Remote sensing provides a practical means for synoptic and multi-temporal monitoring of water quality
The analytical approach is based on the physical relationship between the inherent optical properties (IOPs) of the water column and measured apparent optical properties (AOPs)
Remote sensing data can be inverted by using the analytical modeling approach to retrieve water column properties and bottom depths [5,6]
Summary
Remote sensing provides a practical means for synoptic and multi-temporal monitoring of water quality. Significant attention has been paid to the empirical approach, which focuses on developing best-fit correlational models between remote sensing data (digital numbers, radiance, or reflectance) and measured water quality parameters [1]. The analytical approach is based on the physical relationship between the inherent optical properties (IOPs) of the water column and measured apparent optical properties (AOPs). Remote sensing data can be inverted by using the analytical modeling approach to retrieve water column properties and bottom depths [5,6]. While analytical models are typically developed by simplifying the full radiative transfer equations based on a set of given assumptions, e.g., level water surface or no internal light sources, the radiative transfer models do not have such constraints
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