Digital Soil Mapping
Description
The purpose of this project is to implement new soil map products at local, regional, and national scales to provide more explicit soil information than traditional soil mapping products.
Detailed example
Outputs include maps of soil type for selected local and regional project areas, including areas without any soil map products. Outputs also include continuous soil property maps representing individual soil properties predicted at seven depths for the continental United States.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
The expected benefits are that AI streamlines the data development process, creates more consistent and detailed maps, and provides objective estimates of uncertainty to support conservation activities, decision makers, and environmental modelers.
Controls / human review
ATO: Yes; PIA: Not published
Data needed
The AI models are trained and evaluated using a combination of soil field observations and previous aggregated soil spatial data. The training data include information such as soil particle size distribution, organic matter content, bulk density, depth to restrictive features, and water states.