{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:20:08Z","timestamp":1773512408190,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"\u201cGamma2Cloud: Feasibility of using Sentinel-1 terrain-flattened gamma nought backscatter across EO platforms\u201d","doi-asserted-by":"publisher","award":["AO\/1-9101\/17\/I-NB"],"award-info":[{"award-number":["AO\/1-9101\/17\/I-NB"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000844","name":"\u201cGamma2Cloud: Feasibility of using Sentinel-1 terrain-flattened gamma nought backscatter across EO platforms\u201d","doi-asserted-by":"publisher","award":["878947"],"award-info":[{"award-number":["878947"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004955","name":"\u201cSatellite-based modelling of grassland yield and quality dynamics (SatGrass)\u201d","doi-asserted-by":"publisher","award":["AO\/1-9101\/17\/I-NB"],"award-info":[{"award-number":["AO\/1-9101\/17\/I-NB"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004955","name":"\u201cSatellite-based modelling of grassland yield and quality dynamics (SatGrass)\u201d","doi-asserted-by":"publisher","award":["878947"],"award-info":[{"award-number":["878947"]}],"id":[{"id":"10.13039\/501100004955","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite\u2019s high orbital stability. By propagating Sentinel-1\u2019s orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10\u201350. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.<\/jats:p>","DOI":"10.3390\/s23136072","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:53:16Z","timestamp":1688345596000},"page":"6072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Utilising Sentinel-1\u2019s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5701-9509","authenticated-orcid":false,"given":"Claudio","family":"Navacchi","sequence":"first","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2002-4467","authenticated-orcid":false,"given":"Senmao","family":"Cao","sequence":"additional","affiliation":[{"name":"Earth Observation Data Centre for Water Resources Monitoring (EODC), 1030 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7356-7516","authenticated-orcid":false,"given":"Bernhard","family":"Bauer-Marschallinger","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0374-7076","authenticated-orcid":false,"given":"Paul","family":"Snoeij","sequence":"additional","affiliation":[{"name":"V.O.F. APSS, 4725 SJ Wouwse Plantage, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1440-364X","authenticated-orcid":false,"given":"David","family":"Small","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Zurich, CH-8057 Zurich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7704-6857","authenticated-orcid":false,"given":"Wolfgang","family":"Wagner","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geoinformation, TU Wien, 1040 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117342","DOI":"10.1016\/j.eswa.2022.117342","article-title":"SAR data applications in earth observation: An overview","volume":"205","author":"Tsokas","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"520","article-title":"Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles","volume":"57","author":"Freeman","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bauer-Marschallinger, B., Cao, S., Tupas, M.E., Roth, F., Navacchi, C., Melzer, T., Freeman, V., and Wagner, W. (2022). Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube. Remote Sens., 14.","DOI":"10.3390\/rs14153673"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5831","DOI":"10.1109\/JSTARS.2021.3074068","article-title":"Prediction of categorized sea ice concentration from Sentinel-1 SAR images based on a fully convolutional network","volume":"14","author":"Colin","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Taravat, A., Wagner, M.P., and Oppelt, N. (2019). Automatic grassland cutting status detection in the context of spatiotemporal Sentinel-1 imagery analysis and artificial neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11060711"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dost\u00e1lov\u00e1, A., Lang, M., Ivanovs, J., Waser, L.T., and Wagner, W. (2021). European Wide Forest Classification Based on Sentinel-1 Data. Remote Sens., 13.","DOI":"10.3390\/rs13030337"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nagler, T., Rott, H., Ripper, E., Bippus, G., and Hetzenecker, M. (2016). Advancements for snowmelt monitoring by means of sentinel-1 SAR. Remote Sens., 8.","DOI":"10.3390\/rs8040348"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Salamon, P., Mctlormick, N., Reimer, C., Clarke, T., Bauer-Marschallinger, B., Wagner, W., Martinis, S., Chow, C., B\u00f6hnke, C., and Matgen, P. (2021, January 11\u201316). The new, systematic global flood monitoring product of the copernicus emergency management service. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554214"},{"key":"ref_10","unstructured":"VITO, and JRC (2023, May 05). Copernicus Global Land Service. Available online: https:\/\/land.copernicus.eu\/global\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101151","DOI":"10.1016\/j.ecoinf.2020.101151","article-title":"Towards an integrated forest fire danger assessment system for the European Alps","volume":"60","author":"Vacik","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zotta, R.M., Atzberger, C., Degenhart, J., Hollaus, M., Immitzer, M., Krajnz, H., Lick, H., M\u00fcller, M.M., Oblasser, H., and Schaffhauser, A. (2020, January 4\u20138). CONFIRM-Copernicus Data for Novel High-Resolution Wildfire Danger Services in Mountain Regions. Proceedings of the EGU General Assembly 2020, Online.","DOI":"10.5194\/egusphere-egu2020-19288"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bruggisser, M., Dorigo, W., Dost\u00e1lov\u00e1, A., Hollaus, M., Navacchi, C., Schlaffer, S., and Pfeifer, N. (2021). Potential of Sentinel-1 C-band time series to derive structural parameters of temperate deciduous forests. Remote Sens., 13.","DOI":"10.3390\/rs13040798"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening gamma: Radiometric terrain correction for SAR imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M.E., and Small, D. (2017). Using multitemporal sentinel-1 c-band backscatter to monitor phenology and classify deciduous and coniferous forests in northern switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/2150704X.2022.2092911","article-title":"The effects of radiometric terrain flattening on SAR-based forest mapping and classification","volume":"13","author":"Dostalova","year":"2022","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3055562","article-title":"Wide-area analysis-ready radar backscatter composites","volume":"60","author":"Small","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1038\/s41597-022-01189-6","article-title":"Global seasonal Sentinel-1 interferometric coherence and backscatter data set","volume":"9","author":"Kellndorfer","year":"2022","journal-title":"Sci. Data"},{"key":"ref_19","unstructured":"Committee on Earth Observation Satellites (2022, September 11). CEOS Analysis-Ready Data. Available online: https:\/\/ceos.org\/ard\/."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Truckenbrodt, J., Freemantle, T., Williams, C., Jones, T., Small, D., Dubois, C., Thiel, C., Rossi, C., Syriou, A., and Giuliani, G. (2019). Towards Sentinel-1 SAR analysis-ready data: A best practices assessment on preparing backscatter data for the cube. Data, 4.","DOI":"10.3390\/data4030093"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ticehurst, C., Zhou, Z.S., Lehmann, E., Yuan, F., Thankappan, M., Rosenqvist, A., Lewis, B., and Paget, M. (2019). Building a SAR-Enabled Data Cube Capability in Australia Using SAR analysis-ready Data. Data, 4.","DOI":"10.3390\/data4030100"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J. (2021). Sentinel-1 SAR Backscatter analysis-ready Data Preparation in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13101954"},{"key":"ref_23","unstructured":"GAMMA Remote Sensing (2021, October 18). The GAMMA Software. Available online: https:\/\/www.gamma-rs.ch\/software."},{"key":"ref_24","unstructured":"ISCE (2022, November 16). InSAR Scientific Computing Environment Version 3. Available online: https:\/\/github.com\/isce-framework\/isce3."},{"key":"ref_25","unstructured":"ESA (2022, September 11). SNAP. Available online: https:\/\/step.esa.int\/main\/toolboxes\/snap\/."},{"key":"ref_26","unstructured":"CEOS (2022, September 11). Analysis-Ready Data for Land: Normalised Radar Backscatter. Available online: https:\/\/ceos.org\/ard\/files\/PFS\/NRB\/v5.5\/CARD4L-PFS_NRB_v5.5.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3147472","article-title":"An area-based projection algorithm for SAR radiometric terrain correction and geocoding","volume":"60","author":"Shiroma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yuan, F., Repse, M., Leith, A., Rosenqvist, A., Milcinski, G., Moghaddam, N.F., Dhar, T., Burton, C., Hall, L., and Jorand, C. (2022). An Operational analysis-ready Radar Backscatter Dataset for the African Continent. Remote Sens., 14.","DOI":"10.3390\/rs14020351"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.isprsjprs.2022.07.023","article-title":"Utilising Sentinel-1\u2019s orbital stability for efficient pre-processing of sigma nought backscatter","volume":"192","author":"Navacchi","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Agram, P.S., Warren, M.S., Arko, S.A., and Calef, M.T. (2023). Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery. Remote Sens., 15.","DOI":"10.20944\/preprints202302.0233.v1"},{"key":"ref_31","unstructured":"Bourbigot, M., Johnsen, H., and Piantanida, R. (2020). Sentinel-1 Product Definition, ESA. Available online: https:\/\/sentinels.copernicus.eu\/documents\/247904\/0\/Sentinel-1-Product-Definition\/6049ee42-6dc7-4e76-9886-f7a72f5631f3."},{"key":"ref_32","unstructured":"Small, D., and Schubert, A. (2019). Guide to Sentinel-1 Geocoding\u2014UZH-S1-GC-AD, University of Zurich."},{"key":"ref_33","unstructured":"Schreier, G. (1993). SAR Geocoding: Data and Systems, Wichmann."},{"key":"ref_34","unstructured":"SNAP Team (2021, October 18). SNAP 8.0 Released. Available online: https:\/\/step.esa.int\/main\/snap-8-0-released\/."},{"key":"ref_35","unstructured":"Peters, M. (2021, October 18). Bulk Processing with GPT. Available online: https:\/\/senbox.atlassian.net\/wiki\/spaces\/SNAP\/pages\/70503475\/Bulk+Processing+with+GPT."},{"key":"ref_36","unstructured":"ESA (2023, January 30). POD Products and Requirements. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/technical-guides\/sentinel-1-sar\/pod\/products-requirements."},{"key":"ref_37","unstructured":"Fahrland, E. (2022). Copernicus Digital Elevation Model Product Handbook, Airbus. Available online: https:\/\/spacedata.copernicus.eu\/documents\/20123\/121239\/GEO1988-CopernicusDEM-SPE-002_ProductHandbook_I4.0.pdf."},{"key":"ref_38","unstructured":"Agisoft (2021, October 18). Global Models. Available online: https:\/\/www.agisoft.com\/downloads\/geoids\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_40","unstructured":"Woodhouse, I.H. (2005). Introduction to Microwave Remote Sensing, CRC Press."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.cageo.2014.07.005","article-title":"Optimisation of global grids for high-resolution remote sensing data","volume":"72","author":"Sabel","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Thomopoulos, N.T. (2013). Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models, Springer.","DOI":"10.1007\/978-1-4614-6022-0"},{"key":"ref_43","unstructured":"eoPortal (2023, January 30). RADARSAT Constellation. Available online: https:\/\/www.eoportal.org\/satellite-missions\/rcm."},{"key":"ref_44","unstructured":"ESA (2022, November 16). Ride into Orbit Secured for Sentinel-1C. Available online: https:\/\/www.esa.int\/Applications\/Observing_the_Earth\/Copernicus\/Sentinel-1\/Ride_into_orbit_secured_for_Sentinel-1C."},{"key":"ref_45","unstructured":"Velev, K. (2022, December 20). Mission Quick Facts, Available online: https:\/\/nisar.jpl.nasa.gov\/mission\/quick-facts\/."},{"key":"ref_46","unstructured":"Davidson, M., Chini, M., Dierking, W., Djavidnia, S., Haarpaintner, S., Hajduch, G., Laurin, V.G., Lavalle, M., Martinez, C.L., and Nagler, T. (2019). Copernicus L-Band SAR Mission Requirements Document, European Space Research and Technology Centre. Available online: https:\/\/esamultimedia.esa.int\/docs\/EarthObservation\/Copernicus_L-band_SAR_mission_ROSE-L_MRD_v2.0_issued.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41597-021-01059-7","article-title":"The normalised Sentinel-1 Global Backscatter Model, mapping Earth\u2019s land surface with C-band microwaves","volume":"8","author":"Cao","year":"2021","journal-title":"Sci. Data"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113651","DOI":"10.1016\/j.rse.2023.113651","article-title":"Soil moisture retrieval from Sentinel-1 using a first-order radiative transfer model\u2014A case-study over the Po-Valley","volume":"295","author":"Quast","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6170","DOI":"10.1109\/TGRS.2017.2721981","article-title":"Incidence angle dependence of first-year sea ice backscattering coefficient in Sentinel-1 SAR imagery over the Kara Sea","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6072\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:04:26Z","timestamp":1760126666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":49,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136072"],"URL":"https:\/\/doi.org\/10.3390\/s23136072","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]}}}