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Sentinel-1is 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 theMetadata and Filteringsection.
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:
transmitterReceiverPolarisation: ['VV'], ['HH'], ['VV', 'VH'], or
['HH', 'HV']
resolution: 'M' (medium) or 'H' (high). Seethis
referencefor details.
The following code filters the Sentinel-1 collection bytransmitterReceiverPolarisation,instrumentMode, andorbitProperties_passproperties, then calculates composites for several
observation combinations that are displayed in the map to demonstrate how these
characteristics affect the data.
Imagery in the Earth Engine'COPERNICUS/S1_GRD'Sentinel-1ImageCollectionis 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 theSentinel-1 Toolbox)
to derive the backscatter coefficient in each pixel:
Apply orbit file
Updates orbit metadata with a restitutedorbit file(or a precise orbit file if the restituted one is not
available).
GRD border noise removal
Removes low intensity noise and invalid data on scene edges.
(As of January 12, 2018)
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)
Application of radiometric calibration values
Computes backscatter intensity using sensor
calibration parameters in the GRD metadata.
Terrain correction(orthorectification)
Converts data from ground range geometry, which does
not take terrain into account, to σ° using theSRTM 30 meter DEMor theASTER DEMfor 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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-03-06 UTC."],[[["\u003cp\u003eSentinel-1, part of the Copernicus Programme, provides C-band SAR data for various applications.\u003c/p\u003e\n"],["\u003cp\u003ePre-processing of Sentinel-1 data in Earth Engine involves filtering by metadata and applying specific algorithms.\u003c/p\u003e\n"],["\u003cp\u003eMetadata filtering is crucial for creating a homogeneous subset of data based on polarization, instrument mode, and orbit properties.\u003c/p\u003e\n"],["\u003cp\u003eEarth Engine automatically applies preprocessing steps including orbit file application, noise removal, radiometric calibration, and terrain correction to Sentinel-1 GRD data.\u003c/p\u003e\n"],["\u003cp\u003eThe data represents backscatter coefficient (σ°) in decibels (dB) and undergoes several processing steps to derive this value.\u003c/p\u003e\n"]]],["Sentinel-1 data, collected by the European Space Agency, is pre-processed in Earth Engine to obtain calibrated imagery. Key actions include filtering the heterogeneous data using metadata properties like `transmitterReceiverPolarisation`, `instrumentMode`, `orbitProperties_pass`, `resolution_meters`, and `resolution`. This is demonstrated in code examples using JavaScript and Python, calculating temporal means for visualization. Preprocessing steps involve applying orbit files, removing noise, radiometric calibration, and terrain correction to derive the backscatter coefficient in decibels (dB).\n"],null,["# Sentinel-1 Algorithms\n\n[Sentinel-1](https://earth.esa.int/web/sentinel/missions/sentinel-1) is a\nspace mission funded by the European Union and carried out by the European Space Agency\n(ESA) within the Copernicus Programme. Sentinel-1 collects C-band synthetic aperture\nradar (SAR) imagery at a variety of polarizations and resolutions. Since radar data\nrequires several specialized algorithms to obtain calibrated, orthorectified imagery,\nthis document describes pre-processing of Sentinel-1 data in Earth Engine.\n\nSentinel-1 data is collected with several different instrument configurations,\nresolutions, band combinations during both ascending and descending orbits. Because\nof this heterogeneity, it's usually necessary to filter the data down to a\nhomogeneous subset before starting processing. This process is outlined below in the\n[Metadata and Filtering](/earth-engine/guides/sentinel1#metadata-and-filtering) section.\n\nMetadata and Filtering\n----------------------\n\nTo create a homogeneous subset of Sentinel-1 data, it will usually be necessary to\nfilter the collection using metadata properties. The common metadata fields used for\nfiltering include these properties:\n\n1. `transmitterReceiverPolarisation`: \\['VV'\\], \\['HH'\\], \\['VV', 'VH'\\], or \\['HH', 'HV'\\]\n2. `instrumentMode`: 'IW' (Interferometric Wide Swath), 'EW' (Extra Wide Swath) or 'SM' (Strip Map). See [this\n reference](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/acquisition-modes) for details.\n3. `orbitProperties_pass`: 'ASCENDING' or 'DESCENDING'\n4. `resolution_meters`: 10, 25 or 40\n5. `resolution`: 'M' (medium) or 'H' (high). See [this\n reference](https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/resolutions/level-1-ground-range-detected) for details.\n\nThe following code filters the Sentinel-1 collection by\n`transmitterReceiverPolarisation`, `instrumentMode`, and\n`orbitProperties_pass` properties, then calculates composites for several\nobservation combinations that are displayed in the map to demonstrate how these\ncharacteristics affect the data.\n\n### Code Editor (JavaScript)\n\n```javascript\n// Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.\nvar sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')\n .filterDate('2020-06-01', '2020-10-01');\n\n// Filter the Sentinel-1 collection by metadata properties.\nvar vvVhIw = sentinel1\n // Filter to get images with VV and VH dual polarization.\n .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))\n .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))\n // Filter to get images collected in interferometric wide swath mode.\n .filter(ee.Filter.eq('instrumentMode', 'IW'));\n\n// Separate ascending and descending orbit images into distinct collections.\nvar vvVhIwAsc = vvVhIw.filter(\n ee.Filter.eq('orbitProperties_pass', 'ASCENDING'));\nvar vvVhIwDesc = vvVhIw.filter(\n ee.Filter.eq('orbitProperties_pass', 'DESCENDING'));\n\n// Calculate temporal means for various observations to use for visualization.\n// Mean VH ascending.\nvar vhIwAscMean = vvVhIwAsc.select('VH').mean();\n// Mean VH descending.\nvar vhIwDescMean = vvVhIwDesc.select('VH').mean();\n// Mean VV for combined ascending and descending image collections.\nvar vvIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VV').mean();\n// Mean VH for combined ascending and descending image collections.\nvar vhIwAscDescMean = vvVhIwAsc.merge(vvVhIwDesc).select('VH').mean();\n\n// Display the temporal means for various observations, compare them.\nMap.addLayer(vvIwAscDescMean, {min: -12, max: -4}, 'vvIwAscDescMean');\nMap.addLayer(vhIwAscDescMean, {min: -18, max: -10}, 'vhIwAscDescMean');\nMap.addLayer(vhIwAscMean, {min: -18, max: -10}, 'vhIwAscMean');\nMap.addLayer(vhIwDescMean, {min: -18, max: -10}, 'vhIwDescMean');\nMap.setCenter(-73.8719, 4.512, 9); // Bogota, Colombia\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Load the Sentinel-1 ImageCollection, filter to Jun-Sep 2020 observations.\nsentinel_1 = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate(\n '2020-06-01', '2020-10-01'\n)\n\n# Filter the Sentinel-1 collection by metadata properties.\nvv_vh_iw = (\n sentinel_1.filter(\n # Filter to get images with VV and VH dual polarization.\n ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')\n )\n .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))\n .filter(\n # Filter to get images collected in interferometric wide swath mode.\n ee.Filter.eq('instrumentMode', 'IW')\n )\n)\n\n# Separate ascending and descending orbit images into distinct collections.\nvv_vh_iw_asc = vv_vh_iw.filter(\n ee.Filter.eq('orbitProperties_pass', 'ASCENDING')\n)\nvv_vh_iw_desc = vv_vh_iw.filter(\n ee.Filter.eq('orbitProperties_pass', 'DESCENDING')\n)\n\n# Calculate temporal means for various observations to use for visualization.\n# Mean VH ascending.\nvh_iw_asc_mean = vv_vh_iw_asc.select('VH').mean()\n# Mean VH descending.\nvh_iw_desc_mean = vv_vh_iw_desc.select('VH').mean()\n# Mean VV for combined ascending and descending image collections.\nvv_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VV').mean()\n# Mean VH for combined ascending and descending image collections.\nvh_iw_asc_desc_mean = vv_vh_iw_asc.merge(vv_vh_iw_desc).select('VH').mean()\n\n# Display the temporal means for various observations, compare them.\nm = geemap.Map()\nm.add_layer(vv_iw_asc_desc_mean, {'min': -12, 'max': -4}, 'vv_iw_asc_desc_mean')\nm.add_layer(\n vh_iw_asc_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_desc_mean'\n)\nm.add_layer(vh_iw_asc_mean, {'min': -18, 'max': -10}, 'vh_iw_asc_mean')\nm.add_layer(vh_iw_desc_mean, {'min': -18, 'max': -10}, 'vh_iw_desc_mean')\nm.set_center(-73.8719, 4.512, 9) # Bogota, Colombia\nm\n```\n\nSentinel-1 Preprocessing\n------------------------\n\nImagery in the Earth Engine `'COPERNICUS/S1_GRD'` Sentinel-1\n`ImageCollection` is consists of Level-1 Ground Range Detected\n(GRD) scenes processed to backscatter coefficient (σ°) in\ndecibels (dB). The backscatter coefficient represents\ntarget backscattering area (radar cross-section) per unit ground area. Because it can\nvary by several orders of magnitude, it is converted to dB as\n10\\*log~10~σ°. It measures whether the radiated terrain scatters\nthe incident microwave radiation preferentially away from the SAR sensor\ndB \\\u003c 0) or towards the SAR sensor dB \\\u003e 0). This scattering behavior depends on the\nphysical characteristics of the terrain, primarily the geometry of the terrain elements\nand their electromagnetic characteristics.\n\nEarth Engine uses the following preprocessing steps (as implemented by the\n[Sentinel-1 Toolbox](https://sentinel.esa.int/web/sentinel/toolboxes/sentinel-1))\nto derive the backscatter coefficient in each pixel:\n\n1. **Apply orbit file**\n - Updates orbit metadata with a restituted [orbit file](https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/pod/products-requirements) (or a precise orbit file if the restituted one is not available).\n2. **GRD border noise removal**\n - Removes low intensity noise and invalid data on scene edges. (As of January 12, 2018)\n3. **Thermal noise removal**\n - 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)\n4. **Application of radiometric calibration values**\n - Computes backscatter intensity using sensor calibration parameters in the GRD metadata.\n5. **Terrain correction** (orthorectification)\n - Converts data from ground range geometry, which does not take terrain into account, to σ° using the [SRTM 30 meter DEM](/earth-engine/datasets/catalog/USGS_SRTMGL1_003) or the [ASTER DEM](https://asterweb.jpl.nasa.gov/gdem.asp) for high latitudes (greater than 60° or less than -60°).\n\nDataset Notes\n-------------\n\n- Radiometric Terrain Flattening is not being applied due to artifacts on mountain slopes.\n- The unitless backscatter coefficient is converted to dB as described above.\n- 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.\n- GRD SM assets are not ingested because the `computeNoiseScalingFactor()` function in the [border noise removal operation in the S1 toolbox](https://github.com/senbox-org/s1tbx/blob/master/s1tbx-op-calibration/src/main/java/org/esa/s1tbx/calibration/gpf/RemoveGRDBorderNoiseOp.java) does not support the SM mode."]]
