Skip to main content

Concepts for Open Access Interdisciplinary Remote Sensing with ESA Sentinel-1 SAR Data

  • Conference paper
  • First Online:
Advances and New Trends in Environmental Informatics 2023 (ENVIROINFO 2023)

Part of the book series: Progress in IS ((PROIS))

Included in the following conference series:

  • 137 Accesses

Abstract

Earth observation with advanced, large-scale technologies as satellite piloted Synthetic Aperture Radar (SAR) appear essential to monitor agricultural ecosystems in near future. Radar backscatter e.g. allows insights to crop conditions, soil properties and direct mapping of vegetation growth. Precise SAR pre-processing is a substantial prerequisite to perform machine learning on SAR data, e.g. for early prediction of optimal sowing, harvesting and fertilization time points. Not only for a successful, resource-efficient and environmentally friendly farming but for a wide range of other fields concerning environmental observations. Open access technologies offer the best solutions for collaborative efforts, thus minimizing financial and legal constraints in comparison to technologies residing in the commercial sector. Here, we combine expertise from the area of computer science, data science, software engineering, agriculture and geo-information-systems to build on state-of-the-art, open source (OS) tools and technologies in Germany. Our goal is to provide an easy to employ Sentinel-1 SAR pre-processing tool as well as a Germany wide, open access, pre-processed, analysis-ready database of Sentinel-1 SAR data. With the employment of modern software developing methods including the Model View Controller (MVC) architecture and a procedural and object-oriented design, these solutions can be extended, adapted and tested. This solution is available and accessible here (Jennifer, JenniferMcCl/Sentinel-1_SAR-Data-Processing: Sentinel-1_SAR-Data-Processing_V.1.0-beta, https://zenodo.org/record/8214935).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agency, R.: Exponential Growth in Technology. https://www.rehabagency.ai/insights/exponential-technology-growth (2022). Accessed 13 Jun 2023

  2. Altamira, T.: InSar At a Glance. https://site.tre-altamira.com/insar/. Accessed 13 Jun 2023

  3. Ascher, D.: Dynamic Languages – Ready For The Next Challenges, By Design. https://people.dsv.su.se/~beatrice/DYPL/ascher.pdf (2004). Accessed 13 Jun 2023

  4. Bayılmış, C., Ebleme, M.A., Çavuşoğlu, Ü., Küçük, K., Sevin, A.: A survey on communication protocols and performance evaluations for Internet of Things. Digital Commun. Netw. 8(6), 1094–1104 (2022). ISSN: 2352-8648. https://www.sciencedirect.com/science/article/pii/S2352864822000347

    Article  Google Scholar 

  5. Beyer, F., Brandt, P., Schmidt, M., Stahl, U., Golla, B., Gerighausen, H., Möller, M.: A paradigm shift towards decentralized cloud-integrated spatial data infrastructures: lessons learned and solutions provided for public authorities. PrePrint. https://doi.org/10.31223/X53H3N

  6. Camp, P.-H.: The software industry is still the problem. Accessed 13 Jun 2023 (2021). https://queue.acm.org/detail.cfm?id=3489045

  7. Chatenoux, B., Röösli, C., Wingate, V., Poussin, C., Rodila, D., Peduzzi, P., Steinmeier, C., Ginzler, C., Psomas, A., Schaepman, M., Giuliani, G.: The Swiss data cube, analysis ready data archive using earth observations of Switzerland. Sci. Data 8, 295 (2021)

    Article  Google Scholar 

  8. De Petris, S., Sarvia, F., Gullino, M., Tarantino, E., Borgogno-Mondino, E.: Sentinel-1 polarimetry to map apple orchard damage after a storm. Remote Sens. 13(5) (2021). ISSN: 2072-4292. https://www.mdpi.com/2072-4292/13/5/1030

  9. Denning, P.J., Lewis, T.G.: Exponential laws of computing growth. https://turing.plymouth.edu/~zshen/Webfiles/notes/CS322/mooreCACM012017.pdf (2017). Accessed 13 Jun 2023

  10. ERS: ERS At a Glance. https://www.esa.int/Applications/Observing_the_Earth/ERS_at_a_glance. Accessed 13 Jun 2023

  11. European Space Agency ESA: Sentinel Online: Applications. https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar/applications. Accessed 18 Aug 2023

  12. Gao, J., Gupta, K., Gupta, S., Shim, S.: On building testable software components. In: Dean, J., Gravel, A. (eds.) COTS-Based Software Systems, pp. 108–121. Springer, Berlin (2002). ISBN: 978-3-540-45588-2

    Chapter  Google Scholar 

  13. Gears of Testing, G.: Testable Architecture. https://gearsoftesting.org/testable-architecture.html. Accessed 13 Jun 2023

  14. Gibson, P.J., Power, C.H.: Introductory Remote Sensing. Digital Image Processing and Applications, 249 pp. Routledge, London (2000). https://doi.org/10.1017/S0016756801244951

  15. Goverment, U.: The Launch of Sputnik. https://2001-2009.state.gov/r/pa/ho/time/lw/103729.htm (2001). Accessed 13 Jun 2023

  16. Haltian: Wireless IoT communication protocols comparison. https://haltian.com/resource/iot-communication-protocols-comparison/ (2019). Accessed 13 Jun 2023

  17. Hegener, K.: Agriculture and climate change. https://www.giz.de/expertise/html/60132.html. Accessed 13 Jun 2023

  18. Hoja, D., Reinartz, P., Schroeder, M.: Comparison of DEM generation and combination methods using high resolution optical stereo imagery and interferometric SAR data (Jan 2006)

    Google Scholar 

  19. HOPE: Der erste Computer der Welt: Wer war der Erfinder des Computers?. https://www.computerhope.com/issues/ch000984.html (2022). Accessed 13 Jun 2023

  20. Huttunen, A.: How to Prevent Legacy Code From Emerging. https://www.arhohuttunen.com/prevent-legacy-code-from-emerging/ (2023). Accessed 13 Jun 2023

  21. Inflectra: What is Agile Scrum Methodology? https://www.inflectra.com/Methodologies/Scrum.aspx. Accessed 13 Jun 2023

  22. Janse van Rensburg, G., Kemp, J.: The use of C-band and X-band SAR with machine learning for detecting small-scale mining. Remote Sens. 14(4) (2022). ISSN: 2072-4292. https://www.mdpi.com/2072-4292/14/4/977

  23. Mandal, D., Vaka, D.S., Bhogapurapu, N., Vanama, V.S.K., Kumar, V., Rao, Y., Bhattacharya, A.: Sentinel-1 SLC preprocessing workflow for polarimetric applications: a generic practice for generating dual-pol covariance matrix elements in SNAP S-1 Toolbox (Nov 2019)

    Google Scholar 

  24. Mandal, D., Kumar, V., Ratha, D., Dey, S., Bhattacharya, A., Lopez-Sanchez, J.M., McNairn, H., Rao, Y.S.: Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sens. Environ. 247, 111954 (2020). ISSN: 0034-4257. https://www.sciencedirect.com/science/article/pii/S0034425720303242

    Article  Google Scholar 

  25. McCain, A.: How Fast Is Technology Advancing?. https://www.zippia.com/advice/how-fast-is-technology-advancing/ (2023). Accessed 13 Jun 2023

  26. McNeilly, A.: Creating a better developer experience by avoiding legacy code. https://dev.to/adammc331/creating-a-better-developer-experience-by-avoiding-legacy-code-22dc (2020). Accessed 13 Jun 2023

  27. Mijinyawa, K.: Acceptance of Open Source Software https://10.13140/RG.2.1.1905.8400, (Aug 2015)

    Google Scholar 

  28. Mishra, D., Pathak, G., Singh, B.P., Mohit, Sihag, P., Rajeev, Singh, K., Singh, S.: Crop classification by using dual-pol SAR vegetation indices derived from Sentinel-1 SAR-C data. Environ. Monit. Assess. 195(1), 115 (2022). ISSN: 1573-2959. https://doi.org/10.1007/s10661-022-10591-x

  29. Opensource.org: History of the OSI. https://opensource.org/history/ (2018). Accessed 13 Jun 2023

  30. Partida, D.: Top Open Source Companies 2023. https://www.datamation.com/open-source/35-top-open-source-companies/ (2023). Accessed 13 Jun 2023

  31. Plank, S.: Rapid damage assessment by means of multi-temporal SAR – a comprehensive review and outlook to Sentinel-1. Remote Sens. 6(6), 4870–4906 (2014). ISSN: 2072-4292. https://www.mdpi.com/2072-4292/6/6/4870

    Article  Google Scholar 

  32. Prechelt, L., Lutz: Are scripting languages any good? A validation of Perl, Python, Rexx, and Tcl against C, C++, and Java. Adv. Comput. 57, 205 (2003)

    Article  Google Scholar 

  33. Salma, S., Keerthana, N., Dodamani, B.: Target decomposition using dual-polarization sentinel-1 SAR data: study on crop growth analysis. Remote Sens. Appl. Soc. Environ. 28, 100854 (2022). ISSN: 2352-9385. https://www.sciencedirect.com/science/article/pii/S2352938522001628

    Google Scholar 

  34. Schweighofer, A.: What is legacy code and how to avoid it? https://andreschweighofer.com/tech/what-is-legacy-code-and-how-to-avoid-it/. Accessed 13 Jun 2023

  35. Ticehurst, C., Zhou, Z.-S., Lehmann, E., Yuan, F., Thankappan, M., Rosen-qvist, A., Lewis, B., Paget, M.: Building a SAR-enabled data cube capability in Australia using SAR analysis ready data. Data 4(3) (2019). ISSN: 2306-5729. https://www.mdpi.com/2306-5729/4/3/100

  36. Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C.: Towards Sentinel-1 SAR analysis-ready data: a best practices assessment on preparing backscatter data for the cube. Data 4(3), S. 93 (2019). https://doi.org/10.3390/data4030093

    Article  Google Scholar 

  37. UNICEF: Water and the global climate crisis. https://www.unicef.org/stories/water-and-climate-change-10-things-you-should-know (2023). Accessed 13 Jun 2023

  38. Verlag, B.: When was the first computer invented? https://bmu-verlag.de/der-erste-computer-der-welt/. Accessed 13 Jun 2023

  39. Villarroya-Carpio, A., Lopez-Sanchez, J.M.: Multi-annual evaluation of time series of Sentinel-1 interferometric coherence as a tool for crop monitoring. Sensors 23(4) (2023). ISSN: 1424-8220. https://www.mdpi.com/1424-8220/23/4/1833

  40. Villarroya-Carpio, A., Lopez-Sanchez, J.M., Engdahl, M.E.: Sentinel-1 interferometric coherence as a vegetation index for agriculture. Remote Sens. Environ. 280, 113208 (2022). ISSN: 0034-4257. https://www.sciencedirect.com/science/article/pii/S0034425722003169

    Article  Google Scholar 

  41. Weinberg, G.M.: The Psychology of Computer Programming, Annual. Dorset House, New York (1998)

    Google Scholar 

  42. Weinberg, G.M.: Gerald M. Weinberg About Software. http://geraldmweinberg.com/Site/Software.html. Accessed 13 Jun 2023

  43. Xun, Z., Zhao, C., Kang, Y., Liu, X., Liu, Y., Du, C.: Automatic extraction of potential landslides by integrating an optical remote sensing image with an InSAR-derived deformation map. Remote Sens. 14(11) (2022). ISSN: 2072-4292. https://www.mdpi.com/2072-4292/14/11/2669

  44. Yadav, V.P., Prasad, R., Bala, R., Srivastava, P.K., Vanama, V.S.K.: Appraisal of dual polarimetric radar vegetation index in first order microwave scattering algorithm using sentinel – 1A (C - band) and ALOS - 2 (L - band) SAR data. Geocarto Int. 37(21), 6232–6250 (2022). eprint: https://doi.org/10.1080/10106049.2021.1933209

  45. Yohannes, H.: A review on relationship between climate change and agriculture. J. Earth Sci. Clim. Change 7, 1–8 (2015)

    Google Scholar 

  46. Zaveria: Top 10 Programming Languages in 2023. https://www.analyticsinsight.net/top-10-programming-languages-in-2023-with-the-largest-developer-communities/ (2023). Accessed 13 Jun 2023

  47. ESA. https://scihub.copernicus.eu/ (2023). Accessed 4 Aug 2023

  48. SNAP. https://earth.esa.int/eogateway/tools/snap (2023). Accessed 4 Aug 2023

  49. CODE-DE. https://code-de.org/de/ (2023). Accessed 4 Aug 2023

  50. Karlmarx, T.: Derivation of crop parameters using Sentinel-1 SAR data: a case study for winter wheat in northern Germany (2023). https://doi.org/10.5073/20230612-103122-0. Published: 27 Jun 2023

  51. Jennifer, Mc: JenniferMcCl/Sentinel-1_SAR-Data-Processing: Sentinel-1_SAR-Data-Processing_V.1.0-beta (2023). Published: 04 Aug 2023. https://zenodo.org/record/8214935

  52. Wilgenbusch, et al.: Big data promises and obstacles: agricultural data ownership and privacy, https://acsess.onlinelibrary.wiley.com/doi/epdf/10.1002/agj2.21182 (2023). Accessed 4 Aug 2023

  53. Bill Holtsnider, et al.: Agile Development and Business goals. https://www.sciencedirect.com/book/9780123815200/agile-development-and-business-goals#book-description (2023). Accessed 04 Aug 2023

  54. Copernicus, Sentinel Online. https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1 (2023). Accessed 04 Aug 2023

  55. Copernicus, SciHub. https://scihub.copernicus.eu/userguide/ (2023). Accessed 04 Aug 2023

  56. ESA, SNAP Command Line Tutorial. http://step.esa.int/docs/tutorials/SNAP_CommandLine_Tutorial.pdf (2023). Accessed 04 Aug 2023

  57. CEOS, ARD, https://ceos.org/ard/ (2023). Accessed 04 Aug 2023

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jennifer McClelland .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

McClelland, J., Riedel, T., Beyer, F., Gerighausen, H., Golla, B. (2024). Concepts for Open Access Interdisciplinary Remote Sensing with ESA Sentinel-1 SAR Data. In: Wohlgemuth, V., Kranzlmüller, D., Höb, M. (eds) Advances and New Trends in Environmental Informatics 2023. ENVIROINFO 2023. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-031-46902-2_4

Download citation

Publish with us

Policies and ethics