High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022
Abstract Accurate and timely information on planting intensity and crop rotation is essential for guiding agricultural policies and ensuring food security. However, reliable and up-to-date maps for major crops—wheat, maize, rapeseed, soybean, and potatoes—are lacking in the Loess Plateau, a key grai...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05529-0 |
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| author | Xining Zhao Jichao Wang Yelu Ding Xiaodong Gao Changjian Li Hongwei Huang Xuerui Gao |
| author_facet | Xining Zhao Jichao Wang Yelu Ding Xiaodong Gao Changjian Li Hongwei Huang Xuerui Gao |
| author_sort | Xining Zhao |
| collection | DOAJ |
| description | Abstract Accurate and timely information on planting intensity and crop rotation is essential for guiding agricultural policies and ensuring food security. However, reliable and up-to-date maps for major crops—wheat, maize, rapeseed, soybean, and potatoes—are lacking in the Loess Plateau, a key grain-producing region in western China. To address this gap, this study aims to generate a high-resolution (10 m) crop planting pattern dataset for the Loess Plateau from 2018 to 2022. The research methodology involved four key steps: (1) Enhancing the sample dataset using phenological indices and the Dynamic Time Warping (DTW) algorithm; (2) Identifying crop planting intensity based on phenological growth curves; (3) Developing independent random forest classifiers tailored to agricultural climate zones; and (4) Constructing an optimal feature subset for crop classification. The resulting maps demonstrated high overall accuracies (OA) is greater than 0.81, with satellite-based estimates showing strong agreement with municipal statistical data (R2 ≥ 0.60). These results provide crucial insights for the management of agricultural ecosystems in the Loess Plateau and can support more informed decision-making in regional agriculture. |
| format | Article |
| id | doaj-art-6a55a5b683cb4d74a0c44eba9b331efd |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-6a55a5b683cb4d74a0c44eba9b331efd2025-08-20T03:42:30ZengNature PortfolioScientific Data2052-44632025-07-011211910.1038/s41597-025-05529-0High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022Xining Zhao0Jichao Wang1Yelu Ding2Xiaodong Gao3Changjian Li4Hongwei Huang5Xuerui Gao6Institute of Soil and Water Conservation, Northwest A&F UniversityCollege of Water Resources and Architectural Engineering, Northwest A&F UniversityCollege of Water Resources and Architectural Engineering, Northwest A&F UniversityInstitute of Soil and Water Conservation, Northwest A&F UniversityInstitute of Soil and Water Conservation, Northwest A&F UniversityCollege of Water Resources and Architectural Engineering, Northwest A&F UniversityInstitute of Soil and Water Conservation, Northwest A&F UniversityAbstract Accurate and timely information on planting intensity and crop rotation is essential for guiding agricultural policies and ensuring food security. However, reliable and up-to-date maps for major crops—wheat, maize, rapeseed, soybean, and potatoes—are lacking in the Loess Plateau, a key grain-producing region in western China. To address this gap, this study aims to generate a high-resolution (10 m) crop planting pattern dataset for the Loess Plateau from 2018 to 2022. The research methodology involved four key steps: (1) Enhancing the sample dataset using phenological indices and the Dynamic Time Warping (DTW) algorithm; (2) Identifying crop planting intensity based on phenological growth curves; (3) Developing independent random forest classifiers tailored to agricultural climate zones; and (4) Constructing an optimal feature subset for crop classification. The resulting maps demonstrated high overall accuracies (OA) is greater than 0.81, with satellite-based estimates showing strong agreement with municipal statistical data (R2 ≥ 0.60). These results provide crucial insights for the management of agricultural ecosystems in the Loess Plateau and can support more informed decision-making in regional agriculture.https://doi.org/10.1038/s41597-025-05529-0 |
| spellingShingle | Xining Zhao Jichao Wang Yelu Ding Xiaodong Gao Changjian Li Hongwei Huang Xuerui Gao High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 Scientific Data |
| title | High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 |
| title_full | High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 |
| title_fullStr | High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 |
| title_full_unstemmed | High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 |
| title_short | High-resolution (10 m) dataset of multi-crop planting structure on the Loess Plateau during 2018–2022 |
| title_sort | high resolution 10 m dataset of multi crop planting structure on the loess plateau during 2018 2022 |
| url | https://doi.org/10.1038/s41597-025-05529-0 |
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