Mapping of Hay-Harvesting Grasslands Using Harmonized Landsat Sentinel-2 Time Series and Deep Learning in Temperate Steppe
文献类型：期刊论文
| 作者 | Lu, Wei1,2; Hu, Yunfeng1,2; Batunacun1,3; Liu, Jia1,2; Li, Hao1,2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4419016 |
| 关键词 | Grasslands Remote sensing Meteorology Livestock Time series analysis Optical sensors Monitoring Europe Deep learning Normalized difference vegetation index hay-harvesting grassland mapping optical remote sensing temperate steppe time series |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3614355 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Natural hay-harvesting grasslands are essential for sustaining livestock through winters in semi-arid temperate steppes. However, limited historical records and the absence of systematic spatial data hinder regional management. Moreover, although threshold-based and machine learning approaches have demonstrated effectiveness in monitoring mowing in Western Europe, their applicability in Eurasian semi-arid steppes remains uncertain. Therefore, we aimed to develop an approach for mapping natural hay-harvesting grasslands in semi-arid temperate steppes. Specifically, based on optical satellite time-series data, we employed a time-series classification architecture, light inception with boosting technique (LITE), to identify hay-harvesting grasslands. Results of our experiments, conducted in a typical region in Inner Mongolia, demonstrated that our approach can generate high-quality hay-harvesting grassland maps, with the testing accuracy of 92.06% ( ${F}1$ -score) and 90.65% [Overall accuracy (OA)]. Furthermore, we applied the gradient-weighted class activation mapping (Grad-CAM) technique to interpret the decision-making process of the deep learning model. Our findings showed that LITE concentrated intensively on time steps surrounding mowing dates, underscoring its superiority in mapping the geospatial extent of hay-harvesting grasslands and its potential for extracting temporal information related to hay harvesting. This study offers valuable insights for developing fine-scale hay-harvesting grassland inventories in semi-arid temperate steppe, an area long overlooked in existing research, thereby supporting the sustainable management of grassland resources, and promoting the advancement of smart herding practices. |
| URL标识 | 查看原文 |
| WOS关键词 | FUSION ; SALT |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001591674300028 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217573]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Hu, Yunfeng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China; 3.Inner Mongolia Normal Univ, Coll Geog Sci, Hohhot 010028, Inner Mongolia, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Wei,Hu, Yunfeng,Batunacun,et al. Mapping of Hay-Harvesting Grasslands Using Harmonized Landsat Sentinel-2 Time Series and Deep Learning in Temperate Steppe[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4419016. |
| APA | Lu, Wei,Hu, Yunfeng,Batunacun,Liu, Jia,&Li, Hao.(2025).Mapping of Hay-Harvesting Grasslands Using Harmonized Landsat Sentinel-2 Time Series and Deep Learning in Temperate Steppe.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4419016. |
| MLA | Lu, Wei,et al."Mapping of Hay-Harvesting Grasslands Using Harmonized Landsat Sentinel-2 Time Series and Deep Learning in Temperate Steppe".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4419016. |
入库方式： OAI收割
来源：地理科学与资源研究所
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