基于深度学习的多作物种植面积精细提取,使用Sentinel-2时间序列数据
Jingmin Jiang1,2, Jiahua Zhang3,4, Xue Wang1
1Space Information and Big Earth Data Research Center, School of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
Environmental science and pollution research international
|April 21, 2025
概括
这项研究引入了一个深度学习模型,用于使用卫星数据精确的多作物分类. CNN-LSTM模型的准确度达到96.48%,超过了绘制作物种植面积的其他方法.
科学领域:
- 农业遥感 农业遥感
- 地理空间分析是什么?
- 机器学习用于农业
背景情况:
- 准确地绘制作物分布地图对于粮食安全和可持续农业至关重要.
- 在使用深度学习从高分辨率的时间序列数据中提取多种作物区域存在挑战.
- 现有的方法可能无法充分利用遥感数据中的时间和空间信息.
研究的目的:
- 开发和评估一种有效的深度学习模型,用于使用高分辨率遥感时间序列数据进行多作物分类.
- 为了比较CNN-LSTM和Bi-LSTM模型与传统机器学习算法的性能.
- 评估模型在区域范围内绘制多种作物类型的能力.
主要方法:
- 利用Sentinel-2数据生成月度合成时间序列的规范差异植被指数 (NDVI).
- 开发并实施了两个深度学习模型:卷积神经网络长期短期记忆 (CNN-LSTM) 和双向长期短期记忆 (Bi-LSTM).
- 将深度学习模型与随机森林 (RF) 和极端梯度提升 (XGBoost) 进行了比较,用于山东省的多作物分类.
主要成果:
- 深度学习模型在多作物分类准确性方面显著优于RF和XGBoost.
- 该CNN-LSTM模型实现了96.48%的最高整体精度.
- 在县级,CNN-LSTM表现强,确定系数 (R2) 高:小麦0.91,玉米0.88,春季棉花0.73.
结论:
- 结合每月合成NDVI时间序列的CNN-LSTM模型,为高分辨率的多作物种植面积绘制提供了可行和准确的方法.
- 该方法为农业决策支持和资源管理提供了有价值的数据.
- 深度学习模型显示了促进作物监测和农业可持续性的巨大潜力.
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