Sentinel-2 Water Quality Models and Google Colab Workflow for the Yucatán Peninsula

This dataset contains the calibration and validation datasets, together with the Google Colab workflow, used to develop and apply Sentinel-2-based water quality models for inland aquatic systems. The workflow integrates the Modified Atmospheric Correction for Inland Waters (MAIN) and multiple linear regression (MAIN-MLR) approaches to retrieve five key water quality parameters: chlorophyll-a (Chl-a), total suspended solids (TSS), turbidity (NTU), colored dissolved organic matter (CDOM), and Secchi disk depth (SDD). The dataset includes five Excel files corresponding to each water quality parameter. Each workbook contains two separate datasets: (1) calibration data used for model development and regression analysis, and (2) independent validation data used to evaluate model performance. The accompanying Google Colab notebook provides the complete remote sensing workflow, including Sentinel-2 image preprocessing, cloud and shadow masking, atmospheric correction, water masking, calculation of remote sensing reflectance (Rrs), and generation of water quality products. These data and scripts are provided to facilitate reproducibility, evaluation, and further application of Sentinel-2 remote sensing approaches for monitoring inland waters.