Mapping agricultural tile drainage in the US Midwest using explainable random forest machine learning and satellite imagery
Abstract
There has been an increase in tile-drained areas across the US Midwest and other regions worldwide due to agricultural expansion, intensification, and climate variability. Despite this growth, spatially explicit tile drainage maps remain scarce, limiting the accuracy of hydrologic modeling and the implementation of nutrient reduction strategies. Here, we developed a machine-learning model to provide a Spatially Explicit Estimate of Tile Drainage (SEETileDrain) across the US Midwest in 2017 at a 30-meter resolution. This model used 31 satellite-derived and environmental features after removing less important and highly correlated features. It was trained with 60,938 tile and non-tile ground truth points within the Google Earth Engine cloud-computing platform. We also used multiple feature importance metrics and Accumulated Local Effects to interpret the machine learning model. Our results show that the model achieved high accuracy, with 96% of points classified correctly and an F1 score of 0.90. When tile drainage area was aggregated to the county scale, it agreed well (r² = 0.69) with the reported area from the Ag Census. We found that Land Surface Temperature (LST), along with climate- and soil-related features, were the most important factors for classification. The top-ranked feature was the median summer nighttime LST, followed by median summer soil moisture percentage. This study demonstrates the potential of applying satellite remote sensing to map spatially explicit agricultural tile drainage across large regions. The results should be useful for land-use change monitoring and for hydrologic and nutrient models, including those designed to achieve cost-effective agricultural water and nutrient management strategies. The algorithms developed here should also be applicable for other remote sensing mapping applications.
Luwen Wan
Postdoctoral Fellow
I am an environmental hydrologist who combines geography, hydrogeology and environmental science, and approaches like remote sensing, GIS, machine learning, high-performance computing, and big data analytics to address critical water sustainability challenges under changing landscape and climate.