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Radar Products Calibration



The calibration of Radar EO data is done in the ESA Charter Mapper via a dedicated processing service, named Radar Product Calibration (SAR-Calib), which performs the radiometric calibration of multi-sensor SAR data, given under multiple product types and processing levels, and computes the radar backscatter coefficient at each polarization.

The possible operations applicable with respect to the nature of the products are:

  1. Orbit correction (if necessary),

  2. Border noise removal (if necessary),

  3. Calibration,

  4. Multilooking (if necessary),

  5. Speckle filtering (only for overview generation),

  6. Terrain correction (if not already geocoded),

  7. Conversion to dB,

  8. Image stretching and RGB composite (only for overview generation),

  9. Creation of output products.

This service runs systematically during the acquisitions ingestion process, and is primarily meant to process focused SAR data that has been detected, multi-looked and projected to the ground range using an Earth ellipsoid model (e.g. WGS84) and a scene-averaged value of terrain height (e.g. ICEYE GRD data).

Output single-band Sigma Nought assets included into a SAR Calibrated Dataset can be used as input for further thematic processing (e.g. co-location, change detection).


The SAR-Calib service implements the workflow depicted below.

graph TB i[(COS-2)] style i fill:#ffde86,stroke:#333,color:#282828,stroke-width:2px i --> a1(EO data product) subgraph Inputs style Inputs fill:#e8e8e8,stroke:#333,color:#282828 a1[/SAR Dataset/] style a1 fill:#acc8ff,stroke:#333,color:#282828,stroke-width:2px end subgraph SAR-Calib a1 --> orbcor[Orbit Correction] style orbcor fill:#ffcccc,stroke:#cc0000 orbcor --> bornore[Border Noise Removal] style bornore fill:#ffcccc,stroke:#cc0000 bornore --> calib[Radiometric Calibration] calib --> ml[Multilooking] style ml fill:#ffcccc,stroke:#cc0000 ml --> tc1[Terrain Correction] style tc1 fill:#ffcccc,stroke:#cc0000 tc1 --> ltodb1[Linear to dB] ltodb1 --> cog1[Convert to COG] cog1 --> p1(Sigma Nought) style p1 fill:#cfdfff,stroke:#333,color:#282828 ml --> despeckle[Speckle Filtering] despeckle --> tc2[Terrain Correction] tc2 --> ltodb2[Linear to dB] style tc2 fill:#ffcccc,stroke:#cc0000 ltodb2 --> overcreat[Image stretching and RGB composite creation] overcreat --> cog2[Convert to COG] cog2 --> p2(overview/s) style p2 fill:#cfdfff,stroke:#333,color:#282828 p1 --> stac[Create STAC item] p2 --> stac[Create STAC item] end subgraph Outputs style Outputs fill:#e8e8e8,stroke:#333,color:#282828 stac --> o1[/Sigma Nought asset for each polarization: HH, HV, VH, and VV/] style o1 fill:#acc8ff,stroke:#87afff,color:#282828,stroke-width:2px stac --> o2[/overview-full, overview-dual, overview-hh, overview-hv, etc./] style o2 fill:#acc8ff,stroke:#87afff,color:#282828,stroke-width:2px end

The SAR-Calib service uses SNAP graphs or plain matrix calculations to apply the processing steps shown in the above workflow. The output of the service is a Stac Item with the \(sigma_0\) values provided in decibels and the overview/s product/s.


Steps highlighetd in red in the SAR-Calib flowchart indicates the ones which are executed only if/when necessary according to the specific source EO data product to be calibrated. For more information see below paragraph.

A detailed description of each step of the SAR-Calib chain is provided below.

Orbit correction

The first step, Orbit Correction, is built on SNAP "Apply orbit" toolset and gathers and employs precise orbits which are necessary to improve the geocoding of SAR images. This step is built with SNAP and is currently available only for Sentinel-1 GRD products.

Border noise removal

The second step, Border Noise Removal, is meant to clean out border noise patterns in near and far range zones from SAR data. This step is built with SNAP and is currently only available for Sentinel-1 GRD data (SNAP “Sentinel-1 Remove GRD Border Noise)1.

Radiometric calibration

The third step, Calibration, performs the radiometric calibration of SAR detected image to convert DN into a backscatter coefficient (radar brightness, beta nought \(beta_0\) which represents radar reflectivity per unit area in slant range, or the other forms of average backscatter coefficient: sigma nought \(sigma_0\), and gamma nought \(gamma_0\)). SAR detected images are commonly calibrated into sigma nought \(sigma_0\) which represents averaged radar cross section per unit ground area in \(m^2/m^2\). Sigma nought includes the influence of the terrain into the backscatter signal. In the computation of sigma nought, the local terrain slope derived from the DEM auxiliary data is often employed to derive the local incidence angle \(theta\). For some missions (Kompsat-5 or TerraSAR-X) local incidence angle values over the entire scene can be directly extracted through the decryption of Geocoded Incidence Angle Mask (GIM) included into the source product package2. Such local incidence angles are then used to obtain Sigma nought from the source DN values rescaled with rescaling/calibration factors included into product metadata. The radiometric calibration step is built using either SNAP or GDAL according to the different types of SAR mission.


The fourth step, Multilooking, is employed to reduce the speckle present in oversampled intensity images obtained from SAR data. Averaging neighboring pixels in intensity images is in fact a common practice for noise smoothing. Indeed, this speckle effect is present because SAR images are often provided oversampled by a factor of N, and the sample-rate above the Nyquist rate, to avoid Aliasing. Thus, the multilooking factor R is computed as following:

\[ R = \frac {res_{range} \times res_{azimuth}} {spacing_{range} \times spacing_{range}} \]

where \(res_{range}\) is the range resolution in (m), \(res_azimuth\) is the azimuth resolution in (m), \(spacing_range\) is the range pixel spacing, which is the distance between adjacent pixels perpendicular to the flight path in (m), and \(spacing_azimuth\) is the azimuth pixel spacing, which is the distance between adjacent pixels parallel to the flight path in (m). The computation of (R) allows defining the NxN window over which to average the source calibrated intensity image. Multilooking reduces the presence of Speckle in oversampled SAR intensity images.

Speckle filtering

The fifth step, Speckle Filtering, is about SAR image despeckling, which consists of a radiometric enhancement required for a better interpretation of features into input SAR images due to their typical grainy salt-and-pepper appearance. To remove granular noise in SAR imagery multiple speckle filtering algorithms are available in the literature3, 4, 5, 6,7. This processor employs the Lee Sigma filter4,5. The Lee’s method is widely employed cause it preserves edges while filtering averages of the image. This step is currently built only using SNAP and is applied in the generation of the Overview products.


Speckle filtering is applied only in the creation of the multi-band overviews at 8-bit (products meant to be used only for visual purposes). Instead the speckle filtering is NOT applied in the creation of the single-band Sigma Nought assets in Float32 (the assets to be used for further processing) to do not alter the product and preserve linear features.

Terrain correction

The step six, Terrain Correction, performs the geocoding of SAR data in slant range geometry by using a Range-Doppler approach. Terrain correction is needed to remove distortion into SAR images caused by the side-looking of SAR sensors. In this step the Range Doppler orthorectification method (Small and Schubert, 2008)8 is employed, as given into the SNAP Range Doppler Terrain Correction tool, to obtain geocoded and terrain corrected SAR data.

Convert linear to dB

The seventh step, Linear to dB, consists of a logarithmic scaling of linear values of Sigma nought. This is needed because the SAR intensity can vary many orders of magnitude. Thus, after this final step backscatter values are expressed in dB.

Creation of overviews

The step eight, Overview Image Creation, generates full-resolution overviews from calibrated radar EO data. Output visual products are given as grayscale single band or RGB band composite (using multiple polarizations, when available). Hereinafter is described how RGB composites are made in this service. In the case of dual-pol data, VV&VH or HH&HV, the RGB is created with R=co-pol, G=B=cross-pol. This first representation highlights mainly urban areas, the different orientation of buildings, and vegetation. Instead, in case of full-poll data the RGB is derived as follows: R=HH, G=HV, B=VV. This second representation improves the dual-pol representation, highlighting better volumetric scattering, bare soils and urban areas. This step employs an image stretching, which is applied to consider only Sigma nought within predefined minimum and maximum values while generating the Overview image product (see Table 2).


The inputs are geo-referenced or geocoded SAR detected images from multiple sensors (e.g. SAR L1 detected products like TSX-1 L1B EEC, or GRD for ICEYE, RCM and S1).


The list of SAR sensors supported by the ESA Charter Mapper can be found here.

Steps employed for different products and missions

All these eight steps are mapped in the following Table 1 which provides a complete outlook of the processing chain applied for a selection of the current Charter SAR payload. The full list of the ESA Charter Mapper supported SAR sensors is available in the Satellite configuration table. Table 1 indicates software/tools employed in the steps required for the processing of each SAR mission. All the columns except for the Speckle Filtering and the Overview Creation ones indicate the steps of the primary chain required for the creation of the physical meaning products (Sigma Nought product encoded in Float-32). Instead, the Speckle Filtering and the Overview Creation columns indicate the steps of the secondary chain which generates the visual products (Overview product encoded in UInt-8).

Mission Sensor Lev Prod Data type Mode Orbit Corr. Border noise rem. Calibr. Multi looking Speckle filter Terrain corr. Linear to dB Overview Creation

Table 1 - General outlook about the processing steps required for the creation of physical meaning and visual products in the ESA Charter Mapper's Radar Calibration systematic service.

Signal dynamic ranges used for stretching Sigma Nought

In the creation of all SAR products derived from the systematic SAR calibration (either the geophysical asset or the overview one at 8 bit) an image stretching is applied to consider only Sigma nought within predefined minimum and maximum values. This helps optimize visualization on the screen of the assets. The minimum and maximum values in dB included in Table 2, are specific to mission, SAR band (or frequency) and polarization.

Mission SAR-band Co-pol (VV/HH) [min,max] in dB Cross-pol (VH/HV) [min,max] in dB
ICEYE X [-22,2] [-27,-3]
Kompsat-5 X [-22,2] [-27,-3]
TerraSAR-X / TanDEM-X X [-22,2] [-27,-3]
Gaofen-3 C [-20,0] [-26,-5]
Radarsat-2 C [-20,0] [-26,-5]
RCM C [-20,0] [-26,-5]
Sentinel-1 C [-20,0] [-26,-5]
ALOS-2 L [-27,0] [-35,-5]
SAOCOM-1 L [-27,0] [-35,-5]

Table 2 - Signal dynamic ranges used for stretching Sigma Nought during the generation of SAR overviews products. The values in dB are given for multiple missions, SAR-bands and polarizations.


Product specifications for the Radar Products Calibration service can be found in the following following tables 3 and 4.

Attribute Value / description
Long Name Sigma Nougth at [HH, VV, VH, HV] polarization in dB
Short Name s0_db_B_PP (where PP is the polarization [HH, HV, VH, VV] and B the SAR-band [X,C,L]), e.g.: s0_db_x_hh, s0_db_x_hv, … , s0_db_l_vv
Description Backscatter coefficient σ_0, or sigma-nought, for [HH, VV, VH, HV] polarization derived from a multi-looked and projected to the ground range product (GRD) of the SAR focused signal expressed in dB
Processing level Native or L1D when GRD products at source
Data Type Float 32 bit
Band Single for each polarization
Format COG
Projection Native (or EPSG:4326 - WGS84 if not map projected)
Units dB

Table 3 - Product specification for Sigma Nought Product of the Radar Products Calibration service.

The second type of output product (see Table 4) is a Browse image given as full resolution RGBA composite (multibands GeoTIFF in COG format).

Attribute Value / description
Long Name Full resolution RGBA composite or grayscale single band image from radar EO data
Short Name overview-vv, overview-vh, overview-hv, overview-hh (grayscale), overview-dual, overview-full (false color composite)
Description Grayscale single band geo-referenced image from single polarization or RGBA composites from multi polarization SAR data (including alpha band).
Processing level Native or L1D when GRD products at source
Data Type UnSigned 8-bit
Band 4
Format COG
Projection Native (or EPSG:4326 - WGS84 if not map projected)
Valid Range [0 - 255]
Fill Value 0

Table 4 - Product specification for Browse Image Product of the Radar Products Calibration service.

  1. Ali I., Cao S., Naeimi V., Paulik C. and Wagner W., (2018), "Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 3, pp. 777-786, March 2018, DOI: 10.1109/JSTARS.2017.2787650

  2. Fritz, T., Eineder, M.: TerraSAR-X Basic Product Specification Document, TX-GS-DD-3302, Issue 1.5, February 2008, available at:

  3. Lee J. S., L. Jurkevich, P. Dewaele, P. Wambacq & A. Oosterlinck, (1994), "Speckle filtering of synthetic aperture radar images: A review, Remote Sensing Reviews", 8:4, 313-340, DOI: 10.1080/02757259409532206

  4. Lee, J. S., Ainsworth , T. L., Wang, Y. & Chen, K. S., (2015), "Polarimetric SAR Speckle Filtering and the Extended Sigma Filter", IEEE Transactions on Geoscience and Remote Sensing, 53, 1150-1160, DOI: 10.1109/TGRS.2014.2335114

  5. Lee J. S., Wen J. H., Ainsworth T. L., et al., (2009), "Improved Sigma Filter for Speckle Filtering of SAR Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 1, pp. 202-213, Jan. 2009, DOI: 10.1109/TGRS.2008.2002881

  6. Lopes A., R. Touzi and E. Nezry, (1990), "Adaptive speckle filters and scene heterogeneity," in IEEE Transactions on Geoscience and Remote Sensing, vol. 28, no. 6, pp. 992-1000, DOI: 10.1109/36.62623

  7. G. Vasile, E. Trouve, Jong-Sen Lee and V. Buzuloiu, (2006) "Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, pp. 1609-1621, June 2006, DOI: 10.1109/TGRS.2005.864142

  8. Small D., Schubert A., (2019), "Guide to S1 Geocoding", UZH-S1-GC-AD, Technical Note, Issue 1.10, 26.03.2019, 42p, available at: