A groundwater level-based filtering to improve the accuracy of locating agricultural tile drain and ditch networks

Abstract

Remote sensing (RS) using satellites circling around the Earth has great potential for monitoring surface processes with reduced cost and greater access. This study uses three approaches to identify possible drainage unit locations: existing benchmark techniques and a novel complementary approach based on groundwater table depth. The study area comprises a site in Ontario, Canada, and the Kleine Nete catchment, Belgium. First, a change detection method based on the interpretation of RS imagery is used to retrieve soil moisture differences. Based on the retrieved soil moisture differences, it is possible to distinguish between drained and undrained fields. Secondly, the decision tree classification (DTC) method based on filtering pixels corresponding to agricultural fields with a slowly draining soil class along with a gentle slope was applied to identify drainage units. Finally, a novel filtering technique based on groundwater table depth is applied as a complementary identification tool to the former approach.The remote sensing method resulted in 87.8% accuracy in the first study area, while the decision tree classification achieved 96.7% accuracy. Although the RS approach was not successful in following the ditch network, the DTC was able to indicate ditch networks with up to 58% accuracy. However, the additional filtering using groundwater level measurements increased the drainage unit identification accuracy in the first study area (corresponds to finding an additional 19.4 km2 area of drains). A final quantitative assessment for the second study area revealed a close follow-up of the ditch network to the shallow groundwater table maps. In general, it can be concluded that both the remote sensing and the DTC method have tremendous potential to identify drainage units, although with limitations in particular cases, such as low accuracy. Moreover, it can be advised that a local visit to the study area is required to investigate what type of drainage system is used. Next, the novel use of groundwater level-based filtering further improves the drainage identification procedure. Finally, combining several data and techniques allows for accurately identifying drainage units, which is ultimately useful for the sustainable management of drained water from agricultural fields.

Keywords

• Drainage unit identification
• Remote sensing
• Decision tree classification
• Groundwater
• Ontario Canada
• Kleine Nete Belgium