Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging
Soil moisture (SM) is an important physical quantity that can reflect the land surface condition. There are many ways to measure SM, satellite microwave remote sensing is now considered the primary method because it can provide real-time high-resolution data. However, SM data obtained by satellite r...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2018/9310838 |
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| _version_ | 1832555886228275200 |
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| author | Shan Wang Yuexing Wang Chi Zhang Han Shuai Chun-Xiang Shi |
| author_facet | Shan Wang Yuexing Wang Chi Zhang Han Shuai Chun-Xiang Shi |
| author_sort | Shan Wang |
| collection | DOAJ |
| description | Soil moisture (SM) is an important physical quantity that can reflect the land surface condition. There are many ways to measure SM, satellite microwave remote sensing is now considered the primary method because it can provide real-time high-resolution data. However, SM data obtained by satellite remote sensing exhibit certain deviation compared with reference data obtained from ground stations. To improve the accuracy of SM forecasts, this study proposed the use of a Bayesian model averaging (BMA) method to integrate multisatellite SM data. First, China was divided into eight regions. Then, SM data observed by satellites (FY3B, SMOS, and WINDSAT) were fused using the BMA method and a traditional averaging method. Finally, SM data were predicted using data from ground observation stations as a reference standard. Following the fusion process, three parameters (standard deviation, correlation coefficient, and root mean square deviation) were used to evaluate the fusion results, which revealed the superiority of the BMA method over the traditional averaging method. |
| format | Article |
| id | doaj-art-e023cd40563c442fb6d500a3997104d3 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-e023cd40563c442fb6d500a3997104d32025-02-03T05:47:00ZengWileyAdvances in Meteorology1687-93091687-93172018-01-01201810.1155/2018/93108389310838Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model AveragingShan Wang0Yuexing Wang1Chi Zhang2Han Shuai3Chun-Xiang Shi4School of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaNational Meteorological Information Center, Beijing 100081, ChinaNational Meteorological Information Center, Beijing 100081, ChinaSoil moisture (SM) is an important physical quantity that can reflect the land surface condition. There are many ways to measure SM, satellite microwave remote sensing is now considered the primary method because it can provide real-time high-resolution data. However, SM data obtained by satellite remote sensing exhibit certain deviation compared with reference data obtained from ground stations. To improve the accuracy of SM forecasts, this study proposed the use of a Bayesian model averaging (BMA) method to integrate multisatellite SM data. First, China was divided into eight regions. Then, SM data observed by satellites (FY3B, SMOS, and WINDSAT) were fused using the BMA method and a traditional averaging method. Finally, SM data were predicted using data from ground observation stations as a reference standard. Following the fusion process, three parameters (standard deviation, correlation coefficient, and root mean square deviation) were used to evaluate the fusion results, which revealed the superiority of the BMA method over the traditional averaging method.http://dx.doi.org/10.1155/2018/9310838 |
| spellingShingle | Shan Wang Yuexing Wang Chi Zhang Han Shuai Chun-Xiang Shi Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging Advances in Meteorology |
| title | Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging |
| title_full | Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging |
| title_fullStr | Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging |
| title_full_unstemmed | Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging |
| title_short | Research on Fusing Multisatellite Soil Moisture Data Based on Bayesian Model Averaging |
| title_sort | research on fusing multisatellite soil moisture data based on bayesian model averaging |
| url | http://dx.doi.org/10.1155/2018/9310838 |
| work_keys_str_mv | AT shanwang researchonfusingmultisatellitesoilmoisturedatabasedonbayesianmodelaveraging AT yuexingwang researchonfusingmultisatellitesoilmoisturedatabasedonbayesianmodelaveraging AT chizhang researchonfusingmultisatellitesoilmoisturedatabasedonbayesianmodelaveraging AT hanshuai researchonfusingmultisatellitesoilmoisturedatabasedonbayesianmodelaveraging AT chunxiangshi researchonfusingmultisatellitesoilmoisturedatabasedonbayesianmodelaveraging |