River Turbidity Estimation Using Sentinel-2A Imagery using Google Colab GEE Python
In this hands-on tutorial, we walk through a complete, real-world workflow to derive NDTI (Normalized Difference Turbidity Index) and analyze monthly turbidity dynamics using Google Colab. Whether you’re a remote sensing analyst, environmental researcher, hydrology student, or GEE learner, this video provides EVERYTHING you need from preprocessing to visualization and exporting your turbidity maps and statistics.
What You Will Learn in This Tutorial
How to set up Google Earth Engine in Google Colab, compute NDTI as a proxy for turbidity using Sentinel-2A surface reflectance, apply cloud masking and water detection, generate monthly maximum turbidity maps, create beautiful faceted maps of turbidity trends, compute and plot NDTI time series, export turbidity CSV outputs for water quality analysis, save high-quality turbidity maps for reports and publications
Why NDTI Matters?
NDTI is a strong proxy for detecting suspended sediments, turbid flows, and polluted waters—critical for River health monitoring, Erosion studies, Flood-induced turbidity changes, Water quality assessment, Sediment transport research
Who Should Watch?
Hydrologists, Remote Sensing Learners, Water Resource Engineers, Earth Observation Analysts, Google Earth Engine Users, Students & Researchers
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Useful link
Code Link:
Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A (SR): https://developers.google.com/earth-e...
GoogleColab:
https://drive.google.com/file/d/1KO03...
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#Sentinel2 #Turbidity #NDTI #GoogleColab #GoogleEarthEngine #Hydrology #WaterQuality #RemoteSensing #xarray #geemap #PythonGIS #EarthObservation #EnvironmentalMonitoring #RiverMonitoring
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