研究目的

The primary goal of this study was to utilize freely available Landsat multispectral data to produce a highly predictive reflectance–SSC modeling paradigm for large fluvial systems such as the Missouri and Mississippi Rivers using state-of-the-art ML techniques. Second, this study also intended to address issues related to modeling a wide range of SSC concentrations using a single ML algorithm.

研究成果

Leveraging widely available and free Landsat data in conjunction with USGS monitoring station data, reflectance–SSC models were developed for the lower Missouri and middle Mississippi River system. The results of the SSC modeling showed that ELM outperformed both FFNN and CFNN, as well as the popular ML techniques such as RF and SVM by significant margins when evaluated by R2 and RMSE. ELM models for all Landsat sensors generated R2 values above 0.9 and displayed noteworthy generalization abilities along with negligible overfitting. ELM proved to be a robust model to predict a wide range of SSC based on a single algorithm.

研究不足

The temporal resolution of Landsat based models represents one of the greatest challenges for remote sensing of SSC. Given Landsat 7 and 8’s revisit cycle of 16 days, the satellite platforms produce imagery over a given area on a weekly basis. For the database generated in this study over half (56%) of the images were eliminated during the filtering process with 31% removed due to cloud cover and 13% due to vessel traffic. This equates to sampling once a month per sensor and represents the limiting factor, as USGS monitoring stations provide daily samples.