The dataset of main grain land changes in China over 1985–2020
Abstract Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to pr...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04292-y |
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| author | Shidong Liu Li Wang Jie Zhang |
| author_facet | Shidong Liu Li Wang Jie Zhang |
| author_sort | Shidong Liu |
| collection | DOAJ |
| description | Abstract Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985–2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2 . |
| format | Article |
| id | doaj-art-8fa5a62426de415d871cd14e480e7cb0 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-8fa5a62426de415d871cd14e480e7cb02025-08-20T02:57:39ZengNature PortfolioScientific Data2052-44632024-12-0111111010.1038/s41597-024-04292-yThe dataset of main grain land changes in China over 1985–2020Shidong Liu0Li Wang1Jie Zhang2Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of SciencesKey Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of SciencesDepartmentof Earth System Science Tsinghua University, Tsinghua UniversityAbstract Continuous, Accurate, and detailed information on main grain land (MGL) areas is crucial for provisioning food security and making policies affecting sustainable agricultural production. It still lacks a long-term MGL distribution dataset with fine spatial resolution. This study aimed to produce a long-term, high-resolution MGL distribution map for China. Here, we developed the change map of MGL with resolution 30 m in China for the period 1985–2020 using the Landsat image-based random forest algorithm on the GEE platform. Finally, the planting intensity, gain time and loss time of MGL was calculated. Results indicate that our mapping results are highly consistent with the annual planting area of various grain crops according to national statistics. A validation based on 3113 field survey samples with a 30-m resolution showed that the overall accuracy of MGL were 93.57%. The full MGL dataset is freely available at https://doi.org/10.6084/m9.figshare.26212643.v2 .https://doi.org/10.1038/s41597-024-04292-y |
| spellingShingle | Shidong Liu Li Wang Jie Zhang The dataset of main grain land changes in China over 1985–2020 Scientific Data |
| title | The dataset of main grain land changes in China over 1985–2020 |
| title_full | The dataset of main grain land changes in China over 1985–2020 |
| title_fullStr | The dataset of main grain land changes in China over 1985–2020 |
| title_full_unstemmed | The dataset of main grain land changes in China over 1985–2020 |
| title_short | The dataset of main grain land changes in China over 1985–2020 |
| title_sort | dataset of main grain land changes in china over 1985 2020 |
| url | https://doi.org/10.1038/s41597-024-04292-y |
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