A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution
Abstract Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-01991-w |
| _version_ | 1797865629718413312 |
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| author | Chaolei Zheng Li Jia Tianjie Zhao |
| author_facet | Chaolei Zheng Li Jia Tianjie Zhao |
| author_sort | Chaolei Zheng |
| collection | DOAJ |
| description | Abstract Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m3/m3) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far. |
| first_indexed | 2024-04-09T23:11:11Z |
| format | Article |
| id | doaj.art-c1ce42d2dc3b4eea9b7110a464f9e958 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-09T23:11:11Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-c1ce42d2dc3b4eea9b7110a464f9e9582023-03-22T10:23:43ZengNature PortfolioScientific Data2052-44632023-03-0110111410.1038/s41597-023-01991-wA 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolutionChaolei Zheng0Li Jia1Tianjie Zhao2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of SciencesState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of SciencesState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of SciencesAbstract Global soil moisture estimates from current satellite missions are suffering from inherent discontinuous observations and coarse spatial resolution, which limit applications especially at the fine spatial scale. This study developed a dataset of global gap-free surface soil moisture (SSM) at daily 1-km resolution from 2000 to 2020. This is achieved based on the European Space Agency - Climate Change Initiative (ESA-CCI) SSM combined product at 0.25° resolution. Firstly, an operational gap-filling method was developed to fill the missing data in the ESA-CCI SSM product using SSM of the ERA5 reanalysis dataset. Random Forest algorithm was then adopted to disaggregate the coarse-resolution SSM to 1-km, with the help of International Soil Moisture Network in-situ observations and other optical remote sensing datasets. The generated 1-km SSM product had good accuracy, with a high correlation coefficent (0.89) and a low unbiased Root Mean Square Error (0.045 m3/m3) by cross-validation. To the best of our knowledge, this is currently the only long-term global gap-free 1-km soil moisture dataset by far.https://doi.org/10.1038/s41597-023-01991-w |
| spellingShingle | Chaolei Zheng Li Jia Tianjie Zhao A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution Scientific Data |
| title | A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution |
| title_full | A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution |
| title_fullStr | A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution |
| title_full_unstemmed | A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution |
| title_short | A 21-year dataset (2000–2020) of gap-free global daily surface soil moisture at 1-km grid resolution |
| title_sort | 21 year dataset 2000 2020 of gap free global daily surface soil moisture at 1 km grid resolution |
| url | https://doi.org/10.1038/s41597-023-01991-w |
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