Hyperspectral sensing and AI pave new path for monitoring soil carbon


Date:
March 1, 2022
Source:
University of Illinois College of Agricultural, Consumer and
Environmental Sciences
Summary:
Just how much carbon is in the soil? That's a tough question to
answer at large spatial scales, but understanding soil organic
carbon at regional, national, or global scales could help scientists
predict overall soil health, crop productivity, and even worldwide
carbon cycles.



FULL STORY ==========================================================================
Just how much carbon is in the soil? That's a tough question to answer at
large spatial scales, but 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 analyze the material to determine its
makeup. But that's time- and labor-intensive, costly, and only provides insights on specific locations.

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.

"Soil organic carbon is a very important component for soil health, as
well as for cropland productivity," says lead study author Sheng Wang,
research assistant professor in the Agroecosystem Sustainability Center
(ASC) and the Department of Natural Resources and Environmental Sciences
(NRES) at U of I.

"We did a comprehensive evaluation of machine learning algorithms with
a very intensive national soil laboratory spectral database to quantify
soil organic carbon." 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 U.S. Like every substance, soil reflects light
in unique spectral bands which scientists can interpret to determine
chemical makeup.

"Spectra are data-rich fingerprints of soil properties; we're talking
thousands of points for each sample," says 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,"
he adds.



==========================================================================
"We knew some of these models worked, but the novelty is the scale
and that we tried the full gamut of machine learning algorithms."
Kaiyu Guan, principal investigator, ASC founding director, and associate professor at NRES, says, "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." 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.

Chenhui Zhang, an undergraduate student studying computer science at
Illinois, also worked on the project as part of an internship with
the National Center for Supercomputing Applications' Students Pushing Innovation (SPIN) program.

"Hyperspectral data can provide rich information on soil
properties. Recent advances in machine learning saved us from the nuisance
of constructing hand- crafted features while providing high predictive performance for soil carbon," Zhang says. "As a leading university in
computer sciences and agriculture, U of I gives a great opportunity to
explore interdisciplinary sciences on AI and agriculture. I feel really
excited about that." The research was supported by the U.S. Department
of Energy's Advanced Research Projects Agency-Energy (ARPA-E) SMARTFARM
and SYMFONI projects, Illinois Discovery Partners Institute (DPI),
Institute for Sustainability, Energy, and Environment (iSEE), and
College of Agricultural, Consumer and Environmental Sciences Future Interdisciplinary Research Explorations (ACES FIRE), Center for Digital Agriculture (CDA-NCSA), University of Illinois at Urbana-Champaign.

This work was also partially funded by the USDA National Institute of Food
and Agriculture (NIFA) Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability grant.

The Departments of Natural Resources and Environmental Sciences and Crop Sciences are in the College of Agricultural, Consumer and Environmental Sciences (ACES) at the University of Illinois Urbana-Champaign.

========================================================================== Story Source: Materials provided by University_of_Illinois_College_of_Agricultural,_Consumer and_Environmental_Sciences. Original written by Lauren Quinn. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sheng Wang, Kaiyu Guan, Chenhui Zhang, DoKyoung Lee, Andrew
J. Margenot,
Yufeng Ge, Jian Peng, Wang Zhou, Qu Zhou, Yizhi Huang. Using soil
library hyperspectral reflectance and machine learning to predict
soil organic carbon: Assessing potential of airborne and spaceborne
optical soil sensing. Remote Sensing of Environment, 2022; 271:
112914 DOI: 10.1016/ j.rse.2022.112914 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220301152340.htm

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