Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Satellites, supercomputers, and machine learning provide real-time crop type data

05.04.2018

Corn and soybean fields look similar from space - at least they used to. But now, scientists have proven a new technique for distinguishing the two crops using satellite data and the processing power of supercomputers.

"If we want to predict corn or soybean production for Illinois or the entire United States, we have to know where they are being grown," says Kaiyu Guan, assistant professor in the Department of Natural Resources and Environmental Sciences at the University of Illinois, Blue Waters professor at the National Center for Supercomputing Applications (NCSA), and the principal investigator of the new study.


University of Illinois scientists used short-wave infrared bands from Landsat satellites to accurately distinguish corn and soybeans during the growing season.

Credit: Kaiyu Guan, University of Illinois

The advancement, published in Remote Sensing of Environment, is a breakthrough because, previously, national corn and soybean acreages were only made available to the public four to six months after harvest by the USDA. The lag meant policy decisions were based on stale data. But the new technique can distinguish the two major crops with 95 percent accuracy by the end of July for each field - just two or three months after planting and well before harvest.

The researchers argue more timely estimates of crop areas could be used for a variety of monitoring and decision-making applications, including crop insurance, land rental, supply-chain logistics, commodity markets, and more.

For Guan, however, the work's scientific value is as important as its practical value.

A set of satellites known as Landsat have been continuously circling the Earth for 40 years, collecting images using sensors that represent different parts of the electromagnetic spectrum. Guan says most previous attempts to differentiate corn and soybean from these images were based on the visible and near-infrared part of the spectrum, but he and his team decided to try something different.

"We found a spectral band, the short-wave infrared (SWIR), that was extremely useful in identifying the difference between corn and soybean," says Yaping Cai, Ph.D. student and first author of the work, following the guidance of Guan and another senior co-author, Shaowen Wang in the Department of Geography at U of I.

It turns out corn and soybean have predictably different leaf water status by July most years. The team used SWIR data and other spectral data from three Landsat satellites over a 15-year period, and consistently picked up this leaf water status signal.

"The SWIR band is more sensitive to water content inside the leaf. That signal can't be captured by traditional RGB (visible) light or near-infrared bands, so the SWIR is extremely useful to differentiate corn and soybean," Guan concludes.

The researchers used a type of machine-learning, known as a deep neural network, to analyze the data.

"Deep learning approaches have just started to be applied for agricultural applications, and we foresee a huge potential of such technologies for future innovations in this area," says Jian Peng, assistant professor in the Department of Computer Science at U of I, and a co-author and co-principal investigator of the new study.

The team focused their analysis within Champaign County, Illinois, as a proof-of-concept. Even though it was a relatively small area, analyzing 15 years of satellite data at a 30-meter resolution still required a supercomputer to process tens of terabytes of data.

"It's a huge amount of satellite data. We used the Blue Waters and ROGER supercomputers at the NCSA to handle the process and extract useful information," Guan says. "Technology wise, being able to handle such a huge amount of data and apply an advanced machine-learning algorithm was a big challenge before, but now we have supercomputers and the skills to handle the dataset."

The team is now working on expanding the study area to the entire Corn Belt, and investigating further applications of the data, including yield and other quality estimates.

###

The article, "A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach," is published in Remote Sensing of Environment [DOI: 10.1016/j.rse.2018.02.045]. Additional authors include Christopher Seifert, Brian Wardlow, and Zhan Li. The work was supported by NCSA, NASA, and the National Science Foundation.

Media Contact

Lauren Quinn
ldquinn@illinois.edu
217-300-2435

 @ACESIllinois

http://aces.illinois.edu/ 

Lauren Quinn | EurekAlert!

Further reports about: Agricultural Environmental Sciences crop data satellite data

More articles from Agricultural and Forestry Science:

nachricht Soil-less sustainability: Novel agricultural crop production incorporating water reuse
02.04.2020 | ISOE - Institut für sozial-ökologische Forschung

nachricht Exeter researchers discover a novel chemistry to protect our crops from fungal disease
30.03.2020 | University of Exeter

All articles from Agricultural and Forestry Science >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: A sensational discovery: Traces of rainforests in West Antarctica

90 million-year-old forest soil provides unexpected evidence for exceptionally warm climate near the South Pole in the Cretaceous

An international team of researchers led by geoscientists from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) have now...

Im Focus: Blocking the Iron Transport Could Stop Tuberculosis

The bacteria that cause tuberculosis need iron to survive. Researchers at the University of Zurich have now solved the first detailed structure of the transport protein responsible for the iron supply. When the iron transport into the bacteria is inhibited, the pathogen can no longer grow. This opens novel ways to develop targeted tuberculosis drugs.

One of the most devastating pathogens that lives inside human cells is Mycobacterium tuberculosis, the bacillus that causes tuberculosis. According to the...

Im Focus: Physicist from Hannover Develops New Photon Source for Tap-proof Communication

An international team with the participation of Prof. Dr. Michael Kues from the Cluster of Excellence PhoenixD at Leibniz University Hannover has developed a new method for generating quantum-entangled photons in a spectral range of light that was previously inaccessible. The discovery can make the encryption of satellite-based communications much more secure in the future.

A 15-member research team from the UK, Germany and Japan has developed a new method for generating and detecting quantum-entangled photons at a wavelength of...

Im Focus: Junior scientists at the University of Rostock invent a funnel for light

Together with their colleagues from the University of Würzburg, physicists from the group of Professor Alexander Szameit at the University of Rostock have devised a “funnel” for photons. Their discovery was recently published in the renowned journal Science and holds great promise for novel ultra-sensitive detectors as well as innovative applications in telecommunications and information processing.

The quantum-optical properties of light and its interaction with matter has fascinated the Rostock professor Alexander Szameit since College.

Im Focus: Stem Cells and Nerves Interact in Tissue Regeneration and Cancer Progression

Researchers at the University of Zurich show that different stem cell populations are innervated in distinct ways. Innervation may therefore be crucial for proper tissue regeneration. They also demonstrate that cancer stem cells likewise establish contacts with nerves. Targeting tumour innervation could thus lead to new cancer therapies.

Stem cells can generate a variety of specific tissues and are increasingly used for clinical applications such as the replacement of bone or cartilage....

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

13th AKL – International Laser Technology Congress: May 4–6, 2022 in Aachen – Laser Technology Live already this year!

02.04.2020 | Event News

“4th Hybrid Materials and Structures 2020” takes place over the internet

26.03.2020 | Event News

Most significant international Learning Analytics conference will take place – fully online

23.03.2020 | Event News

 
Latest News

Scientists see energy gap modulations in a cuprate superconductor

02.04.2020 | Physics and Astronomy

AI finds 2D materials in the blink of an eye

02.04.2020 | Information Technology

New 3D cultured cells mimic the progress of NASH

02.04.2020 | Health and Medicine

VideoLinks
Science & Research
Overview of more VideoLinks >>>