Joss paper

.github/workflows/joss_paper.yml

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+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: JOSS Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

paper/paper.bib

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+@article{chang2014detection,
+title = {Detection of cavity migration and sinkhole risk using radar interferometric time series},
+journal = {Remote Sensing of Environment},
+volume = {147},
+pages = {56-64},
+year = {2014},
+issn = {0034-4257},
+doi = {10.1016/j.rse.2014.03.002},
+url = {https://www.sciencedirect.com/science/article/pii/S0034425714000674},
+author = {Ling Chang and Ramon F. Hanssen},
+keywords = {Sinkholes, Cavity monitoring, Satellite radar interferometry},
+abstract = {Upward migration of underground cavities can pose a major hazard for people and infrastructure. Either via sudden collapse sinkholes, or by eroding the support of building foundations, a migrating cavity can cause the collapse of buildings, water defense systems, or transport infrastructure. The main problem for risk assessment is the lack of a priori knowledge on the location of a potentially hazardous cavity. Here we demonstrate the feasibility of satellite radar interferometry to detect a migrating cavity under the city of Heerlen, the Netherlands, leading to foundation instability and the near-collapse of a part of a shopping mall in December 2011. We exploit the data archives of four imaging radar satellites, between 1992 and 2011, to investigate the dynamics of the area and detect shear strain within the structure of the building. Time series analysis shows localized differential vertical deformation rates of ~3mm/yr during 18years, followed by a dramatic increase of up to ~15mm/yr in the last few years. These results imply that the driving mechanism of the 2011 near-collapse event had a very long lead time and was likely due to a long-lasting gradual process, such as the upward migration of a cavity.}
+}
+
+@article{chang2017railway,
+author={Chang, Ling and Dollevoet, Rolf P. B. J. and Hanssen, Ramon F.},
+journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, 
+title={Nationwide Railway Monitoring Using Satellite SAR Interferometry}, 
+year={2017},
+volume={10},
+number={2},
+pages={596-604},
+keywords={Rail transportation;Radar tracking;Tracking;Synthetic aperture radar;Time series analysis;Satellites;Monitoring;Railway infrastructure;satellite;synthetic aperture radar (SAR);synthetic aperture radar interferometry (InSAR);testing theory},
+doi={10.1109/JSTARS.2016.2584783}}
+
+@article{ZHANG2022102847,
+title = {A model-backfeed deformation estimation method for revealing 20-year surface dynamics of the Groningen gas field using multi-platform SAR imagery},
+journal = {International Journal of Applied Earth Observation and Geoinformation},
+volume = {111},
+pages = {102847},
+year = {2022},
+issn = {1569-8432},
+doi = {10.1016/j.jag.2022.102847},
+url = {https://www.sciencedirect.com/science/article/pii/S1569843222000498},
+author = {Bin Zhang and Ling Chang and Alfred Stein},
+keywords = {Model-backfeed InSAR deformation estimation, Multiple Hypothesis Testing, Geolocation uncertainty, Monte Carlo methods, Multi-platform SAR data integration},
+abstract = {The Groningen gas field, the largest natural gas field in Europe, was discovered in 1959 and started its production in 1963. The Earth surface above it experienced subsidence over the past six decades because of gas extraction activities. To accurately reveal this surface movement with satellite SAR data, our study first proposes and demonstrates a model-backfeed (MBF) InSAR deformation estimation method to improve InSAR deformation time series modeling. This method allowed us to include a priori knowledge and to iteratively optimize functional and stochastic models. Using this method and employing a spatio-temporal SAR data integration method based upon Monte Carlo and Multiple Hypothesis Testing methods, we retrieved the 20-year subsidence history of the Groningen gas field by integrating 32 ERS-1/2, 68 Envisat and 82 Radarsat-2 SAR images. The results show that the maximum cumulative surface subsidence in this gas field has been as much as 15.5cm between 1995 and 2015. In terms of precision and accuracy, our MBF method offered a better result than the standard Multi-Temporal InSAR analysis method: the Ensemble Coherence increased by 10%–19% and Spatio-Temporal Consistency decreased by 2%–20%. In terms of accuracy, our results better concur with the external GNSS reference observations. We further show that the spatio-temporal SAR data integration method has better links with multi-platform SAR data if the uncertainties of the InSAR geolocation and temporal deformation are included. The study demonstrates that the MBF method optimized the estimation of deformation parameters and mitigated unwrapping errors.}
+}
+
+@article{fokker2016application,
+title = {Application of an ensemble smoother with multiple data assimilation to the Bergermeer gas field, using PS-InSAR},
+keywords = {Peer-lijst tijdschrift},
+author = {PA Fokker and BBT Wassing and {van Leijen}, FJ and RF Hanssen and DA Nieuwland},
+year = {2016},
+doi = {10.1016/j.gete.2015.11.003},
+language = {English},
+volume = {5},
+pages = {16--28},
+journal = {Geomechanics for Energy and the Environment},
+issn = {2352-3808},
+publisher = {Elsevier},
+number = {March},
+}
+
+@INPROCEEDINGS{Bruna2021,
+  author={Bruna, Marc F.D. and van Leijen, Freek J. and Hanssen, Ramon F.},
+  booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS}, 
+  title={A Generic Storage Method for Coherent Scatterers and Their Contextual Attributes}, 
+  year={2021},
+  volume={},
+  number={},
+  pages={1970-1973},
+  keywords={Scalability;Buildings;Time series analysis;Urban areas;Stability criteria;Geoscience and remote sensing;Spatial databases},
+  doi={10.1109/IGARSS47720.2021.9553453}}
+
+@INPROCEEDINGS{vanLeijen2021,
+  author={Van Leijen, Freek J. and Van der Marel, Hans and Hanssen, Ramon F.},
+  booktitle={2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS}, 
+  title={Towards the Integrated Processing of Geodetic Data}, 
+  year={2021},
+  volume={},
+  number={},
+  pages={3995-3998},
+  keywords={Global navigation satellite system;Systematics;Software packages;Volume measurement;Geoscience and remote sensing;Production;Quality assessment;Geodesy;data integration;InSAR;GNSS;subsidence},
+  doi={10.1109/IGARSS47720.2021.9554887}}
+
+@book{hanssen2001radar,
+  title={Radar interferometry: data interpretation and error analysis},
+  author={Hanssen, Ramon F},
+  volume={2},
+  year={2001},
+  publisher={Springer Science \& Business Media},
+  doi={10.1007/0-306-47633-9}
+}

paper/paper.md

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+---
+title: 'STMTools: Xarray extension for Interferometric SAR data in Space-Time Matrix format'
+tags:
+  - Python
+  - Interferometric
+  - Synthetic Aperture Radar
+  - InSAR
+  - Dask
+  - Xarray
+authors:
+  - name: Ou Ku
+    orcid: 0000-0002-5373-5209
+    affiliation: 1 
+  - name: Fakhereh Alidoost
+    orcid: 0000-0001-8407-6472
+    affiliation: 1
+  - name: Pranav Chandramouli
+    orcid: 0000-0002-7896-2969
+    affiliation: 1
+  - name: Thijs van Lankveld
+    orcid: 0009-0001-1147-4813
+    affiliation: 1
+  - name: Meiert W. Grootes
+    orcid: 0000-0002-5733-4795
+    affiliation: 1
+  - name: Francesco Nattino
+    orcid: 0000-0003-3286-0139
+    affiliation: 1
+  - name: Freek van Leijen
+    orcid: 0000-0002-2582-9267
+    affiliation: 2
+affiliations:
+ - name: Netherlands eScience Center, Netherlands
+   index: 1
+ - name: Delft University of Technology, Netherlands
+   index: 2
+date: 23 Dec 2024
+bibliography: paper.bib
+---
+
+## Summary
+
+Interferometry Synthetic Aperture Radar (InSAR) is a commonly used technology for monitoring ground surface deformation in various applications, such as civil-infrastructure stability [@chang2014detection; @chang2017railway], hydrocarbons extraction [@fokker2016application; @ZHANG2022102847]. InSAR observations typically come in the form of tabular datasets, with each row representing a measurement point and columns representing the properties of the measurement point. This format mixes the spatial and temporal dimensions, which makes it challenging to integrate InSAR data with other spatial and/or temporal datasets, such as cadastral data, weather data, etc. 
+
+Researchers have thus proposed the Space-Time Matrix (STM) formalism for InSAR datasets [@Bruna2021; @vanLeijen2021]. This framework consists in a representation of the InSAR data with the spatial and temporal dimensions separated. The STM formalism facilitates the analysis of InSAR data in combination with space- and/or time-dependent datasets from other sources (the "contextual information"), by providing a framework for integrating the contextual data. In the context of ground surface deformation, the framework facilitates the identification of the mechanisms driving deformation.
+
+## Statement of Need
+
+Modern time-series InSAR methods provide millions of observation points in a single dataset. However, interpretation of these datasets is challenging due to the complex and ambiguous nature of InSAR observations. [@hanssen2001radar] Under STM format, contextual information such as temperature, precipitation, and land-use can be integrated with InSAR data. This facilitates a better interpretation of InSAR data, resulting in a reliable and accurate understanding of the mechanisms of ground deformation. [@Bruna2021, @vanLeijen2021]
+
+To facilitate the analysis of InSAR datasets following the STM formalism in Python, we developed the `STMTools` package in Python-- as an extension of `Xarray`-- leveraging `Xarray`'s support for labeled multi-dimensional arrays for the Space-Time dimensions. `STMTools` provides a set of tools to efficiently connect the InSAR data with various contextual information, such as cadastral data and weather data. The Xarray `Dataset` data structure is used to group InSAR data and the contextual information under shared dimension coordinates (space and/or time). By building on Xarray, STMTools can also leverage `Dask` for parallel computing, enabling the processing of large-scale InSAR datasets.
+
+## Main Functionalities
+
+The main functionalities of `STMTools` are summarized as follows:
+
+- [I/O operations](https://tudelftgeodesy.github.io/stmtools/stm_init/)
+
+- [InSAR Operations](https://tudelftgeodesy.github.io/stmtools/operations/)
+
+- [Reorder STM by Morton Ordering](https://tudelftgeodesy.github.io/stmtools/order/)
+
+## Tutorial
+
+We provide the following tutorials, also available as Jupyter notebooks, to demonstrate the functionalities of `STMTools`:
+
+- [Basic operations](https://tudelftgeodesy.github.io/stmtools/notebooks/demo_operations_stm/)
+
+- [Reordering STM by Morton Ordering](https://tudelftgeodesy.github.io/stmtools/notebooks/demo_order_stm/)
+
+## Acknowledgements
+
+The authors express sincere gratitude to the Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO) for their generous funding of the `STMTools` development through the Collaboration in Innovative Technologies (CIT 2021) Call, grant NLESC.CIT.2021.006. Special thanks to SURF for providing valuable computational resources for `STMTools` testing via grant EINF-2051, EINF-4287 and EINF-6883.
+
+## References