Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018
It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (<i>L<sub>d</sub></i>, 4–100 μm) dataset. Although a number of global <i>L<sub>d</sub></i> datasets are available, their low ac...
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MDPI AG
2021-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/9/1848 |
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| author | Chunjie Feng Xiaotong Zhang Yu Wei Weiyu Zhang Ning Hou Jiawen Xu Shuyue Yang Xianhong Xie Bo Jiang |
| author_facet | Chunjie Feng Xiaotong Zhang Yu Wei Weiyu Zhang Ning Hou Jiawen Xu Shuyue Yang Xianhong Xie Bo Jiang |
| author_sort | Chunjie Feng |
| collection | DOAJ |
| description | It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (<i>L<sub>d</sub></i>, 4–100 μm) dataset. Although a number of global <i>L<sub>d</sub></i> datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily <i>L<sub>d</sub></i> dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (<i>S<sub>d</sub></i>), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated <i>L<sub>d</sub></i> dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily <i>L<sub>d</sub></i> dataset were 17.78 W m<sup>−2</sup>, 0.99 W m<sup>−2</sup>, and 0.96 (<i>p</i> < 0.01). Comparisons with other global land surface radiation products indicated that the generated <i>L<sub>d</sub></i> dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated <i>L<sub>d</sub></i> dataset showed an increasing trend of 1.8 W m<sup>−2</sup> per decade (<i>p</i> < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated <i>L<sub>d</sub></i> dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products. |
| first_indexed | 2024-03-10T11:35:33Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T11:35:33Z |
| publishDate | 2021-05-01 |
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| series | Remote Sensing |
| spelling | doaj.art-512ffb0ded0243e889c9572edcbe2c662023-11-21T18:53:42ZengMDPI AGRemote Sensing2072-42922021-05-01139184810.3390/rs13091848Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018Chunjie Feng0Xiaotong Zhang1Yu Wei2Weiyu Zhang3Ning Hou4Jiawen Xu5Shuyue Yang6Xianhong Xie7Bo Jiang8State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, ChinaIt is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (<i>L<sub>d</sub></i>, 4–100 μm) dataset. Although a number of global <i>L<sub>d</sub></i> datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily <i>L<sub>d</sub></i> dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (<i>S<sub>d</sub></i>), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated <i>L<sub>d</sub></i> dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily <i>L<sub>d</sub></i> dataset were 17.78 W m<sup>−2</sup>, 0.99 W m<sup>−2</sup>, and 0.96 (<i>p</i> < 0.01). Comparisons with other global land surface radiation products indicated that the generated <i>L<sub>d</sub></i> dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated <i>L<sub>d</sub></i> dataset showed an increasing trend of 1.8 W m<sup>−2</sup> per decade (<i>p</i> < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated <i>L<sub>d</sub></i> dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.https://www.mdpi.com/2072-4292/13/9/1848surface downward longwave radiationair temperaturerelative humiditysurface downward shortwave radiationtotal column water vaporgradient boosting regression tree |
| spellingShingle | Chunjie Feng Xiaotong Zhang Yu Wei Weiyu Zhang Ning Hou Jiawen Xu Shuyue Yang Xianhong Xie Bo Jiang Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018 Remote Sensing surface downward longwave radiation air temperature relative humidity surface downward shortwave radiation total column water vapor gradient boosting regression tree |
| title | Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018 |
| title_full | Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018 |
| title_fullStr | Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018 |
| title_full_unstemmed | Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018 |
| title_short | Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018 |
| title_sort | estimation of long term surface downward longwave radiation over the global land from 2000 to 2018 |
| topic | surface downward longwave radiation air temperature relative humidity surface downward shortwave radiation total column water vapor gradient boosting regression tree |
| url | https://www.mdpi.com/2072-4292/13/9/1848 |
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