多传感器NDVI时间序列用于俄罗斯哈巴罗夫斯克州的作物和地分类
Lyubov Illarionova1, Konstantin Dubrovin1, Elizaveta Fomina1
1Computing Center of the Far Eastern Branch of the Russian Academy of Sciences, Khabarovsk 680000, Russia.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究表明,即使有云层覆盖,使用卫星数据也能准确地绘制作物地图. 结合来自Sentinel-2,Landsat-8/9和Meteor-M卫星的数据,大幅提高了可持续农业的土地覆盖分类准确性.
科学领域:
- 农业遥感 农业遥感
- 可持续农业 可持续农业
- 地理空间分析的研究.
背景情况:
- 多年农田监测对于可持续农业至关重要,但面临着作物表态和天气变化的挑战.
- 单个卫星多谱数据可以与植被指数时间序列相扎,特别是在阴天地区.
- 准确的作物测绘对于有效的农业管理和资源分配至关重要.
研究的目的:
- 使用单个和联合卫星数据集 (Sentinel-2,Landsat-8/9,Meteor-M) 评估作物分类的准确性.
- 为了评估里埃序列适配NDVI时间序列构造的有效性.
- 为在哈巴罗夫斯克地区等具有挑战性的地区提供可靠的多年作物绘制方法.
主要方法:
- 来自Sentinel-2和Landsat-8/9的NDVI时间序列是富里埃序列的.
- 每天的NDVI复合图是从Meteor-M卫星图像中生成的.
- 随机森林分类每年应用于五个土地覆盖类别:大豆,谷物作物,多年草,麦和地.
主要成果:
- 年平均分类准确度:Landsat 8/9 (87%),Meteor-M (89%),Sentinel-2 (93%).这些卫星的分类准确度均为9%.
- 将所有三颗卫星的数据结合起来,将交叉验证准确度从92%提高到96% (2022年) 和96%提高到97% (2023年).
- 获得了高分类准确度,证明了方法的稳定性.
结论:
- 单个卫星数据可以提供足够准确的作物绘图.
- 综合卫星数据集提供始终高准确度,是可靠的替代方案,特别是当哨兵数据受到云覆盖的限制时.
- 该研究强调了综合卫星数据在可持续农业中用于先进的耕地监测的潜力.
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