Apply suggestions from code review

Co-authored-by: FreekvanLeijen <f.j.vanleijen@tudelft.nl>

Author: rogerkuou

paper/paper.bib

@@ -36,6 +36,18 @@ @article{ZHANG2022102847
 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.}
 }
+@book{hanssen01,
+    key     = {insar defo atmos delftsar rfh_book},
+    author  = {R. F. Hanssen},
+    title   = {Radar Interferometry: Data Interpretation and Error Analysis},
+    year    = {2001},
+    publisher = {Kluwer Academic Publishers},
+    address = {Dordrecht},
+    mailto  = {r.f.hanssen@tudelft.nl},
+    available = {photocopy},
+    doi = {10.1007/0-306-47633-9},
+    ISBN = {978-0-7923-6945-5}
+} 
 
 @article{fokker2016application,
 title = {Application of an ensemble smoother with multiple data assimilation to the Bergermeer gas field, using PS-InSAR},
@@ -51,6 +63,14 @@ @article{fokker2016application
 publisher = {Elsevier},
 number = {March},
 }
+@article{ozer2018applicability,
+  title={Applicability of satellite radar imaging to monitor the conditions of levees},
+  author={{\"O}zer, I{\c{s}}{\i}l E and van Leijen, Freek J and Jonkman, Sebastiaan N and Hanssen, Ramon F},
+  journal={Journal of Flood Risk Management},
+  pages={e12509},
+  year={2018},
+  publisher={Wiley Online Library}
+}
 
 @article{moreira2013tutorial,
   title={A tutorial on synthetic aperture radar},

paper/paper.md

@@ -20,27 +20,27 @@ authors:
 affiliations:
  - name: Netherlands eScience Center, Netherlands
    index: 1
- - name: Delft University of Technology, Netherlands
+ - name: Department of Geoscience and Remote Sensing, Delft University of Technology, Netherlands
    index: 2
 date: 22 Dec 2024
 bibliography: paper.bib
 ---
 
 ## Summary
 
-Satellite-based Synthetic Aperture Radar (SAR) provides invaluable image data for Earth observation. The Interferometric SAR (InSAR) technique, which utilizes a stack of SAR images in Single Look Complex (SLC) format, plays a significant role in various surface motion monitoring applications, e.g. civil-infrastructure stability [@chang2014detection; @chang2017railway], and hydrocarbons extraction [@fokker2016application; @ZHANG2022102847]. To facilitate advanced data processing for InSAR communities, we developed `SARXarray`, a Xarray extension for handling SLC SAR stacks. 
+Satellite-based Synthetic Aperture Radar (SAR) provides invaluable data for Earth Observation. The Interferometric SAR (InSAR) technique [@hanssen01], which utilizes a stack of SAR images in Single Look Complex (SLC) format, plays a significant role in various surface motion monitoring applications, e.g. civil-infrastructure stability [@chang2014detection; @chang2017railway;ozer2018applicability], and hydrocarbons extraction [@fokker2016application; @ZHANG2022102847]. To facilitate advanced data processing for InSAR communities, we developed `SARXarray`, a Xarray extension for handling SLC SAR stacks. 
 
 ## Statement of Need
 
-Satellite-based SAR generates data stacks with long temporal coverage, broad spatial coverage, and high spatio-temporal resolution. [@moreira2013tutorial] Handling SAR data stacks in an efficient way is a common challenge within InSAR communities. To address this challenge, High-Performance Computing (HPC) is often used to process data in a parallel and distributed manner. However, to fully leverage HPC capabilities, data processing workflows need to be customized for each specific use-case.
+Satellite-based SAR generates data stacks with long temporal coverage, wide spatial coverage, and high spatio-temporal resolution [@moreira2013tutorial]. Handling SAR data stacks in an efficient way is a common challenge within the InSAR community. To address this challenge, High-Performance Computing (HPC) is often used to process data in a parallel and distributed manner. However, to fully leverage HPC capabilities, data processing workflows need to be customized for each specific use-case.
 
-To facilitate efficient processing of SLC SAR stacks and minimize code customization, we developed `SARXarray` for SLC SAR stack. 
+To facilitate efficient processing of SLC SAR stacks and minimize code customization, we developed `SARXarray`. 
 
 `SARXarray` leverages two well-established Python libraries `Xarray` and `Dask` from the [Pangeo community](https://www.pangeo.io/). It utilizes Xarray’s support on labeled multi-dimensional datasets to stress the space-time character of an SLC SAR stack. `Dask` is used to perform lazy evaluation of operations and block-wise computations. SARXarray can be integrated into existing Python workflows of InSAR processing and deployed on a variety of computational infrastructures. 
 
 ## Tutorial
 
-We provided a tutorial as a Jupyter notebook to demonstrate the functionalities of `SARXarray`:
+We provide a tutorial as a Jupyter notebook to demonstrate the functionalities of `SARXarray`:
 
 [Tutorial Jupyter notebook](https://tudelftgeodesy.github.io/sarxarray/notebooks/demo_sarxarray/)
 
@@ -51,13 +51,13 @@ The tutorial includes the following steps:
 - Attaching attributes to the loaded stack
 - Applying common SAR operations on the loaded stack such as:
   - Multi-Looking 
-  - Create Mean-Reflection-Map (MRM)
-  - Calculate complex coherence
+  - Creation of a Mean-Reflectivity-Map (MRM)
+  - Calculation of coherence
 
 ## Acknowledgements
 
-The authors express sincere gratitude to the Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO) for their generous funding of the `SARXarray` 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 `SARXarray` testing via grant EINF-2051, EINF-4287 and EINF-6883.
+The authors express sincere gratitude to the Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO) for their generous funding of the `SARXarray` 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 `SARXarray` testing via grants EINF-2051, EINF-4287 and EINF-6883.
 
-We would also like to thank Dr. Francesco Nattino and Dr. Meiert Willem Grootes for the insightful discussions, which are important contributions to this work.
+We would also like to thank Dr. Francesco Nattino and Dr. Meiert Willem Grootes of the Netherlands eScience Center for the insightful discussions, which are important contributions to this work.
 
 ## References