Monitoring soil carbon with hyperspectral sensing and AI

10-03 | |
Understanding soil organic carbon at regional, national, or global scales could help scientists predict overall soil health, crop productivity, and even worldwide carbon cycles. - Photo: Canva
Understanding soil organic carbon at regional, national, or global scales could help scientists predict overall soil health, crop productivity, and even worldwide carbon cycles. - Photo: Canva

University of Illinois researchers show new machine-learning methods based on laboratory soil hyperspectral data could supply accurate estimates of soil organic carbon.

Understanding soil organic carbon at regional, national, or global scales could help scientists predict overall soil health, crop productivity, and even worldwide carbon cycles.

Classically, researchers collect soil samples in the field and haul them back to the lab, where they analyse the material to determine its makeup. But that’s time- and labor-intensive, costly, and only provides insights on specific locations.

Laboratory soil hyperspectral data

In a recent study, University of Illinois researchers show new machine-learning methods based on laboratory soil hyperspectral data could supply equally accurate estimates of soil organic carbon. Their study provides a foundation to use airborne and satellite hyperspectral sensing to monitor surface soil organic carbon across large areas.

Lead study author Sheng Wang and his collaborators leveraged a public soil spectral library from the USDA Natural Resources Conservation Service containing more than 37,500 field-collected records and representing all soil types around the United States. Soil reflects light in unique spectral bands which scientists can interpret to determine chemical makeup.

Full gamut of machine learning algorithms

According to Andrew Margenot, assistant professor in the Department of Crop Sciences and co-author on the study, you can get carbon content by scanning an unknown sample and applying a statistical method that’s been used for decades. “But here, we tried to screen across pretty much every potential modeling method. We knew some of these models worked, but the novelty is the scale and that we tried the full gamut of machine learning algorithms.”

After selecting the best algorithm based on the soil library, the researchers put it to the test with simulated airborne and spaceborne hyperspectral data. As expected, their model accounted for the “noise” inherent in surface spectral imagery, returning a highly accurate and large-scale view of soil organic carbon.

“NASA and other institutions have new or forthcoming hyperspectral satellite missions, and it’s very exciting to know we will be ready to leverage new AI technology to predict important soil properties with spectral data coming back from these missions,” Wang says.

Kaiyu Guan, principal investigator, ASC founding director, and associate professor at NRES, said, “This work established the foundation for using hyperspectral and multispectral remote sensing technology to measure soil carbon properties at the soil surface level. This could enable scaling to possibly everywhere.”

Claver
Hugo Claver Web editor for Future Farming
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