Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification
GNSS Reflectometry (GNSS-R), a method of remote sensing using the reflections from satellite navigation systems, was initially envisaged for ocean wind speed sensing. In recent times there has been significant interest in the use of GNSS-R for sensing land parameters such as soil moisture, which has...
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MDPI AG
2021-03-01
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Online Access: | https://www.mdpi.com/2072-4292/13/5/1031 |
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author | Lucinda King Martin Unwin Jonathan Rawlinson Raffaella Guida Craig Underwood |
author_facet | Lucinda King Martin Unwin Jonathan Rawlinson Raffaella Guida Craig Underwood |
author_sort | Lucinda King |
collection | DOAJ |
description | GNSS Reflectometry (GNSS-R), a method of remote sensing using the reflections from satellite navigation systems, was initially envisaged for ocean wind speed sensing. In recent times there has been significant interest in the use of GNSS-R for sensing land parameters such as soil moisture, which has been identified as an Essential Climate Variable (ECV). Monitoring objectives for ECVs set by the Global Climate Observing System (GCOS) organisation include a reduction in data gaps from spaceborne sources. GNSS-R can be implemented on small, relatively cheap platforms and can enable the launch of constellations, thus reducing such data gaps in these important datasets. However in order to realise operational land sensing with GNSS-R, adaptations are required to existing instrumentation. Spaceborne GNSS-R requires the reflection points to be predicted in advance, and for land sensing this means the effect of topography must be considered. This paper presents an algorithm for on-board prediction of reflection points over the land, allowing generation of DDMs on-board as well as compression and calibration. The algorithm is tested using real satellite data from TechDemoSat-1 in a software receiver with on-board constraints being considered. Three different resolutions of Digital Elevation Model are compared. The algorithm is shown to perform better against the operational requirements of sensing land parameters than existing methods and is ready to proceed to flight testing. |
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id | doaj.art-6e61061af22f433c8f0ab4cb2c9b3a9b |
institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-10T13:25:08Z |
publishDate | 2021-03-01 |
publisher | MDPI AG |
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series | Remote Sensing |
spelling | doaj.art-6e61061af22f433c8f0ab4cb2c9b3a9b2023-11-21T09:42:27ZengMDPI AGRemote Sensing2072-42922021-03-01135103110.3390/rs13051031Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and VerificationLucinda King0Martin Unwin1Jonathan Rawlinson2Raffaella Guida3Craig Underwood4Surrey Space Centre, University of Surrey, Guildford GU2 7HX, UKSurrey Satellite Technology Ltd., Guildford GU2 7YE, UKSurrey Satellite Technology Ltd., Guildford GU2 7YE, UKSurrey Space Centre, University of Surrey, Guildford GU2 7HX, UKSurrey Space Centre, University of Surrey, Guildford GU2 7HX, UKGNSS Reflectometry (GNSS-R), a method of remote sensing using the reflections from satellite navigation systems, was initially envisaged for ocean wind speed sensing. In recent times there has been significant interest in the use of GNSS-R for sensing land parameters such as soil moisture, which has been identified as an Essential Climate Variable (ECV). Monitoring objectives for ECVs set by the Global Climate Observing System (GCOS) organisation include a reduction in data gaps from spaceborne sources. GNSS-R can be implemented on small, relatively cheap platforms and can enable the launch of constellations, thus reducing such data gaps in these important datasets. However in order to realise operational land sensing with GNSS-R, adaptations are required to existing instrumentation. Spaceborne GNSS-R requires the reflection points to be predicted in advance, and for land sensing this means the effect of topography must be considered. This paper presents an algorithm for on-board prediction of reflection points over the land, allowing generation of DDMs on-board as well as compression and calibration. The algorithm is tested using real satellite data from TechDemoSat-1 in a software receiver with on-board constraints being considered. Three different resolutions of Digital Elevation Model are compared. The algorithm is shown to perform better against the operational requirements of sensing land parameters than existing methods and is ready to proceed to flight testing.https://www.mdpi.com/2072-4292/13/5/1031GNSS-Rtopographydata compressionon-board data processing |
spellingShingle | Lucinda King Martin Unwin Jonathan Rawlinson Raffaella Guida Craig Underwood Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification Remote Sensing GNSS-R topography data compression on-board data processing |
title | Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification |
title_full | Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification |
title_fullStr | Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification |
title_full_unstemmed | Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification |
title_short | Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification |
title_sort | towards a topographically accurate reflection point prediction algorithm for operational spaceborne gnss reflectometry development and verification |
topic | GNSS-R topography data compression on-board data processing |
url | https://www.mdpi.com/2072-4292/13/5/1031 |
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