Harnessing rich satellite data to estimate crop yield
Leveraging satellite data greatly increases capacity to monitor crops and crop yield.
August 22, 2017
![Harnessing rich satellite data to estimate crop yield Harnessing rich satellite data to estimate crop yield](https://eu-images.contentstack.com/v3/assets/blte5a51c2d28bbcc9c/bltca97a5b655e1d74b/646ce0fe2b2b262fd0843336/feedstuffs-placeholder.png?width=850&auto=webp&quality=95&format=jpg&disable=upscale)
Without advanced sensing technology, humans see only a small portion of the entire electromagnetic spectrum, while satellites see the full range — from high-energy gamma rays to visible, infrared and low-energy microwaves.
The images and data satellites collect can be used to solve complex problems. For example, satellite data are being harnessed by researchers at the University of Illinois to obtain a more complete picture of cropland and to estimate crop yield in the U.S. Corn Belt.
"In places where we may see just the color green in crops, electromagnetic imaging from satellites reveals much more information about what's actually happening in the leaves of plants and even inside the canopy. How to leverage this information is the challenge," said Kaiyu Guan, University of Illinois environmental scientist and lead author on the research. "Using various spectral bands and looking at them in an integrated way, reveals rich information for improving crop yield."
Guan said this work is the first time so many spectral bands -- including visible, infrared, thermal, passive and active microwave and canopy fluorescence measurements -- have been brought together to look at crops.
"We used an integrated framework called partial least-square regression to analyze all of the data together. This specific approach can identify commonly shared information across the different data sets. When we pull the shared information out from each data set, what's left is the unique information relevant to vegetation conditions and crop yield," Guan explained.
The study showed that many satellite data sets share common information related to crop biomass grown aboveground. However, the researchers also discovered that different satellite data can reveal environmental stresses that crops experience related to drought and heat.
Guan said the challenging aspect of crop observation is that the grain -- which is what crop yield is all about -- grows inside the canopy, where it isn't visible from above.
"Visible or near-infrared bands typically used for crop monitoring are mainly sensitive to the upper canopy but provide little information about deeper vegetation and soil conditions affecting crop water status and yield," added John Kimball from the University of Montana, a long-term collaborator with Guan and a co-author of the paper.
"Our study suggests that the microwave radar data at the Ku-band contain uniquely useful information on crop growth," Guan said. "Besides the biomass information, it also contains additional information associated with crop water stress because of the higher microwave sensitivity to canopy water content, and microwave can also penetrate the canopy and see through part or all the canopy.
"We also find that thermal bands provide water and heat stress information," Guan added. "This information tells us when leaves open or close their pores to breathe and absorb carbon for growth."
Co-author David Lobell from Stanford University, who crafted the idea with Guan, said leveraging all of the satellite data together greatly increases the capacity to monitor crops and crop yield.
"This is an age of big data. How to make sense of all of the data available, to generate useful information for farmers, economists and others who need to know the crop yield, is an important challenge," Guan said. "This will be an important tool. Although we started with the U.S. Corn Belt, this framework can be used to analyze cropland anywhere on the planet."
The study, "The Shared & Unique Values of Optical, Fluorescence, Thermal & Microwave Satellite Data for Estimating Large-scale Crop Yields," was published in Remote Sensing of Environment.
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