BarrySaidy/lake-naivasha-monitoring

A comprehensive study comparing cloud-optimized (Zarr) vs traditional download workflows for satellite water quality monitoring of Lake Naivasha, Kenya. # Lake Naivasha Cloud-Optimized Monitoring Study A comprehensive study comparing cloud-optimized (Zarr) vs traditional download workflows for satellite water quality monitoring of Lake Naivasha, Kenya. ## Project Overview This project evaluates whether cloud-optimized streaming workflows can outperform traditional download-first approaches for near real-time chlorophyll-a monitoring using Sentinel-2 satellite data. ### Key Research Questions 1. How much faster is cloud-optimized processing compared to traditional downloads? 2. How much bandwidth/storage can be saved with streaming approaches? 3. Are the results (Chl-a values) equivalent between both workflows? 4. What are the seasonal patterns of Chl-a in Lake Naivasha? ## Project Structure ``` lake-naivasha-monitoring/ ├── config/ │ ├── config.yaml # Main configuration │ └── test_scenes.csv # Test scenes for comparison ├── notebooks/ │ ├── 01_cloud_optimized_workflow.ipynb # Zarr streaming workflow │ ├── 02_traditional_workflow.ipynb # Traditional download workflow │ ├── 03_comparison_workflow.ipynb # Comparison orchestrator │ └── 04_seasonal_analysis.ipynb # Seasonal analysis ├── src/ │ ├── config.py # Configuration loader │ ├── data_discovery.py # STAC search utilities │ ├── metrics.py # Metrics calculation │ └── visualization.py # Plotting utilities ├── data/ │ ├── raw/ │ │ ├── traditional/ # Downloaded scenes │ │ └── cloud_optimized/ # Zarr references │ ├── processed/ │ │ └── metrics/ # Output metrics CSVs │ └── results/ │ ├── comparisons/ # Comparison analysis │ └── seasonal_analysis/ # Seasonal trends └── environment.yml ``` ## Setup ### 1. Create Conda Environment ```bash cd lake-naivasha-monitoring conda env create -f environment.yml conda activate lake-nai …