An Improved Tropospheric Tomographic Model Based on Artificial Neural Network
Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals,...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10130398/ |
| _version_ | 1797813918449532928 |
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| author | Minghao Zhang Kefei Zhang Suqin Wu Longjiang Li Dantong Zhu Moufeng Wan Peng Sun Jiaqi Shi Shangyi Liu Andong Hu |
| author_facet | Minghao Zhang Kefei Zhang Suqin Wu Longjiang Li Dantong Zhu Moufeng Wan Peng Sun Jiaqi Shi Shangyi Liu Andong Hu |
| author_sort | Minghao Zhang |
| collection | DOAJ |
| description | Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography. |
| first_indexed | 2024-03-13T07:59:47Z |
| format | Article |
| id | doaj.art-15e5b7ec39274c099c49bf67f0e5d324 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-13T07:59:47Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-15e5b7ec39274c099c49bf67f0e5d3242023-06-01T23:00:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01164801481910.1109/JSTARS.2023.327830210130398An Improved Tropospheric Tomographic Model Based on Artificial Neural NetworkMinghao Zhang0https://orcid.org/0000-0002-5786-7436Kefei Zhang1https://orcid.org/0000-0001-9376-1148Suqin Wu2https://orcid.org/0000-0002-0994-402XLongjiang Li3Dantong Zhu4https://orcid.org/0000-0002-5870-6327Moufeng Wan5https://orcid.org/0000-0001-5380-0587Peng Sun6https://orcid.org/0000-0003-0607-6877Jiaqi Shi7Shangyi Liu8https://orcid.org/0000-0001-8472-5145Andong Hu9School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaGNSS Research Center, Wuhan University, Wuhan, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaCooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, USAGlobal navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography.https://ieeexplore.ieee.org/document/10130398/BP-ANNglobal navigation satellite systems (GNSS)tropospheric tomography |
| spellingShingle | Minghao Zhang Kefei Zhang Suqin Wu Longjiang Li Dantong Zhu Moufeng Wan Peng Sun Jiaqi Shi Shangyi Liu Andong Hu An Improved Tropospheric Tomographic Model Based on Artificial Neural Network IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing BP-ANN global navigation satellite systems (GNSS) tropospheric tomography |
| title | An Improved Tropospheric Tomographic Model Based on Artificial Neural Network |
| title_full | An Improved Tropospheric Tomographic Model Based on Artificial Neural Network |
| title_fullStr | An Improved Tropospheric Tomographic Model Based on Artificial Neural Network |
| title_full_unstemmed | An Improved Tropospheric Tomographic Model Based on Artificial Neural Network |
| title_short | An Improved Tropospheric Tomographic Model Based on Artificial Neural Network |
| title_sort | improved tropospheric tomographic model based on artificial neural network |
| topic | BP-ANN global navigation satellite systems (GNSS) tropospheric tomography |
| url | https://ieeexplore.ieee.org/document/10130398/ |
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