Xia Xingsheng, Lv Shenghui, Liu Meijuan, Yan Meng, Chen Qiong, Pan Yaozhong
Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation (Ministry of Education) & Qinghai Provincial Key Laboratory of Physical Geography and Environmental Process, School of Geographical Sciences & School of National Safety and Emergency Management, Qinghai Normal University, Xining, 810016, China.
Academy of Plateau Science and Sustainability, Qinghai Normal University & Beijing Normal University, Xining, 810016, China.
Sci Data. 2025 Aug 23;12(1):1472. doi: 10.1038/s41597-025-05841-9.
This study developed a 30-m resolution annual cropland dataset spanning 1988-2024 to resolve the unstable data quality and high sample acquisition costs in mapping cropland distributions in two agricultural regions of the Qinghai-Tibet Plateau (QTP): the Hehuang Valley (HV) and middle basin of the Yarlung Zangbo River and its two tributaries (the Lhasa and Nianchu rivers; MBYZR and LNR, respectively). This dataset was generated using Landsat imagery and training samples derived from visual interpretation. An initial classification was conducted using a Random Forest classifier. To ensure the stability of training sample quality across time, a sample cleaning approach was applied annually, based on spectral consistency constraints, allowing for the temporal extension of samples. The dataset demonstrated high classification accuracy, whereas the MBYZR and LNR demonstrated better classification performance, reflecting strong stability and robustness. Both regions showed favorable results regarding precision and recall, validating this approach's effectiveness in multi-temporal remote sensing classification. Therefore, this dataset provides critical support for cropland monitoring, food security assessment, and agricultural adaptation in QTP studies, offering a practical reference for time-series sample construction and transfer in remote sensing classification.
本研究开发了一个分辨率为30米的1988 - 2024年年度耕地数据集，以解决青藏高原两个农业区（河湟谷地（HV）以及雅鲁藏布江中游流域及其两条支流（分别为拉萨河和年楚河；MBYZR和LNR））耕地分布制图中数据质量不稳定和样本采集成本高的问题。该数据集是利用陆地卫星影像和目视解译得出的训练样本生成的。使用随机森林分类器进行初始分类。为确保训练样本质量随时间的稳定性，每年基于光谱一致性约束应用样本清理方法，以实现样本的时间扩展。该数据集显示出较高的分类精度，而MBYZR和LNR表现出更好的分类性能，体现出较强的稳定性和稳健性。两个区域在精度和召回率方面均取得了良好结果，验证了该方法在多时相遥感分类中的有效性。因此，该数据集为青藏高原研究中的耕地监测、粮食安全评估和农业适应性提供了关键支持，为遥感分类中的时间序列样本构建和转移提供了实用参考。
