A Nonlinear Hierarchical Model For Forecasting Crop Growth In The Us Corn Belt

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite has recently been shown to measure variables containing information relevant to agronomists. SMOS was initially intended to monitor the water content of soil. However, a combination of SMOS's antenna technology and data processing algorithms make it possible to estimate the mass of water contained in vegetation tissue. Recent work by Hornbuckle et al., as well as Lawrence et al., suggest tau roughly mirrors the growth and senescence of crops [1, 2].In this paper we analyze SMOS data from an intensively cultivated agricultural region in the Midwest to provide new information about crop phenology. In addition to modeling the seasonal pattern of crop growth, we estimate the day of the year when tau reaches its peak. Because SMOS has a fine temporal resolution, the ability to model tau during a growing season could be useful to understanding changes in crop development, climate conditions, as well as forecasting future growth cycles.