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Sentinel-2 Data Sources and Filtering

DARTS supports multiple sources for Sentinel-2 data, each with different characteristics and filtering capabilities. This guide explains the available options and how to use them effectively.

Available Data Sources

CDSE Scenes (Individual Scenes)

Source identifier: cdse (default)

Individual Sentinel-2 Level-2A scenes from the Copernicus Data Space Ecosystem (CDSE) accessed via their STAC API.

Characteristics:

  • Resolution: 10m, 20m, and 60m bands (all resampled to 10m during loading)
  • Coverage: Individual satellite passes (~100km × 100km per scene)
  • Temporal resolution: Every 5 days (combined Sentinel-2A and 2B)
  • Processing level: Level-2A (surface reflectance)
  • Quality information: Scene Classification Layer (SCL) for cloud/snow masking
  • Available bands: All Sentinel-2 bands (B01-B12, plus SCL)

Best for:

  • High temporal resolution analysis
  • Cloud-free scene selection
  • Specific date requirements
  • Small to medium study areas

Example: 

[darts.inference.sentinel2-sequential]
raw-data-source = "cdse"
aoi-file = "./data/myaoi.gpkg"
start-date = "2024-07-01"
end-date = "2024-09-30"
max-cloud-cover = 10
max-snow-cover = 5

CDSE Mosaics (Quarterly Composites)

Source identifier: cdse-mosaic

Quarterly mosaic composites from CDSE's Global Mosaics collection.

Characteristics:

  • Resolution: 10m only (composite of multiple acquisitions)
  • Coverage: Global MGRS tiles (~110km × 110km)
  • Temporal resolution: Quarterly (Q1: Jan-Mar, Q2: Apr-Jun, Q3: Jul-Sep, Q4: Oct-Dec)
  • Processing level: Level-3 (composite surface reflectance)
  • Quality information: Number of observations per pixel (used for quality masking)
  • Available bands: RGB-NIR only (B02-Blue, B03-Green, B04-Red, B08-NIR, plus observations layer)

Best for:

  • Large-scale processing (regional to continental)
  • Seasonal analysis
  • Reduced cloud interference (composites select best pixels)
  • Lower storage requirements than individual scenes

Quality Masking:

Unlike scenes with SCL, mosaics use an observation count layer:

  • Invalid (quality_mask = 0): observations == 0
  • Low quality (quality_mask = 1): 1 ≤ observations ≤ 3
  • High quality (quality_mask = 2): observations > 3

Example: 

[darts.inference.sentinel2-sequential]
raw-data-source = "cdse-mosaic"
aoi-file = "./data/myaoi.gpkg"
quarters = [3]  # Summer quarter
years = [2024, 2025]

GEE (Google Earth Engine)

Source identifier: gee

Sentinel-2 Level-2A scenes from Google Earth Engine.

Characteristics:

  • Resolution: 10m, 20m, and 60m bands (resampled to 10m)
  • Coverage: Individual satellite passes
  • Temporal resolution: Every 5 days
  • Processing level: Level-2A (surface reflectance, harmonized)
  • Quality information: SCL cloud/snow masking
  • Available bands: All Sentinel-2 bands (B01-B12, plus SCL)

Processing inconsistencies

GEE's Sentinel-2 data has been loaded only once and uses different processing levels across years. This can result in spectral inconsistencies that may reduce model performance. CDSE is recommended for production use.

Best for:

  • Users already working on Google Cloud Platform
  • Integration with other GEE datasets
  • Prototyping and exploration

Example: 

[darts.inference.sentinel2-sequential]
raw-data-source = "gee"
ee-project = "my-gee-project"
aoi-file = "./data/myaoi.gpkg"
start-date = "2024-07-01"
end-date = "2024-09-30"

Scene Selection Methods

Four mutually exclusive methods for selecting Sentinel-2 data:

1. Direct Scene IDs

Provide exact scene identifiers. Works with all sources.

scene-ids = [
    "20230701T194909_20230701T195350_T11XNA",
    "20230704T195909_20230704T200350_T11XNA"
]

For mosaics: 

scene-ids = [
    "Sentinel-2_mosaic_2024_Q3_33UUP_0_0"
]

2. Scene ID File

Load scene IDs from a file (one per line, JSON array).

scene-id-file = "./scene_ids.json"

3. Tile IDs with Filtering

Specify MGRS tile IDs and apply temporal/quality filters.

tile-ids = ["33UUP", "33UVP"]
start-date = "2024-07-01"
end-date = "2024-09-30"
max-cloud-cover = 10
max-snow-cover = 5

4. AOI with Filtering

Use a geographic area of interest (shapefile/geopackage) with filters.

aoi-file = "./data/myaoi.gpkg"
start-date = "2024-07-01"
end-date = "2024-09-30"
max-cloud-cover = 10
max-snow-cover = 5

Filtering Parameters

When using tile IDs or AOI files for scene selection, you can apply various filters to narrow down the results based on temporal and quality criteria.

Date-Based Filtering

You can filter scenes temporally using either precise date ranges or more flexible month/quarter/year combinations, depending on your data source.

Date Range (CDSE/GEE scenes only)

Precise date range in YYYY-MM-DD format:

start-date = "2024-07-01"
end-date = "2024-09-30"

Month/Quarter/Year (CDSE only)

Flexible temporal selection:

For CDSE scenes - use months or quarters: 

months = [6, 7, 8]  # June through August
# quarters = [2, 3] # this would be the equivalent to month = [4, 5, 6, 7, 8, 9]
years = [2023, 2024]

For CDSE mosaics - use quarters: 

quarters = [3]  # Q3 = July-September
years = [2023, 2024, 2025]

GEE limitations

Month/quarter/year filtering is not supported for GEE. Use date ranges instead.

Quality-Based Filtering

For individual scenes (not mosaics), you can filter based on cloud and snow cover percentages derived from the Scene Classification Layer.

Cloud Cover (CDSE/GEE scenes only)

Maximum acceptable cloud cover percentage (0-100):

max-cloud-cover = 10  # Default

Disabling cloud filtering: 

max-cloud-cover = 100  # Accept all scenes
# Or, alternativly, leave it empty since it defaults to None

Snow Cover (CDSE/GEE scenes only)

Maximum acceptable snow cover percentage (0-100):

max-snow-cover = 5  # Default
# Or, alternativly, leave it empty since it defaults to None

Mosaic quality

CDSE mosaics don't have cloud/snow cover metadata. Quality is determined by the number of observations per pixel instead.

Comparison Table

Feature CDSE Scenes CDSE Mosaics GEE
Resolution 10/20/60m 10m 10/20/60m
Available bands All (B01-B12) RGB-NIR only All (B01-B12)
Temporal Every 5 days Quarterly Every 5 days
Cloud filtering ✅ ❌ ✅
Snow filtering ✅ ❌ ✅
Date range ✅ ❌ ✅
Month filtering ✅ ✅* ❌
Quarter filtering ❌ ✅ ❌
Quality mask SCL-based Observation count SCL-based
Storage efficiency Low High Low
Processing speed Medium Fast Slow (network)
Best for Precise dates Large areas GCP workflows

* Automatically converted to quarters

Usage Examples

The following examples demonstrate common use cases for different Sentinel-2 data sources and filtering approaches.

Example 1: Summer Analysis with CDSE Scenes

High-quality summer scenes with strict cloud filtering:

[darts.inference.sentinel2-sequential]
raw-data-source = "cdse"
aoi-file = "./data/study_area.gpkg"
start-date = "2024-06-15"
end-date = "2024-08-31"
max-cloud-cover = 5
max-snow-cover = 0

Example 2: Multi-Year Quarterly Mosaics

Large-scale seasonal analysis using mosaics:

[darts.inference.sentinel2-sequential]
raw-data-source = "cdse-mosaic"
tile-ids = ["33UUP", "33UVP", "33UWP"]
quarters = [2, 3]  # Spring and summer
years = [2022, 2023, 2024]

Example 3: Specific Tiles with Month Filtering

CDSE scenes for specific months across multiple years:

[darts.inference.sentinel2-sequential]
raw-data-source = "cdse"
tile-ids = ["33UUP"]
months = [7, 8]  # July and August
years = [2020, 2021, 2022, 2023, 2024]
max-cloud-cover = 15

Example 4: Google Earth Engine Workflow

Using GEE with date range (required for GEE):

[darts.inference.sentinel2-sequential]
raw-data-source = "gee"
ee-project = "my-gee-project"
aoi-file = "./data/study_area.gpkg"
start-date = "2024-07-01"
end-date = "2024-07-31"
max-cloud-cover = 10

Best Practices

Follow these guidelines to optimize your Sentinel-2 data acquisition and processing workflow.

Choosing a Data Source

  1. Default choice: Use cdse for most applications
  2. Large-scale processing: Use cdse-mosaic for regions > 1000 km²
  3. Seasonal analysis: Use cdse-mosaic with quarters
  4. Specific dates needed: Use cdse or gee with date ranges
  5. GCP integration: Use gee only if already on Google Cloud

Filtering Strategy

  1. Start conservative: Begin with low cloud/snow thresholds (5-10%)
  2. Iterate if needed: Increase thresholds if insufficient scenes found
  3. Check coverage: Use prep-data to verify scene availability before full pipeline runs

Download Optimization

  1. Enable caching: Use raw-data-store to avoid downloading the same data again and again (enabled by default)
  2. Disable for one-off: Use --no-raw-data-store for single-use processing