Sentinel-2 Reference Guide

This reference guide provides detailed information about Sentinel-2 MSI data parameters and options available through the Copernicus Data Space Ecosystem.

Overview

Sentinel-2 is a constellation of two polar-orbiting satellites (Sentinel-2A and Sentinel-2B) carrying a Multi-Spectral Instrument (MSI). The mission provides high-resolution optical imagery for land monitoring, emergency response, and climate change studies.

Key Features: - Multi-Spectral Instrument (MSI) with 13 spectral bands - 10m, 20m, and 60m spatial resolution depending on spectral band - 290 km swath width - 5-day revisit time (constellation) - Global coverage with systematic acquisitions over land and coastal areas

Search Parameters

Parameter Types

When searching for Sentinel-2 data, parameters are passed in two ways:

Direct Parameters: Passed directly to the search() method - collection_name - Mission/collection identifier - product_type - Product type (S2MSI1C, S2MSI2A, etc.) - orbit_direction - Orbit direction (ASCENDING/DESCENDING) - aoi_wkt - Area of interest in WKT format - start_date / end_date - Temporal range - top - Maximum number of results
Attributes: Passed in the attributes dictionary - tileId - Tile identifier (e.g., 32TQM) - processingLevel - Processing level (S2MSI1C, S2MSI2A) - platform - Satellite platform (S2A, S2B, S2C) - instrument - Instrument type (MSI, AUX) - orbitNumber - Absolute orbit number - sensorMode - Sensor mode (INS-NOBS, INS-RAW, INS-VIC) - cloudCover - Cloud cover percentage - status - Product availability status - relativeOrbitNumber - Relative orbit number - processingBaseline - Processing baseline version - missionTakeId - Mission take identifier

Basic Parameters

Collection Name

Use 'SENTINEL-2' as the collection name.

results = searcher.search(collection_name='SENTINEL-2')

Geographic Parameters

Geometry

Region of Interest defined in Well Known Text (WKT) format with coordinates in decimal degrees (EPSG:4326).

# Polygon example
aoi_wkt = 'POLYGON((12.4 41.9, 12.5 41.9, 12.5 42.0, 12.4 42.0, 12.4 41.9))'
results = searcher.search(
    collection_name='SENTINEL-2',
    aoi_wkt=aoi_wkt
)

Tile Identifier

Sentinel-2 data is organized in tiles following the Military Grid Reference System (MGRS). Tile IDs follow the pattern: UTM zone (2 digits) + latitude band (1 letter) + square identifier (2 letters).

# Search for specific tile
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'tileId': '32TQM'}
)

Product Parameters

Product Types

Sentinel-2 offers various product types:

# Search for Level-1C products
results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI1C'
)

Processing Level

Available processing levels:

S2MSI1C - Level-1C (Top-of-Atmosphere reflectance)
S2MSI2A - Level-2A (Bottom-of-Atmosphere reflectance)

# Search for Level-2A products
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'processingLevel': 'S2MSI2A'}
)

Platform

Sentinel-2 constellation satellites:

S2A - Sentinel-2A (launched 2015)
S2B - Sentinel-2B (launched 2017)
S2C - Sentinel-2C (planned)

# Search for Sentinel-2A data only
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'platform': 'S2A'}
)

Instrument

MSI - Multi-Spectral Instrument (main optical instrument)
AUX - Auxiliary data files

# Search for MSI instrument data
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'instrument': 'MSI'}
)

Sensor Mode

Sentinel-2 sensor modes:

INS-NOBS - Instrument Normal Observation mode
INS-RAW - Instrument Raw mode
INS-VIC - Instrument Vicarious Calibration mode

# Search for normal observation mode
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'sensorMode': 'INS-NOBS'}
)

Cloud Cover

Cloud Cover Percentage

Filter products by cloud cover percentage (0-100%).

# Search for products with less than 20% cloud cover
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'cloudCover': '[0,20]'}
)

# Search for products with exactly 10% cloud cover
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'cloudCover': '10'}
)

Orbit Parameters

Orbit Direction

ASCENDING - Satellite moving from south to north
DESCENDING - Satellite moving from north to south

results = searcher.search(
    collection_name='SENTINEL-2',
    orbit_direction='DESCENDING'
)

Orbit Number

Absolute orbit number (integer value or range).

# Single orbit
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'orbitNumber': '12345'}
)

# Orbit range
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'orbitNumber': '[12345,12350]'}
)

Relative Orbit Number

Relative orbit number (1-143 for Sentinel-2), representing the orbit within a repeat cycle.

# Search for relative orbit 51
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'relativeOrbitNumber': '51'}
)

Quality and Processing

Processing Baseline

Processing baseline version (affects product quality and algorithms used).

# Search for specific processing baseline
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'processingBaseline': '04.00'}
)

# Search for baseline range
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'processingBaseline': '[04.00,05.00]'}
)

Status

Product availability status:

ONLINE - Immediately available for download
OFFLINE - Requires retrieval from long-term storage
ALL - Both online and offline products

# Search for immediately available products
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'status': 'ONLINE'}
)

Mission Take ID

Mission take identifier for specific acquisition sessions.

# Search for specific mission take
results = searcher.search(
    collection_name='SENTINEL-2',
    attributes={'missionTakeId': 'GS2A_20230601T101030_000123_N04.00'}
)

Practical Examples

Example 1: Basic Level-1C Search

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for Level-1C products with low cloud cover
results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI1C',
    aoi_wkt='POLYGON((12.4 41.9, 12.5 41.9, 12.5 42.0, 12.4 42.0, 12.4 41.9))',
    start_date='2023-06-01',
    end_date='2023-06-30',
    attributes={'cloudCover': '[0,20]'}
)

print(f"Found {len(results)} Level-1C products with <20% cloud cover")

Example 2: Level-2A Surface Reflectance

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for atmospherically corrected Level-2A products
results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI2A',
    aoi_wkt='POLYGON((12.4 41.9, 12.5 41.9, 12.5 42.0, 12.4 42.0, 12.4 41.9))',
    start_date='2023-06-01',
    end_date='2023-06-30',
    attributes={
        'cloudCover': '[0,10]',
        'processingLevel': 'S2MSI2A'
    }
)

print(f"Found {len(results)} Level-2A products")

Example 3: Specific Tile Search

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for specific tile over time
results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI1C',
    start_date='2023-01-01',
    end_date='2023-12-31',
    attributes={
        'tileId': '32TQM',
        'cloudCover': '[0,30]'
    }
)

print(f"Found {len(results)} products for tile 32TQM")

Example 4: Time Series Analysis

from phidown import CopernicusDataSearcher
import pandas as pd

searcher = CopernicusDataSearcher()

# Search for consistent time series data
results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI1C',
    start_date='2023-01-01',
    end_date='2023-12-31',
    attributes={
        'tileId': '32TQM',
        'cloudCover': '[0,20]',
        'relativeOrbitNumber': '51'
    }
)

# Group by date to analyze temporal coverage
results['Date'] = pd.to_datetime(results['ContentDate']).dt.date
temporal_coverage = results.groupby('Date').size()

print(f"Found {len(results)} products over {len(temporal_coverage)} unique dates")

Example 5: Multi-Platform Comparison

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Compare data from both Sentinel-2A and Sentinel-2B
s2a_results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI1C',
    aoi_wkt='POLYGON((12.4 41.9, 12.5 41.9, 12.5 42.0, 12.4 42.0, 12.4 41.9))',
    start_date='2023-06-01',
    end_date='2023-06-30',
    attributes={
        'platform': 'S2A',
        'cloudCover': '[0,15]'
    }
)

s2b_results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI1C',
    aoi_wkt='POLYGON((12.4 41.9, 12.5 41.9, 12.5 42.0, 12.4 42.0, 12.4 41.9))',
    start_date='2023-06-01',
    end_date='2023-06-30',
    attributes={
        'platform': 'S2B',
        'cloudCover': '[0,15]'
    }
)

print(f"Sentinel-2A: {len(s2a_results)} products")
print(f"Sentinel-2B: {len(s2b_results)} products")

Example 6: Processing Baseline Filtering

from phidown import CopernicusDataSearcher

searcher = CopernicusDataSearcher()

# Search for products with latest processing baseline
results = searcher.search(
    collection_name='SENTINEL-2',
    product_type='S2MSI2A',
    aoi_wkt='POLYGON((12.4 41.9, 12.5 41.9, 12.5 42.0, 12.4 42.0, 12.4 41.9))',
    start_date='2023-06-01',
    end_date='2023-06-30',
    attributes={
        'processingBaseline': '[04.00,05.00]',
        'cloudCover': '[0,25]'
    }
)

print(f"Found {len(results)} products with processing baseline 4.00-5.00")

Search Optimization Tips

Use Tile IDs: For specific areas, use tile IDs rather than large polygons for better performance.
Filter by Cloud Cover: Always set appropriate cloud cover thresholds for your application.
Choose Processing Level: Use Level-1C for general applications, Level-2A for surface analysis.
Consider Processing Baseline: Newer baselines provide better quality but may not be available for all historical data.
Optimize Temporal Range: Use appropriate date ranges to balance between data availability and search performance.
Use Relative Orbit Numbers: For consistent geometry in time series analysis.
Check Product Status: Use attributes={'status': 'ONLINE'} for immediate download needs.

Common Use Cases

Land Applications: - Vegetation monitoring: Level-2A, low cloud cover - Crop mapping: Level-1C or Level-2A, growing season - Land cover classification: Level-2A, multi-temporal - Deforestation monitoring: Level-1C, specific tiles

Water Applications: - Water quality monitoring: Level-2A, clear conditions - Coastal monitoring: Level-1C, coastal tiles - Lake/reservoir monitoring: Level-2A, specific areas

Urban Applications: - Urban expansion monitoring: Level-1C, consistent orbit - Infrastructure monitoring: Level-1C, high spatial resolution bands - Heat island studies: Level-2A, thermal considerations

Agricultural Applications: - Crop health monitoring: Level-2A, vegetation indices - Yield prediction: Level-1C/2A, time series - Irrigation mapping: Level-2A, SWIR bands

Environmental Applications: - Wildfire monitoring: Level-1C, near real-time - Drought monitoring: Level-2A, vegetation stress - Biodiversity studies: Level-2A, habitat mapping

Technical Specifications

Spectral Bands: - Band 1 (Coastal aerosol): 443 nm, 60m - Band 2 (Blue): 490 nm, 10m - Band 3 (Green): 560 nm, 10m - Band 4 (Red): 665 nm, 10m - Band 5 (Vegetation Red Edge): 705 nm, 20m - Band 6 (Vegetation Red Edge): 740 nm, 20m - Band 7 (Vegetation Red Edge): 783 nm, 20m - Band 8 (NIR): 842 nm, 10m - Band 8A (Vegetation Red Edge): 865 nm, 20m - Band 9 (Water vapour): 945 nm, 60m - Band 10 (SWIR – Cirrus): 1375 nm, 60m - Band 11 (SWIR): 1610 nm, 20m - Band 12 (SWIR): 2190 nm, 20m

Orbital Characteristics: - Altitude: 786 km - Inclination: 98.62° - Repeat cycle: 10 days (single satellite) - Revisit time: 5 days (constellation) - Swath width: 290 km

Radiometric Performance: - Radiometric resolution: 12 bits - Signal-to-noise ratio: >100 for most bands - Radiometric accuracy: <3% (reflectance)

For more detailed information about Sentinel-2 specifications and applications, refer to the official ESA Sentinel-2 documentation.