Dataset supporting: Zone-Aware Evaluation of Sentinel-2 Spectral Indices for Chlorophyll-a-Defined Bloom-Risk Monitoring in Barr Lake, Colorado

This repository contains the processed dataset and analysis scripts used to support the evaluation of Sentinel-2 spectral indices for chlorophyll-a-defined bloom-risk monitoring in Barr Lake, Colorado, USA (2019–2025). The dataset integrates in-situ chlorophyll-a (Chl-a) measurements with spectral indices derived from Sentinel-2 Level-2A surface reflectance imagery. Each observation represents a field sampling event paired with the closest valid Sentinel-2 acquisition within a ±5-day temporal window following quality-control procedures. The final dataset contains 70 quality-controlled satellite–field matchups. Included files: * BarrLake_Sentinel2_HAB_dataset_2019_2025.xlsx * main_analysis.py * GEE_Sentinel2_Index_Extraction_BarrLake.py * GEE_Figure6_SpatialComparison_BarrLake.py Three Sentinel-2 spectral indices were evaluated: * Normalized Difference Chlorophyll Index (NDCI) * Floating Algae Index (FAI) * Absorption-Band Difference Index (ABDI) Bloom-risk conditions were defined as chlorophyll-a concentrations greater than 50 µg L⁻¹. Primary regression analyses used uncapped log₁₀-transformed chlorophyll-a concentrations, whereas capped chlorophyll-a values (>400 µg L⁻¹) were used only in regression sensitivity analyses. The dataset reveals strong spatial variability in chlorophyll-a concentrations between nearshore and open-water environments. Relationships between spectral indices and continuous chlorophyll-a were statistically significant but modest, whereas classification analyses produced stronger and more management-relevant performance. Among the evaluated indices, NDCI provided the most balanced overall bloom-risk classification performance, FAI exhibited higher specificity and more conservative classification behavior, and ABDI demonstrated intermediate classification performance with relatively strong precision–recall behavior. Version 3 includes updated statistical analyses, revised manuscript tables and supplementary materials, updated Python and Google Earth Engine scripts, and improved repository documentation aligned with the final manuscript. This repository provides the processed dataset and analysis scripts necessary to reproduce the analyses presented in the associated manuscript and may be used for research on harmful algal bloom monitoring, chlorophyll-a classification, satellite-assisted water-quality assessment, and remote sensing applications in optically complex inland waters.