News Release

MADISON, WI, DECEMBER 28, 2007 -- Crop mapping tools including remote-sensed images and satellite data have been used since the 1970s, yet financial and weather limitations have prevented farmers from taking full advantage of these crop monitoring strategies.

Recent research at South Dakota State University has made use of existing data from MODIS (MODerate Resolution Imaging Spectroradiometer) and the Landsat-derived USDA NASS Cropland Data Layers (CDL) to develop more accurate crop maps for more effective crop monitoring of corn and soybean areas in the U.S.

Efficient and timely crop mapping can help farmers and scientists alike monitor crop production to better anticipate market fluctuations and ensure an adequate food supply. Some challenges in doing this include properly spacing and timing the images to help accurately identify the crops as well as subtle differences in their growth.

By running analyses on the MODIS time-series data and NASS data, the South Dakota State University researchers found that their estimates of corn and soybean cover differed by 6% and 4%, respectively, at the national scale. In addition to improving mapping accuracy, the researchers were also able to discriminate between soybean and corn crops using reduced data inputs and thus allowing the resulting models to be more easily adapted to operational settings. The data also help farmers and scientists to monitor the growth changes of crops as well as generate large area crop cover maps.

In recent years, particularly with the expected impacts of global warming, the use of biofuels has also become an important topic regarding crop production.

“The goal is to test a standard, robust procedure in improving the monitoring of key cash crops globally, particularly in the light of changing energy production priorities,” said author JiyulChang. “The primary limitation to doing so is not the MODIS inputs or tree algorithm, but the presence of high-quality training information for model calibration.”

The research was funded by the NASA Applied Science Program and USDA Foreign Agriculture Service via the Global Agriculture Monitoring (GLAM) project. The detailed results from this study are published in the November-December 2007 issue of the Agronomy Journal.