Noise Analysis and Combination of Hydrology Loading-Induced Displacements
Large uncertainties exist in the available hydrology loading prediction models, and currently no consensus is reached on which loading model is superior or appears to represent nature in a more satisfactory way. This study discusses the noise characterization and combination of the vertical loadings...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/12/2840 |
| _version_ | 1797482705371267072 |
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| author | Chang Xu Xin Yao Xiaoxing He |
| author_facet | Chang Xu Xin Yao Xiaoxing He |
| author_sort | Chang Xu |
| collection | DOAJ |
| description | Large uncertainties exist in the available hydrology loading prediction models, and currently no consensus is reached on which loading model is superior or appears to represent nature in a more satisfactory way. This study discusses the noise characterization and combination of the vertical loadings predicted by different hydrology reanalysis (e.g., MERRA, GLDAS/Noah, GEOS-FPIT, and ERA interim). We focused on the hydrology loading predictions in the time span from 2011 to 2014 for the 70 Global Positioning System (GPS) sites, which are located close to the great rivers, lakes, and reservoirs. The maximum likelihood estimate with Akaike information criteria (AIC) showed that the auto-regressive (AR) model with an order from 2 to 5 is a good description of the temporal correlation that exists in the hydrology loading predictions. Moreover, significant discrepancy exists in the root mean square (RMS) of different hydrology loading predictions, and none of them have the lowest noise level for the all-time domain. Principal component analysis (PCA) was therefore used to create a combined loading-induced time series. Statistical indices (e.g., mean overlapping Hadamard variance, Nash-Sutcliffe efficiency, and variance reduction) showed that our proposed algorithm had an overall good performance and seemed to be potentially feasible for performing corrections on geodetic GPS heights. |
| first_indexed | 2024-03-09T22:37:15Z |
| format | Article |
| id | doaj.art-f14fa44a11a646639be1dfafc6da8125 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:37:15Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f14fa44a11a646639be1dfafc6da81252023-11-23T18:47:43ZengMDPI AGRemote Sensing2072-42922022-06-011412284010.3390/rs14122840Noise Analysis and Combination of Hydrology Loading-Induced DisplacementsChang Xu0Xin Yao1Xiaoxing He2School of Road and Bridge, Zhejiang Institute of Communications, Hangzhou 311112, ChinaSchool of Road and Bridge, Zhejiang Institute of Communications, Hangzhou 311112, ChinaSchool of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaLarge uncertainties exist in the available hydrology loading prediction models, and currently no consensus is reached on which loading model is superior or appears to represent nature in a more satisfactory way. This study discusses the noise characterization and combination of the vertical loadings predicted by different hydrology reanalysis (e.g., MERRA, GLDAS/Noah, GEOS-FPIT, and ERA interim). We focused on the hydrology loading predictions in the time span from 2011 to 2014 for the 70 Global Positioning System (GPS) sites, which are located close to the great rivers, lakes, and reservoirs. The maximum likelihood estimate with Akaike information criteria (AIC) showed that the auto-regressive (AR) model with an order from 2 to 5 is a good description of the temporal correlation that exists in the hydrology loading predictions. Moreover, significant discrepancy exists in the root mean square (RMS) of different hydrology loading predictions, and none of them have the lowest noise level for the all-time domain. Principal component analysis (PCA) was therefore used to create a combined loading-induced time series. Statistical indices (e.g., mean overlapping Hadamard variance, Nash-Sutcliffe efficiency, and variance reduction) showed that our proposed algorithm had an overall good performance and seemed to be potentially feasible for performing corrections on geodetic GPS heights.https://www.mdpi.com/2072-4292/14/12/2840GPSnoisehydrology loadingPCAHadamard variance |
| spellingShingle | Chang Xu Xin Yao Xiaoxing He Noise Analysis and Combination of Hydrology Loading-Induced Displacements Remote Sensing GPS noise hydrology loading PCA Hadamard variance |
| title | Noise Analysis and Combination of Hydrology Loading-Induced Displacements |
| title_full | Noise Analysis and Combination of Hydrology Loading-Induced Displacements |
| title_fullStr | Noise Analysis and Combination of Hydrology Loading-Induced Displacements |
| title_full_unstemmed | Noise Analysis and Combination of Hydrology Loading-Induced Displacements |
| title_short | Noise Analysis and Combination of Hydrology Loading-Induced Displacements |
| title_sort | noise analysis and combination of hydrology loading induced displacements |
| topic | GPS noise hydrology loading PCA Hadamard variance |
| url | https://www.mdpi.com/2072-4292/14/12/2840 |
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