Colored dissolved organic matter (CDOM) and chlorophyll-a (Chla) are important water quality parameters and play crucial roles in aquatic environment. Remote sensing of CDOM and Chla concentrations for inland lakes is often limited by low spatial resolution. The newly launched Sentinel-2 satellite is equipped with high spatial resolution (10, 20, and 60 m). Empirical band ratio models were developed to derive CDOM and Chla concentrations in Lake Huron. The leave-one-out cross-validation method was used for model calibration and validation. The best CDOM retrieval algorithm is a B3/B5 model with accuracy coefficient of determination (R2)=0.884, root-mean-squared error (RMSE)=0.731 m−1, relative root-mean-squared error (RRMSE)=28.02%, and bias=−0.1 m−1. The best Chla retrieval algorithm is a B5/B4 model with accuracy R2=0.49, RMSE=9.972 mg/m3, RRMSE=48.47%, and bias=−0.116 mg/m3. Neural network models were further implemented to improve inversion accuracy. The applications of the two best band ratio models to Sentinel-2 imagery with 10 m×10 m pixel size presented the high potential of the sensor for monitoring water quality of inland lakes.

Spectral reflectance data, including irradiance reflectance (R _t ) and remote sensing reflectance (R _rs , sr ^-1 ), and colored dissolved organic matter (CDOM) absorption coefficients a _CDOM (440), were collected in the Saginaw River and Kawkawlin River plume regions of Lake Huron. We developed an empirical band ratio algorithm to derive a _CDOM (440) that could be directly applicable to Landsat-8 imagery. A model ranking method is used to determine the best band ratios as well as their empirical functions. One problem of previous CDOM estimations from Landsat imagery is that they usually use R _t or R _t /π rather than the real R _rs as the input data, but as a result of our study, algorithms derived using R _rs performed much better than using Rt. The green/red band ratio gave the best accuracies by fitting with power and exponential models (power model: R ² = 0.819 and RMSE = 0.889 m ^-1 and exponential model: R ² = 0.829 and RMSE = 0.863 m ^-1 ). The power and exponential models were further validated using an independent data group, achieving excellent results with the RMSE of 0.642 and 0.504 m ^-1 , respectively. The application of the exponential model a _CDOM (440) = 40.75e ^-2.463x , x = R _rs (B3)/R _rs (B4) on two Landsat-8 images demonstrated that estimating CDOM from to Landsat-8 imagery has potential applications for monitoring water quality in long term and large scale.

Inland waters, characterized by small scale but a large number, play an important role in the carbon budget and global carbon cycle. Colored dissolved organic matter (CDOM) is a significant indicator used for tracing dissolved organic carbon (DOC) in inland waters. Accurate remote-sensing estimation of CDOM concentration is still a challenge due to complex optical properties of inland waters. Many efforts have been made to estimate high concentration CDOM, leading to a knowledge gap in using remote sensing to estimate low concentration CDOM, which results in difficulty and uncertainty for estimating total carbon storage in global inland waters. Currently, few studies are devoted to estimating low concentration CDOM, while Landsat-8’s applicability for estimating low concentration CDOM is still unknown. In this study, two datasets, NRL_SFE and Lake_Erie, were collected to represent extremely low CDOM conditions that a_CDOM (440) (the absorptions of CDOM at 440 nm) ranges 0.215–1.165 m^–1, and 0.066–1.242 m^–1, respectively. The best CDOM retrieval model (validation results: R² = 0.78; RMSE = 0.161 m^–1; MRE = 26.02%), a_CDOM(440) = 0.483x^–1.776, x = R_rs(B2)/R_rs(B4), was developed for monitoring CDOM in the two regions. Results show that Landsat-8 and the best model work well for estimating low concentration CDOM in inland waters. In addition, we have proved that Landsat-8 surface reflectance products, which are freely provided by USGS, are convenient and useful for developing remote sensing algorithm of CDOM estimation after correcting water surface reflectance. The image-derived CDOM’s spatial patterns in Lake Erie demonstrate the Landsat-8’s applicability to observe spatiotemporal variations of low concentration CDOM.