Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients
The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field model...
Published in: | Remote Sensing |
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Main Authors: | , , , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2020
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Subjects: | |
Online Access: | https://doi.org/10.3390/rs12213483 |
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author | Betty Heller Frank Siegismund Roland Pail Thomas Gruber Roger Haagmans |
author_facet | Betty Heller Frank Siegismund Roland Pail Thomas Gruber Roger Haagmans |
author_sort | Betty Heller |
collection | MDPI Open Access Publishing |
container_issue | 21 |
container_start_page | 3483 |
container_title | Remote Sensing |
container_volume | 12 |
description | The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field models at large spatial scales, at which temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the reprocessed GOCE SGG data for their ability to resolve time-variable gravity signals. For the gravity field processing, we apply and compare a spherical harmonics (SH) approach and a mass concentration (mascon) approach. Although their global signal-to-noise ratio is <1, SH GOCE SGG-only models resolve the strong regional signals of glacier melting in Greenland and Antarctica, and the 2011 moment magnitude 9.0 earthquake in Japan, providing an estimation of gravity variations independent of Gravity Recovery and Climate Experiment (GRACE) data. The benefit of combined GRACE/GOCE SGG models is evaluated based on the ice mass trend signals in Greenland and Antarctica. While no signal contribution from GOCE SGG data additional to the GRACE models could be observed, we show that the incorporation of GOCE SGG data numerically stabilizes the related normal equation systems. |
format | Text |
genre | Antarc* Antarctica glacier Greenland |
genre_facet | Antarc* Antarctica glacier Greenland |
geographic | Greenland |
geographic_facet | Greenland |
id | ftmdpi:oai:mdpi.com:/2072-4292/12/21/3483/ |
institution | Open Polar |
language | English |
op_collection_id | ftmdpi |
op_coverage | agris |
op_doi | https://doi.org/10.3390/rs12213483 |
op_relation | Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs12213483 |
op_rights | https://creativecommons.org/licenses/by/4.0/ |
op_source | Remote Sensing; Volume 12; Issue 21; Pages: 3483 |
publishDate | 2020 |
publisher | Multidisciplinary Digital Publishing Institute |
record_format | openpolar |
spelling | ftmdpi:oai:mdpi.com:/2072-4292/12/21/3483/ 2025-01-16T19:04:25+00:00 Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients Betty Heller Frank Siegismund Roland Pail Thomas Gruber Roger Haagmans agris 2020-10-23 application/pdf https://doi.org/10.3390/rs12213483 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs12213483 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 21; Pages: 3483 GOCE time-variable gravity combination mascon spherical harmonics satellite gravitational gradiometry Text 2020 ftmdpi https://doi.org/10.3390/rs12213483 2023-08-01T00:20:16Z The reprocessing of the satellite gravitational gradiometry (SGG) data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission in 2018/2019 considerably reduced the low-frequency noise in the data, leading to reduced noise amplitudes in derived gravity field models at large spatial scales, at which temporal variations of the Earth’s gravity field have their highest amplitudes. This is the motivation to test the reprocessed GOCE SGG data for their ability to resolve time-variable gravity signals. For the gravity field processing, we apply and compare a spherical harmonics (SH) approach and a mass concentration (mascon) approach. Although their global signal-to-noise ratio is <1, SH GOCE SGG-only models resolve the strong regional signals of glacier melting in Greenland and Antarctica, and the 2011 moment magnitude 9.0 earthquake in Japan, providing an estimation of gravity variations independent of Gravity Recovery and Climate Experiment (GRACE) data. The benefit of combined GRACE/GOCE SGG models is evaluated based on the ice mass trend signals in Greenland and Antarctica. While no signal contribution from GOCE SGG data additional to the GRACE models could be observed, we show that the incorporation of GOCE SGG data numerically stabilizes the related normal equation systems. Text Antarc* Antarctica glacier Greenland MDPI Open Access Publishing Greenland Remote Sensing 12 21 3483 |
spellingShingle | GOCE time-variable gravity combination mascon spherical harmonics satellite gravitational gradiometry Betty Heller Frank Siegismund Roland Pail Thomas Gruber Roger Haagmans Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients |
title | Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients |
title_full | Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients |
title_fullStr | Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients |
title_full_unstemmed | Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients |
title_short | Temporal Gravity Signals in Reprocessed GOCE Gravitational Gradients |
title_sort | temporal gravity signals in reprocessed goce gravitational gradients |
topic | GOCE time-variable gravity combination mascon spherical harmonics satellite gravitational gradiometry |
topic_facet | GOCE time-variable gravity combination mascon spherical harmonics satellite gravitational gradiometry |
url | https://doi.org/10.3390/rs12213483 |