Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2
During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relativel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/12/1935 |
| _version_ | 1797565258781425664 |
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| author | Anna Derkacheva Jeremie Mouginot Romain Millan Nathan Maier Fabien Gillet-Chaulet |
| author_facet | Anna Derkacheva Jeremie Mouginot Romain Millan Nathan Maier Fabien Gillet-Chaulet |
| author_sort | Anna Derkacheva |
| collection | DOAJ |
| description | During the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relatively large intrinsic errors associated with the measurements. Here, we process and combine together measurements of ice velocity of Russell Gletscher in Greenland from three satellites—Sentinel-1, Sentinel-2, and Landsat-8, creating a multi-year velocity database with high temporal and spatial resolution. We then investigate post-processing methodologies with the aim of generating corrected, ordered, and simplified time series. We tested rolling mean and median, cubic spline regression, and linear non-parametric local regression (LOWESS) smoothing algorithms to reduce data noise, evaluated the results against ground-based GPS in one location, and compared the results between two locations with different characteristics. We found that LOWESS provides the best solution for noisy measurements that are unevenly distributed in time. Using this methodology with these sensors, we can robustly derive time series with temporal resolution of 2–3 weeks and improve the accuracy on the ice velocity to about 10 m/yr, or a factor of three compared to the initial measurements. The presented methodology could be applied to the entire Greenland ice sheet with an aim of reconstructing comprehensive sub-seasonal ice flow dynamics and mass balance. |
| first_indexed | 2024-03-10T19:09:35Z |
| format | Article |
| id | doaj.art-d55fb944be5b4c7cb75ca201842fa5da |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:09:35Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d55fb944be5b4c7cb75ca201842fa5da2023-11-20T03:54:49ZengMDPI AGRemote Sensing2072-42922020-06-011212193510.3390/rs12121935Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2Anna Derkacheva0Jeremie Mouginot1Romain Millan2Nathan Maier3Fabien Gillet-Chaulet4Institut de Géosciences de l’Environnement—Université Grenoble Alpes, CNRS, IRD, INP, 38400 Grenoble, Isère, FranceInstitut de Géosciences de l’Environnement—Université Grenoble Alpes, CNRS, IRD, INP, 38400 Grenoble, Isère, FranceInstitut de Géosciences de l’Environnement—Université Grenoble Alpes, CNRS, IRD, INP, 38400 Grenoble, Isère, FranceInstitut de Géosciences de l’Environnement—Université Grenoble Alpes, CNRS, IRD, INP, 38400 Grenoble, Isère, FranceInstitut de Géosciences de l’Environnement—Université Grenoble Alpes, CNRS, IRD, INP, 38400 Grenoble, Isère, FranceDuring the last decade, the number of available satellite observations has increased significantly, allowing for far more frequent measurements of the glacier speed. Appropriate methods of post-processing need to be developed to efficiently deal with the large volumes of data generated and relatively large intrinsic errors associated with the measurements. Here, we process and combine together measurements of ice velocity of Russell Gletscher in Greenland from three satellites—Sentinel-1, Sentinel-2, and Landsat-8, creating a multi-year velocity database with high temporal and spatial resolution. We then investigate post-processing methodologies with the aim of generating corrected, ordered, and simplified time series. We tested rolling mean and median, cubic spline regression, and linear non-parametric local regression (LOWESS) smoothing algorithms to reduce data noise, evaluated the results against ground-based GPS in one location, and compared the results between two locations with different characteristics. We found that LOWESS provides the best solution for noisy measurements that are unevenly distributed in time. Using this methodology with these sensors, we can robustly derive time series with temporal resolution of 2–3 weeks and improve the accuracy on the ice velocity to about 10 m/yr, or a factor of three compared to the initial measurements. The presented methodology could be applied to the entire Greenland ice sheet with an aim of reconstructing comprehensive sub-seasonal ice flow dynamics and mass balance.https://www.mdpi.com/2072-4292/12/12/1935ice velocitytime seriespost-processingdata reductionnon-parametric regressionmulti-sensor data |
| spellingShingle | Anna Derkacheva Jeremie Mouginot Romain Millan Nathan Maier Fabien Gillet-Chaulet Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 Remote Sensing ice velocity time series post-processing data reduction non-parametric regression multi-sensor data |
| title | Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 |
| title_full | Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 |
| title_fullStr | Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 |
| title_full_unstemmed | Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 |
| title_short | Data Reduction Using Statistical and Regression Approaches for Ice Velocity Derived by Landsat-8, Sentinel-1 and Sentinel-2 |
| title_sort | data reduction using statistical and regression approaches for ice velocity derived by landsat 8 sentinel 1 and sentinel 2 |
| topic | ice velocity time series post-processing data reduction non-parametric regression multi-sensor data |
| url | https://www.mdpi.com/2072-4292/12/12/1935 |
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