abnormally large geolocation error for Sentinel-1

[kyutecdaichi]

I am currently conducting InSAR analysis using ISCE2-MintPy with Sentinel data. However, when I convert the geo_temporalCoherence.h5 or geo_velocity.h5 files generated by MintPy to GeoTIFF format (.tif) and display them in QGIS, there is a slight misalignment in the images. I am unsure about the cause or how to resolve this issue. If anyone knows the reason or has a solution, please let me know. I would greatly appreciate your help. Thank you!

• Operating system: Ubuntu 18.04.6 LTS
• Python environment: conda
• MintPy version: 1.5.3
• InSAR processor/product: isce2

[welcome[bot]]

👋 Thanks for opening your first issue here! Please filled out the template with as much details as possible. We appreciate that you took the time to contribute! Make sure you read our contributing guidelines.

[codeautopilot[bot]]

Potential solution

The plan to solve the bug involves verifying and correcting the geographic metadata and geocoding processes. The misalignment issue likely stems from discrepancies in the metadata or the geocoding process. The solution involves:

1. Metadata Verification: Ensure that the input HDF5 files have complete and accurate geographic metadata, including the coordinate reference system (CRS) and geotransform parameters.

2. Geocoding Process: Verify the accuracy of the lookup tables used in the geocoding process and ensure that the interpolation method is appropriate for the dataset.

3. Code Enhancements: Implement additional checks and error handling in the conversion and geocoding scripts to ensure metadata consistency and correct geocoding.

What is causing this bug?

The bug is likely caused by incomplete or incorrect geographic metadata in the input HDF5 files or errors in the geocoding process. Specifically:

• Metadata Issues: The save_gdal.py script checks for the X_FIRST attribute to determine if the file is geocoded. If this attribute or other geographic metadata (like CRS or geotransform parameters) is incorrect or missing, it could lead to misalignment in the output GeoTIFF.

• Geocoding Errors: The geocode.py script relies on lookup tables for accurate geocoding. If these tables are incorrect or misaligned, or if the interpolation method is unsuitable, it could cause the observed misalignment.

Code

Here are some implementation details and code snippets to address the issues:

1. Metadata Verification in save_gdal.py:

   • Add checks for CRS and geotransform parameters.
   • Ensure that the X_FIRST attribute and other necessary metadata are present and correct.

   def verify_metadata(hdf5_file):
      required_attributes = ['X_FIRST', 'Y_FIRST', 'X_STEP', 'Y_STEP', 'CRS']
      for attr in required_attributes:
          if attr not in hdf5_file.attrs:
              raise ValueError(f"Missing required metadata attribute: {attr}")

2. Geocoding Process in geocode.py:

   • Verify the accuracy of the lookup tables.
   • Experiment with different interpolation methods.

   def geocode_with_verification(input_file, lookup_table, method='nearest'):
      if method not in ['nearest', 'linear']:
          raise ValueError("Unsupported interpolation method")
      # Verify lookup table
      if not os.path.exists(lookup_table):
          raise FileNotFoundError("Lookup table not found")
      # Perform geocoding
      # ...

How to replicate the bug

To replicate the bug, follow these steps:

1. Use the save_gdal.py script to convert a MintPy HDF5 file (e.g., geo_temporalCoherence.h5) to a GeoTIFF format.
2. Open the resulting GeoTIFF file in QGIS.
3. Observe the misalignment in the displayed image, which indicates the bug.

Ensure that the input HDF5 file lacks complete geographic metadata or that the lookup table used in the geocoding process is incorrect to replicate the issue accurately.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on src/mintpy/cli/save_gdal.py

## Analysis of `src/mintpy/cli/save_gdal.py` The file `save_gdal.py` is responsible for converting MintPy HDF5 files into GDAL-compatible raster formats, such as GeoTIFF. The script uses command-line arguments to specify the input file, dataset, output file name, and output format. The conversion process relies on the `save_gdal` function from the `mintpy.save_gdal` module. ### Key Points: 1. **Argument Parsing**: - The script uses `argparse` to handle command-line arguments, allowing users to specify the input file, dataset, output file name, and output format. - The default output format is set to `GTiff` (GeoTIFF). 2. **Input File Check**: - The script checks if the input file is geocoded by verifying the presence of the `X_FIRST` attribute in the file's metadata. If this attribute is missing, the script raises a `ValueError`. 3. **Conversion Process**: - The `save_gdal` function is called to perform the actual conversion of the input file to the specified output format. ### Potential Issues: - **Geocoding Check**: The script checks for the `X_FIRST` attribute to determine if the file is geocoded. If this attribute is incorrectly set or missing, it could lead to errors or misalignment in the output GeoTIFF. - **Metadata Handling**: The script does not explicitly handle or verify other geographic metadata such as the coordinate reference system (CRS) or geotransform parameters. Any discrepancies in these parameters could result in misalignment when the GeoTIFF is displayed in GIS software like QGIS. ### Recommendations: - **Verify Metadata**: Ensure that the input HDF5 files have correct and complete geographic metadata, including CRS and geotransform parameters, before conversion. - **Enhance Error Checking**: Consider adding more comprehensive checks for geographic metadata beyond the presence of the `X_FIRST` attribute to prevent potential misalignment issues. - **Debugging**: If misalignment persists, inspect the `save_gdal` function for potential issues in how it handles geographic metadata during the conversion process. In summary, the script appears to be functioning as intended for converting geocoded HDF5 files to GeoTIFF. However, the issue might stem from incomplete or incorrect geographic metadata in the input files or how this metadata is handled during conversion. Further investigation into the `save_gdal` function and the input file's metadata is recommended.

Changes on src/mintpy/cli/geocode.py

## Analysis of `src/mintpy/cli/geocode.py` The `geocode.py` script is responsible for resampling radar-coded files into geo-coordinates or vice versa. It uses various parameters and options to perform geocoding, including interpolation methods, bounding boxes, and lookup tables. Here's a breakdown of the key components and potential issues: ### Key Components 1. **Argument Parsing**: - The script uses `argparse` to handle command-line arguments, allowing users to specify files, datasets, lookup tables, bounding boxes, interpolation methods, and more. - It supports both radar-to-geo and geo-to-radar transformations. 2. **Interpolation/Resampling**: - The script offers two interpolation methods: `nearest` and `linear`. - It uses either `pyresample` or `scipy` for interpolation, with `pyresample` as the default. 3. **Lookup Tables**: - Lookup tables are crucial for geocoding, and the script checks for their existence. - It supports ISCE-2 lat/lon.rdr files as lookup tables. 4. **Bounding Box and Pixel Size**: - Users can specify a bounding box (`--bbox`) and pixel size (`--lalo-step`) for the output grid in geo-coordinates. 5. **Processor Utilization**: - The script allows specifying the number of processors for computation, although it defaults to using a single processor due to performance considerations. ### Potential Issues 1. **Interpolation Method**: - The default interpolation method is `nearest`, which might not be suitable for all datasets. If the misalignment is due to interpolation, switching to `linear` might help. 2. **Lookup Table**: - The script relies heavily on the lookup table for accurate geocoding. If the lookup table is incorrect or misaligned, it could cause the observed misalignment. 3. **Bounding Box and Pixel Size**: - Incorrect bounding box or pixel size settings could lead to misalignment. Ensure that these parameters are set correctly. 4. **Coordinate System Mismatch**: - If the input files and lookup tables are not in the same coordinate system, it could cause alignment issues. 5. **Software Choice**: - The choice between `pyresample` and `scipy` could affect the output. If `scipy` is used, ensure that it supports all required features. ### Recommendations - Verify that the lookup table is correct and corresponds accurately to the input data. - Experiment with different interpolation methods to see if it resolves the misalignment. - Double-check the bounding box and pixel size settings to ensure they match the expected output. - Ensure that all input files and lookup tables are in the same coordinate system. - Consider testing with both `pyresample` and `scipy` to determine if one provides better results for your data. This analysis focuses on potential issues within the `geocode.py` script that could contribute to the reported geolocation error. Further investigation into other parts of the codebase may be necessary to identify the root cause fully.

_Disclaimer: This comment was entirely generated using AI. Be aware that the information provided may be incorrect._ Current plan usage: 0.00% **Have feedback or need help?** [Discord](https://discord.gg/r72ykfvyx7) [Documentation](https://docs.codeautopilot.com/) [support@codeautopilot.com](mailto:support@codeautopilot.com)

[yunjunz]

@kyutecdaichi could you check the alignment of the KMZ file in Google Earth? If there is misalignment there as well, then the problem is in the data itself, due to the inaccurate geolocation, probably from the reference image used in coregistration while running ISCE2/topsStack. If there is no misalignment there, then they may be a problem in the code itself, while saving the HDF5 to GeoTIFF.

[kyutecdaichi]

@yunjunz Thank you for your response. When I checked the KMZ file on Google Maps, I observed that the same misalignment appeared in the KMZ file as well. If I reanalyze the data with ISCE2, could you please advise on any points I should pay attention to, such as parameters for stackSentinel or other related settings? Your help would be much appreciated. Thank you.

[yunjunz]

Use a reference date in 2019 for coregistration, to avoid potential ionospheric displacement.

How big is this misalignment you observed?

[kyutecdaichi]

When I displayed the geo_velocity.kmz on Google Maps, I noticed that the roads were slightly misaligned. By the way, the analysis period was from 2015 to 2023 (excluding December, January, and February of each year).

[yunjunz]

Is it possible that you are using ALOS-2, processed with isce2/stripmapStack, and forgot to run stackStripMap.py with --zero optioin?

[kyutecdaichi]

I am using Sentinel-1 data, not ALOS-2 data. Therefore, I am using the isce2/topsStack's stackSentinel.py for processing.

[yunjunz]

Interesting. Could you post your basic data info here?

• path number
• bounding box in SNWE
• stackSentinel.py command used for isce2 processing?

[kyutecdaichi]

I am not very familiar with 'path number,' but when checking on ASF data search, should I use Path 46 as shown in the photo?

・path number 　46 ・bounding box in SNWE 　'43.031439 43.279942 141.276939 141.652175' ・stackSentinel.py command 　-n '2 3' -c 5 ・dem.py command 　dem.py -a stitch -b 42 43 141 142 -r -s 1 -c

[yunjunz]

My student @weizhenlwz recently encountered the same issue. He re-ran stackSentinel.py with a different --reference_date option and the problem disappeared. We suspect it's due to a problem with the reference acquisition orbit, but has not confirmed it yet in the S1 orbit quality website.

[kyutecdaichi]

I see that the same issue was happening. I will try adding the --reference_date. Thank you!