Working with Sentinel-2 Imagery in GEE: Cloud Masking and Visualization

Learn how to use cloud masking and visualize Sentinel-2 imagery in Google Earth Engine. Includes scripts for RGB, NDVI, and true-color composites.

Sentinel-2 with cloud masking, developed by the European Space Agency (ESA), offers high-resolution multispectral imagery with a revisit time of just 5 days. It's one of the most popular datasets in Google Earth Engine (GEE) thanks to its 13 spectral bands, wide area coverage, and frequent updates.

But like most optical satellites, Sentinel-2 is affected by clouds, haze, and shadows, which can obscure land features. To get clean, usable imagery, cloud masking is a critical first step.

In this tutorial, you'll learn how to:

• Load Sentinel-2 surface reflectance data

• Mask clouds and cloud shadows using QA60 and SCL bands

• Visualize imagery in true color, false color, and NDVI

1. Load Sentinel-2 SR Collection in GEE

We’ll use Level-2A (surface reflectance) data:
📦 Dataset ID: COPERNICUS/S2_SR

var region = ee.Geometry.Point([-71.9, 45.3]); // Sherbrooke, Canada

var s2 = ee.ImageCollection('COPERNICUS/S2_SR')
            .filterDate('2023-01-01', '2023-01-31')
            .filterBounds(region)
            .sort('CLOUDY_PIXEL_PERCENTAGE');

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2. Cloud Masking Using QA60 Band (Bitmask Method)

The QA60 band contains cloud and cirrus information encoded as bits.

Cloud Mask Function:

function maskS2clouds(image) {
  var qa = image.select('QA60');
  var cloudBitMask = 1 << 10; // clouds
  var cirrusBitMask = 1 << 11; // cirrus
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
                .and(qa.bitwiseAnd(cirrusBitMask).eq(0));
  return image.updateMask(mask).copyProperties(image, ['system:time_start']);
}

 Apply Cloud Mask:

var s2Clean = s2.map(maskS2clouds);

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3. Alternative Cloud Masking Using SCL Band (Scene Classification)

SCL is available only in Level-2A products. This band classifies each pixel (e.g., cloud, vegetation, water).

 SCL-based Mask Function:

function sclMask(image) {
  var scl = image.select('SCL');
  var cloudMask = scl.neq(3) // not cloud shadow
                   .and(scl.neq(8)) // not cloud medium probability
                   .and(scl.neq(9)) // not cloud high probability
                   .and(scl.neq(10)); // not cirrus
  return image.updateMask(cloudMask).copyProperties(image, ['system:time_start']);
}

 Apply:

var s2Masked = s2.map(sclMask);

SCL method is more flexible, especially for thematic classification.

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4. Visualize Sentinel-2 in True Color

RGB (B4, B3, B2)

var rgb = s2Clean.median();
Map.centerObject(region, 10);
Map.addLayer(rgb, 
  {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000}, 'True Color');

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5. Visualize Sentinel-2 in False Color (Vegetation Highlight)

NIR, Red, Green (B8, B4, B3)

Map.addLayer(rgb, 
  {bands: ['B8', 'B4', 'B3'], min: 0, max: 3000}, 'False Color');

🌿 Healthy vegetation appears bright red.

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6. Calculate and Visualize NDVI

NDVI is one of the most common vegetation indices, calculated as:

 (NIR - RED) / (NIR + RED)

For Sentinel-2: NIR = B8, RED = B4

var ndvi = rgb.normalizedDifference(['B8', 'B4']).rename('NDVI');
Map.addLayer(ndvi, 
  {min: 0, max: 1, palette: ['white', 'green']}, 'NDVI');

📈 You can also chart NDVI over time or export it for further analysis.

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7. Best Practices for Sentinel-2 Visualization

Tip	Why It Matters
Use median() or mosaic()	Reduces cloud impact
Apply cloud masking before calculations	Ensures data integrity
Use scale = 10 when exporting	Matches Sentinel-2 spatial resolution
Visualize with appropriate color palettes	Enhances interpretability
Filter by CLOUDY_PIXEL_PERCENTAGE < 20	Pre-selects best images

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8. Exporting Cloud-Free Images

Export.image.toDrive({
  image: rgb.clip(region),
  description: 'Sentinel2_TrueColor',
  folder: 'GEE_exports',
  scale: 10,
  region: region,
  maxPixels: 1e13
});

Ideal for reports, map layouts, and temporal comparisons.

Conclusion

With proper cloud masking and visualization techniques, Sentinel-2 imagery in Google Earth Engine becomes a powerful tool for agriculture, forestry, urban monitoring, and environmental analysis. Using both QA60 and SCL-based methods, you can generate clean composites, calculate indices like NDVI, and export high-quality results.

By mastering these techniques, you ensure that your analysis is both visually accurate and scientifically reliable.

🔗 Related Articles:

• Top 10 Free Satellite Datasets Available in Google Earth Engine

• Exploring MODIS Products in GEE: A Complete Guide

• Compute EVI using expressions in GEE Google Earth Engine

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Written by: Jamal Chaaouan | GEE Academy