Querying Data

The insar-query.py script is your gateway to accessing InSAR data from the Norwegian Ground Motion Service. This guide covers all available options and best practices.

Basic Usage

python insar-query.py --path /path/to/output --bbox=lon1,lat1,lon2,lat2

Command-Line Parameters

Required Parameters

--path

Directory where downloaded data will be stored.

--path /data/insar-analysis

--bbox

Bounding box coordinates defining your area of interest.

Format: longitude1,latitude1,longitude2,latitude2 (WGS84/EPSG:4326)

--bbox=7.986722817,58.149864623,7.991700669,58.146240529

Coordinate System Information

• Input: Decimal degrees in WGS84 (longitude,latitude)
• Output: Both coordinate systems in CSV:
  • easting, northing in EPSG:3035 (ETRS89-LAEA) - primary
  • longitude, latitude in WGS84 (EPSG:4326) - converted
• EPSG:3035 is used by GIS viewer for higher accuracy in European analysis
• Keep areas reasonably small for faster processing

Optional Parameters

--period

Time period for Sentinel-1 datasets.

Available options: - 2019-2023 (default) - 2020-2024 - latest - all

--period 2020-2024

--list-datasets

Display all available datasets on the server without downloading.

python insar-query.py --list-datasets

Example

Small Urban Area

python insar-query.py \
    --path /data/oslo-center \
    --bbox=10.7300,59.9100,10.7400,59.9200 \
    --period all

Understanding the Data

File Structure

After querying, your output directory will contain:

/path/to/output/
├── dataset1_ascending.csv
├── dataset1_descending.csv
├── dataset2_ascending.csv
└── dataset2_descending.csv

CSV File Contents

Each CSV file contains InSAR measurement points with columns:

Column	Description
geometry	Point coordinates (WKT format)
velocity	Ground motion velocity (mm/year)
coherence	Data quality measure (0-1)
date	Measurement date
track	Satellite track (ascending/descending)

Data Quality Considerations

Coherence Values

• High (0.8-1.0): Excellent data quality
• Medium (0.6-0.8): Good data quality
• Low (0.4-0.6): Moderate quality, use with caution
• Very Low (<0.4): Poor quality, consider filtering

Spatial Coverage

• Urban areas: Better coverage due to stable reflectors
• Rural areas: Sparser coverage, especially in vegetation
• Coastal areas: Variable quality depending on conditions

Troubleshooting

Common Issues

No data returned

• Check bounding box coordinates are valid
• Verify the area has InSAR coverage on https://insar.ngu.no/
• Try a different time period

Download failures

• Check internet connection
• Verify server availability with --list-datasets
• Try smaller bounding box areas

Slow downloads

• Large areas take longer to process
• Consider splitting into smaller regions
• Use latest period for faster queries

Best Practices

1. Start Small: Begin with small areas to understand data patterns
2. Check Coverage: Use --list-datasets to verify data availability
3. Multiple Periods: Compare different time periods for comprehensive analysis
4. Document Queries: Keep track of bounding boxes and periods used

Next Steps

Once you have downloaded data, proceed to Processing Data to analyze the InSAR measurements for significant changes.