Spatially Explicit Agricultural Tile Drainage Maps - US Midwest (SEETileDrain, Wan et al., 2024)

The source is a repository of Spatially Explicit Estimate of Tile Drainage (SEETileDrain) products across the US Midwest in 2017 at a 30-m resolution. It includes the binary classification map (tile and non-tile), tile probability (how likely a grid cell is tile-drained). The Python scripts to generate the PAW layers and the R scripts (see also: https://github.com/LuwenWan/SEETileDrain_MidWest) to select variables, implement the random forest model and visualize the figures, are also available.

In this work, we developed a machine learning model using 31 satellite-derived and environmental variables and trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud computing platform. The results show that our model achieved good accuracy, with 96 % of the points correctly classified and an F1 score of 0.90. When the tile drainage areas are aggregated to the county scale, it agrees well (R-squared = 0.68) with the reported area from the 2017 Ag Census. The product, SEETileDrain (Spatially Explicit Estimate of Tile Drainage), is described in full detail in the manuscript and the supporting information of Wan et al. (2024). If needed, copies of the tile drainage product can be requested from the corresponding author at [e-mail].

Preferred citation:
L. Wan, A.D. Kendall, J. Rapp, D.W. Hyndman. 2024. Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery, Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2024.175283

本数据集的数据源为2017年美国中西部地区30米分辨率的瓦片排水空间显式估算（Spatially Explicit Estimate of Tile Drainage, SEETileDrain）产品库。该数据集包含二分类地图（暗管排水区与非排水区）以及暗管排水概率（即每个网格单元为暗管排水区的可能性）。此外还提供了生成PAW图层的Python脚本，以及用于变量筛选、实现随机森林模型与绘图可视化的R脚本（详见：https://github.com/LuwenWan/SEETileDrain_MidWest）。

本研究依托谷歌地球引擎（Google Earth Engine, GEE）云平台，使用31个卫星衍生变量与环境变量构建机器学习模型，以60938组暗管排水区与非排水区的地面真值点开展训练。模型分类效果良好，整体分类准确率达96%，F1分数为0.90。将暗管排水区面积汇总至县级尺度后，其结果与2017年美国农业普查（Ag Census）公布的面积吻合度较高，决定系数（R-squared）为0.68。SEETileDrain（瓦片排水空间显式估算）产品的详细信息已完整发表于Wan等人（2024）的论文及其补充材料中。如有需要，可通过通讯作者邮箱[e-mail]申请获取该暗管排水产品副本。

推荐引用格式：
L. Wan, A.D. Kendall, J. Rapp, D.W. Hyndman. 2024. 利用可解释随机森林机器学习与卫星影像绘制美国中西部农业暗管排水分布图, 整体环境科学（Science of the Total Environment）. https://doi.org/10.1016/j.scitotenv.2024.175283