An Integrated Framework for Mapping Nationwide Daily Temperature in China
Air temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/9895576 |
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| author | Shaobo Zhong Xinlan Ye Mingxing Wang Xin Mei Dunjiang Song Wenhui Wang |
| author_facet | Shaobo Zhong Xinlan Ye Mingxing Wang Xin Mei Dunjiang Song Wenhui Wang |
| author_sort | Shaobo Zhong |
| collection | DOAJ |
| description | Air temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (Ts) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate Ta in view of the relationship between Ta and Ts. In this article, we proposed a two-phase integrated framework to estimate daily mean Ta nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy. |
| format | Article |
| id | doaj-art-6729355d06fd440789399fd649d49550 |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-6729355d06fd440789399fd649d495502025-02-03T05:53:50ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/9895576An Integrated Framework for Mapping Nationwide Daily Temperature in ChinaShaobo Zhong0Xinlan Ye1Mingxing Wang2Xin Mei3Dunjiang Song4Wenhui Wang5Urban Construction SchoolFaculty of Resources and Environment ScienceFaculty of Resources and Environment ScienceFaculty of Resources and Environment ScienceInstitute of Science and DevelopmentFaculty of Resources and Environment ScienceAir temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (Ts) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate Ta in view of the relationship between Ta and Ts. In this article, we proposed a two-phase integrated framework to estimate daily mean Ta nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy.http://dx.doi.org/10.1155/2022/9895576 |
| spellingShingle | Shaobo Zhong Xinlan Ye Mingxing Wang Xin Mei Dunjiang Song Wenhui Wang An Integrated Framework for Mapping Nationwide Daily Temperature in China Advances in Meteorology |
| title | An Integrated Framework for Mapping Nationwide Daily Temperature in China |
| title_full | An Integrated Framework for Mapping Nationwide Daily Temperature in China |
| title_fullStr | An Integrated Framework for Mapping Nationwide Daily Temperature in China |
| title_full_unstemmed | An Integrated Framework for Mapping Nationwide Daily Temperature in China |
| title_short | An Integrated Framework for Mapping Nationwide Daily Temperature in China |
| title_sort | integrated framework for mapping nationwide daily temperature in china |
| url | http://dx.doi.org/10.1155/2022/9895576 |
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