By using high-res photos snapped by a new wave of compact satellites, crop yield can be estimated from space.
Stanford researchers have developed a new way to estimate crop yields from space, using high-resolution photos snapped by a new wave of compact satellites. The approach could be used to estimate agricultural productivity and test intervention strategies in poor regions of the world where data are currently extremely scarce.
“Improving agricultural productivity is going to be one of the main ways to reduce hunger and improve livelihoods in poor parts of the world,” said study-coauthor Marshall Burke, an assistant professor of Earth system science at Stanford’s School of Earth, Energy & Environmental Sciences. “But to improve agricultural productivity, we first have to measure it, and unfortunately this isn’t done on most farms around the world.”
Earth-observing satellites have been around for over 3 decades, but most of the imagery they capture has not been high-enough resolution to visualise the very small agricultural fields typical in developing countries. Recently, however, satellites have shrunk in both size and cost while simultaneously improving in resolution, and today there are several companies competing to launch refrigerator- and shoebox-sized satellites into space that take high resolution images of Earth.
In the new study, researchers set out to test whether the images from this new wave of satellites are good enough reliably estimate crop yields. The researchers focused on an area in Western Kenya where there are a lot of smallholder farmers that grow maize. The scientists compared two different methods for estimating agricultural productivity yields using satellite imagery. The first approach involved “ground truthing,” or conducting ground surveys to check the accuracy of yield estimates calculated using the satellite data.
“We get a lot of great data, but it’s incredibly time consuming and fairly expensive, meaning we can only survey at most a thousand or so farmers during one campaign,” said Marshall Burke.”If you want to scale up our operation, you don’t want to have to recollect ground survey data everywhere in the world.”
For this reason, the team also tested an alternative “uncalibrated” approach that did not depend on ground survey data to make predictions. Instead, it uses a computer model of how crops grow, along with information on local weather conditions, to help interpret the satellite imagery and predict yields. The researchers have plans to scale up their project and test their approach across more of Africa.