Quantifying Relationships between Near-Infrared Reflectance Spectra of Lake Sediments and Water Chemistry

Abstract

One of the most useful approaches to long-term monitoring of aquatic systems is the analysis of lake sediments. Biological indicators, such as diatoms, preserved in the sediments are widely used. We suggest that use of near-infrared reflectance (NIR) spectroscopy of lake sediments could become a rapid and cost-effective technique for environmental monitoring to follow long-term changes in water quality. NIR spectra of surface sediments from Swedish lakes were used to establish relationships between sediment properties and measured lake water chemistry. Predictive models for inferring total phosphorus (TP), pH, and total organic carbon (TOC) from sediment NIR data were developed using partial least squares regression. The model for inferring lake water TP (n = 33 lakes) captured 83% of the variance, while the explained variance for pH (n = 52 lakes) and TOC (n = 25 lakes) was 85 and 68%, respectively. We also used the TP model to evaluate the effect of inaccuracy in measured lake water chemistry for the model performance, i.e., the amount of explained variance. The inaccuracy in measured lake water chemistry corresponds to 10.5% of the total variance in the model. The highest possible variance to model then being 89.5%. This evaluation indicated that the obtained modeled variance almost equaled the variance possible to model, which suggests that further improvement of the models should be focused on enlargement of the calibration data set to include more lake types.