Forecasting corn yield with imaging spectroscopy

Abstract

Corn is the most widely produced grain in the United States with 87 million acres planted in 2009 accounting for more than 90 percent of total value and production of feed grains. Half of United States' corn production is used in livestock feed with the remainder processed into a multitude of food and industrial products including starch, sweeteners, corn oil, beverage and industrial alcohol, and fuel ethanol. With increased focus on renewable energy, an unusual link between corn and oil commodities has been created increasing the demand for the grain in ethanol production. As a result, monitoring crop performance is vital for yield forecasting and developing timely remediation strategies to optimize crop performance. Several factors including water availability, nitrogen (N) supply, soil organic mater, disease, and supply of other nutrients, have a significant impact on crop growth and grain yields. Imaging spectroscopy can provide timely, spatially explicit information for managing agricultural ecosystems. The HyspIRI mission called for by the NRC Decadal Survey identifies the need for a near term space-borne hyperspectral imaging spectrometer to globally map early signs of ecosystem change through altered physiology. The primary instrument on the proposed NASA HyspIRI mission is a hyperspectral (10 nm FWHM) mapper with a 60 m ground resolution and a 19 day global revisit, which will enable imaging spectroscopy with high temporal repeat to capture the impact of environmental perturbations on ecosystem productivity. Recent advances in airborne hyperspectral imaging systems [i.e., AVIRIS, AISA EAGLE & Hawk (Specim, Oulu, Finland)] along with Earth Observing One (EO-1) Hyperion satellite data have made it possible to obtain high resolution spatial and full range visible (VIS) to short wave infrared (SWIR) spectral information that can be further employed to explore vegetation productivity and change in both agricultural and surrounding ecosystems to further define algorithms and products applicable to the HyspIRI mission. From hyperspectral data, numerous statistical and spectroscopic approaches have been developed that use features in vegetation spectral curves to gain insight to biophysical parameters, including: biomass, pigments, tissue water content, and the amount of lignin, cellulose, and foliar N. In the case of optically dense vegetation, the spectral derivative has been shown to be indicative of the abundance and activity of the absorbers in the leaves. Further, linear unmixing and spectral angle matching techniques take advantage of the high dimensionality of hyperspectral data and can be used alone or in conjunction with other vegetation indices for ecosystem assessment. Here we will further investigate these spectroscopic techniques to enhance corn yield forecasting capabilities.