Testing RRINGG

This section provides a step-by-step guide to test RRINGG using two prepared datasets. The goal is to ensure that your installation is working correctly and that you can reproduce the expected results.

All required data and reference outputs can be downloaded here:

After download, extract the archive and place the contents into your project folder.

Overview of the tests

We provide two test cases:

  1. EPOS TCS dataset - Demonstrates how to preprocess EPOS CSV data into raster format. - Runs a correction of the InSAR velocity field.

  2. FlatSim dataset - Already provided in raster format (no preprocessing required). - Directly runs velocity-field correction.

For both tests, the corrected outputs can be compared against the provided reference solutions.

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Test 1 — EPOS TCS dataset

The EPOS TCS dataset comes in CSV format and must be converted to raster before processing. This step ensures that RRINGG can ingest the data.

  1. Convert CSV to raster:

python ./tools/csv2raster.py \
  -ts ./data/raw/ics/DTSLOS_CNRIREA_20160714_20231030_ZII6.csv \
  -op ./data/raw/ics/

This generates several raster layers (mean velocity, LOS vector, etc.) in ./data/raw/ics.

  1. Run velocity-field correction:

python ./rringg.py \
  -po correction \
  -vr ./data/raw/ics/CNRIREA_MV-LOS_geo_5rlks.tiff \
  -lo ./data/raw/ics/CNRIREA_CosNEU_geo_5rlks.tiff \
  -ou ./data/output/ics \
  -rs BLGN00ITA COL100ITA AULL00ITA MODE00ITA MOPS00ITA \
  -hm simple -iv

  • -rs removes specific GNSS stations to test robustness.

  • -hm simple specifies the convex hull method.

  • -iv forces LOS vector inversion.

The corrected velocity raster is written to ./data/output/ics.

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Test 2 — FlatSim dataset

The FlatSim dataset is already in GeoTIFF raster format. This means you can directly run the correction without any preprocessing.

Run correction:

python ./rringg.py \
  -po correction \
  -vr ./data/raw/flatsim/CNES_MV-LOS_geo_16rlks.tiff \
  -lo ./data/raw/flatsim/CNES_CosENU_geo_16rlks.tiff \
  -ou ./data/output/flatsim \
  -rs MTHO00GRC KRYO00GRC ANKY00GRC KITH00GRC \
      VASS00GRC KORO00GRC SKYR00GRC PAT000GRC

Outputs are stored in ./data/output/flatsim.

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Expected outputs

Each test produces corrected rasters that can be validated against the provided reference files.

Expected Outputs

Test case

Output directory

Key output files

EPOS TCS

./data/output/ics/

RRINGG_CNRIREA_MV-LOS_geo_5rlks.tiff RRINGG_CNRIREA_MV-LOS_geo_5rlks.meta RRINGG_EPOS_SCATTER_PLOT_AFTER_FITTING.png RRINGG_EPOS_SCATTER_PLOT_BEFORE_FITTING.png RRINGG_EPOS_SPATIAL_COVERAGE_AFTER_FITTING.png RRINGG_EPOS_SPATIAL_DISTRIBUTION.png RRINGG_EPOS_STATIONS_MAP.png RRINGG_EPOS_VALID_STATION_DISTRIBUTION_MAP.png mexique_timeserie.png

FlatSim

./data/output/flatsim/

RRINGG_CNES_MV-LOS_geo_16rlks.tiff RRINGG_CNES_MV-LOS_geo_16rlks.meta RRINGG_EPOS_SCATTER_PLOT_AFTER_FITTING.png RRINGG_EPOS_SCATTER_PLOT_BEFORE_FITTING.png RRINGG_EPOS_SPATIAL_COVERAGE_AFTER_FITTING.png RRINGG_EPOS_SPATIAL_DISTRIBUTION.png RRINGG_EPOS_STATIONS_MAP.png RRINGG_EPOS_VALID_STATION_DISTRIBUTION_MAP.png

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Validation

To validate your run:

  1. Check file generation Ensure the corrected rasters (RRINGG_*_MV-LOS_geo_*rlks.tiff) and metadata file (RRINGG_*_MV-LOS_geo_*rlks.meta) are created in the output folders.

  2. Visual inspection Open the outputs in QGIS or another GIS software and compare them with the reference rasters included in the dataset archive.

  3. Numerical comparison Use gdalinfo or gdal_calc.py to check that statistical properties (mean, min, max) match the reference outputs within tolerance.

If your results are consistent, your RRINGG installation and configuration are working as expected.