用可解释的随机森林机器学习和卫星图像绘制美国中西部农业排水地图
Luwen Wan1, Anthony D Kendall2, Jeremy Rapp2
1Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824, USA; Department of Earth System Science, Stanford University, Stanford, CA 94305, USA; Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA 94305, USA.
The Science of the total environment
|August 7, 2024
概括
绘制农业排水地图对于水资源管理至关重要. 这项研究开发了一个机器学习模型,使用卫星数据为美国中西部创建精确的排水地图,改进了水文和营养模型.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 由于农业的强化和气候变化,农业排水在全球范围内不断扩大.
- 缺少精确的,空间上明确的排水地图,阻碍了水文建模和营养管理.
- 现有的方法难以在大型农业地区提供高分辨率的排水数据.
研究的目的:
- 开发和验证一个机器学习模型,用于创建一个高分辨率 (30米) 空间显式估计排水 (SEETileDrain) 在美国中西部.
- 确定影响排水的主要卫星衍生和环境特征.
- 为改善农业用水和营养管理策略提供准确的数据.
主要方法:
- 开发了一个机器学习模型 (SEETileDrain),使用31个卫星衍生和环境特征.
- 在谷歌地球引擎平台上使用超过6万个地面真理点训练模型.
- 采用特征重要性指标和模型解释的累积局部效应.
主要成果:
- 该SEETileDrain模型实现了高精度 (96%的正确分类,F1得分为0.90).
- 各县汇总的排水面积与农业普查数据有很强的一致性 (r2 = 0.69).
- 平均夏季夜间土地表面温度 (LST) 和土壤湿度是最有影响力的特征.
结论:
- 卫星遥感有效地绘制了农业排水的大规模地图,具有高空间明确性.
- SEETileDrain模型为土地利用监测,水文和营养模型提供了有价值的数据.
- 开发的算法有可能用于农业中更广泛的遥感测绘应用.
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