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Sentinel-1 & Sentinel-2 (ESA Copernicus)

Provider
ESA (European Space Agency) / Copernicus Programme
Website
scihub.copernicus.eu | GEE
Access Level
🟡 Free Registration | 🔵 GEE for direct analysis
Formats Available
GeoTIFF (Level-1C, Level-2A), SAFE format
Resolution
S1: 10m SAR | S2: 10m optical
Revisit Time
S1: 12 days | S2: 5 days (with both satellites)

Sentinel-1 vs Sentinel-2

Sentinel-1 Sentinel-2
Type SAR (Radar) Optical (like a camera)
Works in clouds? ✅ Yes ❌ No
Works at night? ✅ Yes ❌ No
Resolution 10m 10m
Best for Flood mapping, soil moisture, ship detection Crop monitoring, LULC, vegetation
Bands VV, VH polarisation 13 spectral bands

Sentinel-2 Bands (13 Bands)

Band Wavelength Resolution Use Case
B1 443nm (Coastal) 60m Aerosol
B2 490nm (Blue) 10m True colour
B3 560nm (Green) 10m True colour
B4 665nm (Red) 10m True colour, NDVI
B5 705nm (Red-edge) 20m Vegetation stress
B6 740nm (Red-edge) 20m
B7 783nm (Red-edge) 20m
B8 842nm (NIR) 10m NDVI, LAI
B8A 865nm (NIR narrow) 20m
B9 945nm (Water vapour) 60m
B11 1610nm (SWIR) 20m Moisture, burn
B12 2190nm (SWIR) 20m Geology, burn

Access Methods

Method 1: Copernicus Data Space (Free Registration) 🟡

  1. Register at dataspace.copernicus.eu (new Copernicus portal)
  2. Use the "Open Search" tool to find images by date and location
  3. Download SAFE format → Extract → Load in QGIS

GEE has the complete Sentinel archive — use it for large-area analysis without downloading:

// GEE: Sentinel-2 NDVI for Agricultural Area
var punjab = ee.Geometry.Rectangle([74, 29, 77, 32]); // Punjab wheat belt

// Load Sentinel-2 (rabi season, February)
var s2 = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
  .filterBounds(punjab)
  .filterDate('2024-02-01', '2024-02-28')
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10))
  .median();

// NDVI
var ndvi = s2.normalizedDifference(['B8', 'B4']).rename('NDVI');

// EVI (Enhanced Vegetation Index — better in dense vegetation)
var evi = s2.expression(
  '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))',
  {'NIR': s2.select('B8'), 'RED': s2.select('B4'), 'BLUE': s2.select('B2')}
).rename('EVI');

Map.centerObject(punjab, 8);
Map.addLayer(ndvi, {min: 0, max: 0.8, palette: ['tan', 'yellow', 'green', 'darkgreen']},
             'NDVI — Punjab Feb 2024 (Wheat)');
Map.addLayer(s2, {bands:['B4','B3','B2'], min: 0, max: 3000}, 'True Colour');

Sentinel-1: Flood Mapping

Sentinel-1's SAR can detect floods even through monsoon clouds — critical for disaster response:

// GEE: Flood Mapping using Sentinel-1
// Compare before-flood vs during-flood VV backscatter

var flood_area = ee.Geometry.Rectangle([80.5, 25.5, 82.5, 27.5]); // UP flood 2023

// Before flood
var before = ee.ImageCollection("COPERNICUS/S1_GRD")
  .filterBounds(flood_area)
  .filterDate('2023-06-01', '2023-06-30')
  .filter(ee.Filter.eq('instrumentMode', 'IW'))
  .select('VV').mean();

// During flood (monsoon)
var during = ee.ImageCollection("COPERNICUS/S1_GRD")
  .filterBounds(flood_area)
  .filterDate('2023-08-01', '2023-08-31')
  .filter(ee.Filter.eq('instrumentMode', 'IW'))
  .select('VV').mean();

// Flood = significant decrease in backscatter
var flood_mask = during.lt(before.subtract(3));  // 3dB drop = flooded

Map.centerObject(flood_area, 9);
Map.addLayer(flood_mask, {palette: ['white', 'blue']}, 'Potential Flood (Aug 2023)');

✏️ Practice Exercise

Exercise 5.3 — Map Crop Fields Using Sentinel-2

Goal: Identify wheat fields in your district using NIR-Red-Green False Colour Composite.

In GEE Code Editor: 

var myDistrict = ee.Geometry.Point([75.7, 26.9]).buffer(30000); // Change to your district

var s2 = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED")
  .filterBounds(myDistrict)
  .filterDate('2024-01-01', '2024-03-31')  // Rabi season
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 15))
  .median();

// False colour: NIR=Red channel, Red=Green, Green=Blue
// Healthy vegetation appears bright RED in this composite
Map.addLayer(s2, {bands: ['B8', 'B4', 'B3'], min: 0, max: 4000},
             'False Colour (NIR-R-G)');
Map.centerObject(myDistrict, 11);

  • Areas appearing bright red-magenta = healthy crops/vegetation
  • Light blue/grey = urban/bare soil
  • Dark blue = water bodies

Can you identify individual agricultural fields? How large are they?

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