Sentinel-1 Algorithms

Sentinel-1 is a space mission funded by the European Union and carried out by the European Space Agency (ESA) within the Copernicus Programme. Sentinel-1 collects C-band synthetic aperture radar (SAR) imagery at a variety of polarizations and resolutions. Since radar data requires several specialized algorithms to obtain calibrated, orthorectified imagery, this document describes pre-processing of Sentinel-1 data in Earth Engine.

Sentinel-1 data is collected with several different instrument configurations, resolutions, band combinations during both ascending and descending orbits. Because of this heterogeneity, it's usually necessary to filter the data down to a homogeneous subset before starting processing. This process is outlined below in the Metadata and Filtering section.

Metadata and Filtering

To create a homogeneous subset of Sentinel-1 data, it will usually be necessary to filter the collection using metadata properties. The common metadata fields used for filtering include these properties:

1. transmitterReceiverPolarisation: ['VV'], ['HH'], ['VV', 'VH'], or ['HH', 'HV']
2. instrumentMode: 'IW' (Interferometric Wide Swath), 'EW' (Extra Wide Swath) or 'SM' (Strip Map). See this reference for details.
3. orbitProperties_pass: 'ASCENDING' or 'DESCENDING'
4. resolution_meters: 10, 25 or 40
5. resolution: 'M' (medium) or 'H' (high). See this reference for details.

The following code filters the Sentinel-1 collection by transmitterReceiverPolarisation, instrumentMode, and orbitProperties_pass properties, then calculates composites for several observation combinations that are displayed in the map to demonstrate how these characteristics affect the data.

Code Editor (JavaScript)

// Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.
var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2020-06-01', '2020-10-01');

// Filter the Sentinel-1 collection by metadata properties.
var vvVhIw = sentinel1
  // Filter to get images with VV and VH dual polarization.
  .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
  .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
  // Filter to get images collected in interferometric wide swath mode.
  .filter(ee.Filter.eq('instrumentMode', 'IW'));

// Separate ascending and descending orbit images into distinct collections.
var vvVhIwAsc = vvVhIw.filter(
  ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));
var vvVhIwDesc = vvVhIw.filter(
  ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));

// Calculate temporal means for various observations to use for visualization.
// Mean VH ascending.
var vhIwAscMean = vvVhIwAsc.select('VH').mean();
// Mean VH descending.
var vhIwDescMean = vvVhIwDesc.select('VH').mean();
// Mean VV for combined ascending and descending image collections.
var vvIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VV').mean();
// Mean VH for combined ascending and descending image collections.
var vhIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VH').mean();

// Display the temporal means for various observations, compare them.
Map.addLayer(vvIwAscDescMean, {min: -12, max: -4}, 'vvIwAscDescMean');
Map.addLayer(vhIwAscDescMean, {min: -18, max: -10}, 'vhIwAscDescMean');
Map.addLayer(vhIwAscMean, {min: -18, max: -10}, 'vhIwAscMean');
Map.addLayer(vhIwDescMean, {min: -18, max: -10}, 'vhIwDescMean');
Map.setCenter(-73.8719, 4.512, 9);  // Bogota, Colombia

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.
sentinel_1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(
    '2020-06-01', '2020-10-01'
)

# Filter the Sentinel-1 collection by metadata properties.
vv_vh_iw = (
    sentinel_1.filter(
        # Filter to get images with VV and VH dual polarization.
        ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')
    )
    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
    .filter(
        # Filter to get images collected in interferometric wide swath mode.
        ee.Filter.eq('instrumentMode', 'IW')
    )
)

# Separate ascending and descending orbit images into distinct collections.
vv_vh_iw_asc = vv_vh_iw.filter(
    ee.Filter.eq('orbitProperties_pass', 'ASCENDING')
)
vv_vh_iw_desc = vv_vh_iw.filter(
    ee.Filter.eq('orbitProperties_pass', 'DESCENDING')
)

# Calculate temporal means for various observations to use for visualization.
# Mean VH ascending.
vh_iw_asc_mean = vv_vh_iw_asc.select('VH').mean()
# Mean VH descending.
vh_iw_desc_mean = vv_vh_iw_desc.select('VH').mean()
# Mean VV for combined ascending and descending image collections.
vv_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VV').mean()
# Mean VH for combined ascending and descending image collections.
vh_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VH').mean()

# Display the temporal means for various observations, compare them.
m = geemap.Map()
m.add_layer(vv_iw_asc_desc_mean, {'min': -12, 'max': -4}, 'vv_iw_asc_desc_mean')
m.add_layer(
    vh_iw_asc_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_desc_mean'
)
m.add_layer(vh_iw_asc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_mean')
m.add_layer(vh_iw_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_desc_mean')
m.set_center(-73.8719, 4.512, 9)  # Bogota, Colombia
m

Sentinel-1 Preprocessing

Imagery in the Earth Engine 'COPERNICUS/S1_GRD' Sentinel-1 ImageCollection is consists of Level-1 Ground Range Detected (GRD) scenes processed to backscatter coefficient (σ°) in decibels (dB). The backscatter coefficient represents target backscattering area (radar cross-section) per unit ground area. Because it can vary by several orders of magnitude, it is converted to dB as 10*log₁₀σ°. It measures whether the radiated terrain scatters the incident microwave radiation preferentially away from the SAR sensor dB < 0) or towards the SAR sensor dB > 0). This scattering behavior depends on the physical characteristics of the terrain, primarily the geometry of the terrain elements and their electromagnetic characteristics.

Earth Engine uses the following preprocessing steps (as implemented by the Sentinel-1 Toolbox) to derive the backscatter coefficient in each pixel:

1. Apply orbit file
   • Updates orbit metadata with a restituted orbit file (or a precise orbit file if the restituted one is not available).
2. GRD border noise removal
   • Removes low intensity noise and invalid data on scene edges. (As of January 12, 2018)
3. Thermal noise removal
   • Removes additive noise in sub-swaths to help reduce discontinuities between sub-swaths for scenes in multi-swath acquisition modes. (This operation cannot be applied to images produced before July 2015)
4. Application of radiometric calibration values
   • Computes backscatter intensity using sensor calibration parameters in the GRD metadata.
5. Terrain correction (orthorectification)
   • Converts data from ground range geometry, which does not take terrain into account, to σ° using the SRTM 30 meter DEM or the ASTER DEM for high latitudes (greater than 60° or less than -60°).

Dataset Notes

• Radiometric Terrain Flattening is not being applied due to artifacts on mountain slopes.
• The unitless backscatter coefficient is converted to dB as described above.
• Sentinel-1 SLC data cannot currently be ingested, as Earth Engine does not support images with complex values due to inability to average them during pyramiding without losing phase information.
• GRD SM assets are not ingested because the computeNoiseScalingFactor() function in the border noise removal operation in the S1 toolbox does not support the SM mode.