3rd National Workshop with international participation under the EU Copernicus programme
Published December 29, 2023 | Version v1
Conference paper Open

SENTINEL-HUB cloud based platform utilization, for COPERNICUS Sentinel-1 and Sentinel-2, as well as VHR data processing

Authors/Creators

  • 1. ROR icon Space Research and Technology Institute
  • 2. ROR icon Bulgarian Academy of Sciences

Description

This report is focused on the utilization of cloud based platform – SENTINEL-HUB©, operated by Sinergise© Slovenia, for processing of a large amount of satellite data from different sensors. The aim is to demonstrate the processing of ESA’s COPERNICUS Sentinel-1 time series SAR, and reference optical Sentinel-2 imagery, in the purpose of SRTI-BAS/ESA’s project - “FoReS”. Also, the report describes the processing of optical VHR satellite imagery from the WorldView and GeoEye missions, via the SENTINEL-HUB platform. The test site is located in mountainous forest in Stara Planina mountain massif. Firstly, an overview of the platform and its activation through dedicated API key was made. The user interface, making cURL requests, and formulating various functions in Javascript are covered. The processing of individual radar images from Sentinel-1 and optical images from Sentinel-2 is demonstrated. Averaging SAR time series were then performed for two summer periods, in the years 2020 and 2021, in the purpose of FoReS – WP4. Two SAR indices were also calculated - dual-pol Radar Vegetation Index (dRVI) and Degree of Polarization (DoP), available at the Sentinel-Hub’s Custom Repository. Also, for the FoReS - WP4 purposes, high-resolution imagery from the MAXAR’s collection of WorldView and GeoEye were provided. Four-band composites, pansharpen image, and the Normalized Vegetation Index (NDVI) were calculated. In conclusion, the author brings the focus on the convenience of working with the cloud based platform SENTINEL-HUB with dedicated APIs, highlighting its advantages in processing a large set of data. Access to the platform, as well as the VHR data, was carried out through a project proposal to the ESA, via Notation of Request (NoR).

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Additional details

Identifiers

ISBN
978-619-7490-16-9

Dates

Issued
2023-12-29

References

  • Avetisyan, D., Stankova, N. & Dimitrov, Z., 2023. Assessment of Spectral Vegetation Indices Performance for Post-Fire Monitoring of Different Forest Environments. MDPI, 6(8), p. 290.
  • Balzter, H., Rowland, C. & Saich, P., 2007. Forest canopy height and carbon estimation at Monks Wood National Nature Reserve, UK, using dual-wavelength SAR interferometry. Remote Sensing of Environment, Volume 108, p. 224–239.
  • Banqué, X. et al., 2015. POLARIMETRY-BASED LAND COVER CLASSIFICATION WITH SENTINEL-1 DATA, ESA.
  • Bhanumurthy, Y. & Mallikarjuna, R. Y., 2011. SAR Data Processing with Range Cell Migration. International Journal of Engineering Science and Technology, 3(1), pp. 388–398.
  • Cloude, S. R. & Pottier, E., 1996. A Review of Target Decomposition Theorems in Radar Polarimetry. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 34(2), p. 21.
  • Erxue, C., Zengyuan, L., Xin, T. & Fengyun, F., 2009. FOREST STRUCTURE INFROMATION EXTRACTION FROM POARIMETRIC ALOS PALSAR DATA. Bejing, Institute of Forest Resources Information Technique, Chinese Academy of Forestry.
  • Fernandez-Ordonez, Y., Soria-Ruiz, J. & Leblon, B., 2009. Forest Inventory using Optical and Radar Remote Sensin. Advances in Geoscience and Remote Sensing, pp. 540–556.
  • Ferro-Famil, L., 2006. Speckle Filtering. In: TUTORIAL ON SAR POLARIMETRY. Rennes: ESA, p. 12.
  • Imhoff, M. L., 1993. Radar Backscatter/Biomass Saturation: Observations and Implications for Global Biomass Assessment. Tokyo, s.n., p. 43–45.
  • López-Martínez, C., Ferro-Famil, L. & Pottier, E., 2005a. POLARIMETRIC DECOMPOSITIONS (PolSARPro), Rennes.
  • Mandal, D., 2021. Radar Vegetation Index for Sentinel-1 SAR data - RVI4S1 Script. [Online] Available at: https://custom-scripts.sentinel-hub.com/sentinel-1/radar_vegetation_index/
  • Mandal, D. et al., 2020. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sensing of Environment, Volume 247, p. 17.
  • Papathanassiou, K. P. & Cloude, S. R., 2001. Single-Baseline Polarimetric SAR Interferometry. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 39(11), pp. 2352–2364.
  • Postman Inc., 2022. Postman APIs. [Online] Available at: https://www.postman.com/
  • Quegan, S. et al., 2017. D6 – Global Biomass Map: Algorithm Theoretical Basis Document, Frascatti: ESA‐ESRIN.
  • Santoro, M. et al., 2015. Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, Volume 168, p. 316–334.
  • Santoro, M. et al., 2011. Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements. Remote Sensing of Environment, Volume 115, p. 490–507.
  • Santoro, M. et al., 2021. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth System Science Data, Volume 13, p. 3927–3950.
  • Santoro, M., Quegan, S. & Seifert, F., 2019. CCI BIOMASS, s.l.: ESA / Aberystwyth Uni / Gamma Remote Sensing.
  • Small, D. et al., 2010. Terrain-corrected Gamma: improved thematic land-cover retrieval for SAR with robust radiometric terrain correction. Bergen, ESA Living Planet Symposium.
  • Thiel, C. & Schmullius, C., 2013. Investigating ALOS PALSAR interferometric coherence in central Siberia at unfrozen and frozen conditions: implications for forest growing stock volume estimation. Remote Sensing, 39(3).
  • Yamaguchi, Y. et al., 2010. A New Four-component Scattering Power Decomposition Applied to ALOS-PALSAR PLR Data Sets. s.l., s.n.
  • Yommy, A. S., Liu, R. & Wu, A. S., 2015. SAR Image Despeckling Using Refined Lee Filter. 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Volume 2, pp. 260–265.