S1 Stability


Hello there!

Could you help me with processing sentinel 1 grd please? Am I doing something wrong ?
I’ve downloaded 2 scenes of same view , same time :


Then I processed them with gpt with graph.xml

<graph id="Graph">
  <node id="Read">
    <parameters class="com.bc.ceres.binding.dom.XppDomElement">
  <node id="Calibration">
      <sourceProduct refid="Read"/>
    <parameters class="com.bc.ceres.binding.dom.XppDomElement">
      <auxFile>Product Auxiliary File</auxFile>
  <node id="Terrain-Correction">
      <sourceProduct refid="Write"/>
    <parameters class="com.bc.ceres.binding.dom.XppDomElement">
      <demName>SRTM 3Sec</demName>
      <incidenceAngleForSigma0>Use projected local incidence angle from DEM</incidenceAngleForSigma0>
      <incidenceAngleForGamma0>Use projected local incidence angle from DEM</incidenceAngleForGamma0>
      <auxFile>Latest Auxiliary File</auxFile>
  <node id="Write(3)">
      <sourceProduct refid="Terrain-Correction"/>
    <parameters class="com.bc.ceres.binding.dom.XppDomElement">
  <node id="Write">
      <sourceProduct refid="Calibration"/>
    <parameters class="com.bc.ceres.binding.dom.XppDomElement">
  <applicationData id="Presentation">
    <node id="Read">
            <displayPosition x="53.0" y="21.0"/>
    <node id="Calibration">
      <displayPosition x="151.0" y="21.0"/>
    <node id="Terrain-Correction">
      <displayPosition x="127.0" y="144.0"/>
    <node id="Write(3)">
      <displayPosition x="274.0" y="142.0"/>
    <node id="Write">
            <displayPosition x="267.0" y="20.0"/>

Processing include :
then converting to db (10*log10(Band))

After processing I got 2x sets of images and look at vh polarization.
As I see one image brighter, than other.
Sometimes difference of the pixel’s db values at same pixel more than 10db.(Sometime it’s almost the same)
Difference between mean valuse - 1.827.

gdalinfo return for scene :
Minimum=-129.179, Maximum=29.212,

Hist + Png


Hist + Png

Best regards,


For a statistical comparison of grey values, don’t focus on mean and min/max because they strongly depend on outliers.
Both your images have the maximum peak at -15, so I don’t think something is wrong. Or do I miss a crucial point?


Looks OK to me, differences in moisture cause large differences in backscatter for distributed scatterers.


Hi mengdahl!

It seems fine - but there are some doubts. Your opinion is good if we look one agriculture field, but not for the scene as it is. We read a paper (but there are areas with stable radar reflecion) - and so this qwestion arised )))
Analysis of Sentinel-1 Radiometric Stability and
Quality for Land Surface Applications
Mohammad El Hajj 1,*, Nicolas Baghdadi 1, Mehrez Zribi 2 and Sébastien Angelliaume 3
1 IRSTEA, UMR TETIS, 500 rue François Breton, 34093 Montpellier cedex 5, France;
2 CNRS, CESBIO, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse cedex 9, France; mehrez.zribi@ird.fr
3 ONERA, BA 701 13661 Salon Cedex AIR, 13661 Salon-de-Provence, France; Sebastien.Angelliaume@onera.fr

  • Correspondence: mohammad.el-hajj@teledetection.fr; Tel.: +33-4-6704-6300
    Academic Editors: Richard Müller and Prasad S. Thenkabail
    Received: 1 March 2016; Accepted: 4 May 2016; Published: 11 May 2016


Forest has generally the most stable backscatter - tropical forest (always wet) is so stable it’s used as a calibration target. If you want to keep the original statistics of the data as intact as possible use the Nearest Neighbour in resampling so that no new backscatter values are generated by interpolation (or do your work in radar geometry).

The rightmost swath appears to have some RadioFrequency Interference (RFI), probably from a ground-based radar.


Dear Marcus
thanks. We supposed so, but as no so good in theme ask here forum.