SAN FRANCISCO, Feb. 14 (Xinhua) -- Researchers at Stanford University have used high-resolution photos snapped by compact satellites and developed a new way to estimate crop yields from space, raising hope that it may help test intervention strategies in regions of the world where data are 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 Marshall Burke, an assistant professor of Earth system science at Stanford's School of Earth, Energy & Environmental Sciences and co-author of a study published in Proceedings of the National Academy of 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 three decades, but most of the imagery they capture has not been of high enough resolution to visualize the very small agricultural fields typical in developing countries. Recently, satellites have shrunk in both size and cost while simultaneously improving in resolution. "You can get lots of them up there, all capturing very small parts of the land surface at very high resolution," study-co-author David Lobell, an associate professor of Earth system science, was quoted as saying in a news release.
"Any one satellite doesn't give you very much information, but the constellation of them actually means that you're covering most of the world at very high resolution and at very low cost," said Lobell. "That's something we never really had even a few years ago."
The researchers compared two different methods for estimating agricultural productivity yields using satellite imagery, the first involving "ground truthing," or conducting ground surveys to check the accuracy of yield estimates calculated using the satellite data, which was donated by the company Terra Bella. "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," Burke said. "If you want to scale up our operation, you don't want to have to recollect ground survey data everywhere in the world."
Burke and his field team spent weeks conducting house-to-house surveys, talking to farmers and gathering information about individual farms.
The team also tested an alternative "uncalibrated" approach that did not depend on ground survey data to make predictions. Instead, it used a computer model of how crops grow, along with information on local weather conditions, to help interpret the satellite imagery and predict yields.
"Just combining the imagery with computer-based crop models allows us to make surprisingly accurate predictions, based on the imagery alone, of actual productivity on the field," Burke said.
The researchers have plans to scale up their project and test their approach across more of Africa. "Our aspiration is to make accurate seasonal predictions of agricultural productivity for every corner of sub-Saharan Africa," Burke said.