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#           Machine-learning model to delineate sub-surface agricultural drainage from satellite imagery      

      May 11, 2023    

Knowing subsurface drainage (tile-drain) extent is integral to understanding how landscapes respond to precipitation events and subsequent days of drying, as well as how soil characteristics and land management influence stream response. Consequently, a time series of tile-drain extent would inform one aspect of land management that complicates our ability to explain streamflow and water-quality as a function of climate variability or conservation management. We trained a UNet machine-learning model, a convolutional neural network designed to highlight objects of interest within an image, to delineate tile-drain networks in panchromatic satellite imagery without additional data on soils, topography, or historical tile-drain extent. This was done by training the model to match the accuracy of human experts manually tracing the surface representation of tile drains in satellite imagery. Our approach began with a library of images that were used to train and quantify the accuracy of the model, with model performance tested on imagery from two areas that were not used to train the model. Satellite imagery included acquisition dates from 2008 to 2020. Training imagery was from agricultural areas within the US Great Lakes basin. Validation imagery was from the upper Maumee River, tributary to western Lake Erie, and an Indiana, Ohio-River headwater tributary. Our analysis of the satellite imagery paired with meteorological and soil data found that during spring, a combination of relatively high solar radiation, intermediate soil-water content and bare fields enabled the best model performance. Each area of interest was heavily tile-drained, where better understanding the movement of water, nutrients, and sediment from fields to downstream water bodies is key to managing harmful algal blooms and hypoxia. The trained UNet model successfully identified tile drains visible in the validation imagery with an accuracy of 93%–96% and balanced accuracy of 52%–54%, similar to performance for training data (95% and 63%, respectively). Model performance will benefit from ongoing contributions to the training library.

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link

April 20, 2020

####                 Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery              

Community for Data Integration (CDI)

link

April 20, 2020

####   Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery

Learn More

May 31, 2023

####                 Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)              

Community for Data Integration (CDI), Ohio-Kentucky-Indiana Water Science Center

May 17, 2023

####                 Continuous meteorological and soil data to support understanding of nutrient and sediment loads from overland and subsurface-tile flow at paired edge-of-field agricultural sites, 2015–21, Black Creek watershed, near Harlan, Indiana, USA              

Ohio-Kentucky-Indiana Water Science Center

May 4, 2023

####                 Meteorological data from edge-of-field sites in Michigan and Wisconsin, 2015-18              

Ohio-Kentucky-Indiana Water Science Center

#### Tanja N.  Williamson, PhD

##### Research Hydrologist

Ohio-Kentucky-Indiana Water Science Center

tnwillia@usgs.gov

502-413-7699

#### Alex Headman

##### Geographer

Washington Water Science Center

aheadman@usgs.gov

253-552-1613

## Related Content

link

April 20, 2020

####                 Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery              

Community for Data Integration (CDI)

link

April 20, 2020

####   Using machine learning to map topographic-soil & densely-patterned sub-surface agricultural drainage (tile drains) from satellite imagery

Learn More

May 31, 2023

####                 Machine learning with satellite imagery to document the historical transition from topographic to dense sub-surface agricultural drainage networks (tile drains)              

Community for Data Integration (CDI), Ohio-Kentucky-Indiana Water Science Center

May 17, 2023

####                 Continuous meteorological and soil data to support understanding of nutrient and sediment loads from overland and subsurface-tile flow at paired edge-of-field agricultural sites, 2015–21, Black Creek watershed, near Harlan, Indiana, USA              

Ohio-Kentucky-Indiana Water Science Center

May 4, 2023

####                 Meteorological data from edge-of-field sites in Michigan and Wisconsin, 2015-18              

Ohio-Kentucky-Indiana Water Science Center

#### Tanja N.  Williamson, PhD

##### Research Hydrologist

Ohio-Kentucky-Indiana Water Science Center

tnwillia@usgs.gov

502-413-7699

#### Alex Headman

##### Geographer

Washington Water Science Center

aheadman@usgs.gov

253-552-1613

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#### Dakota Water Science Center- Rapid City

1608 Mountain ViewRapid City, SD57702United States

(605) 394-3200

(605) 355-4523

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