Selective Filtering for Enhancing Chlorophyll Retrieval Accuracy from Sentinel-3 Data Using Random Forest Models

Abstract

This paper presents an investigation into the use of a random forest (RF) model for retrieving chlorophyll content from Sentinel-3 satellite data. We train various RF regression models on available datasets and introduce a classifier to identify instances where predictions may be inaccurate. This classifier aids in filtering out less reliable cases, enhancing the overall accuracy of our models at the expense of reducing the amount of processed data. Additionally, we optimize the hyperparameters of this hybrid model to improve its performance further. Our findings illustrate the effectiveness of combining regression models with a classifier in environmental remote sensing, offering a promising method for improving the accuracy of satellite-derived chlorophyll measurements.