MODIS-based corn grain yield estimation model incorporating crop phenology information

Toshihiro Sakamoto, Anatoly A. Gitelson, Timothy J. Arkebauer

Research output: Contribution to journalArticlepeer-review

191 Scopus citations


A crop yield estimation model using time-series MODIS WDRVI was developed. The main feature of the proposed model is the incorporation of crop phenology detection using MODIS data, called the "Shape-Model Fitting Method". MODIS WDRVI taken 7-10. days before the corn silking stage had strong linear correlation with corn final grain yield at both field and regional scales. The model revealed spatial patterns of corn final grain yield all over the U.S. from 2000 to 2011. State-level corn yield was estimated accurately with coefficient of variation below 10% especially for the 18 major corn producing states including Iowa, Illinois, Delaware, Minnesota, Ohio, West Virginia, Wisconsin, Michigan, Indiana, Nebraska, Kentucky, New York, South Dakota, Missouri, Pennsylvania, Tennessee, New Jersey and Maryland. The results corresponded well with the spatial pattern of high-yield regions derived from the USDA/NASS data. However, the model tended to underestimate corn grain yield in three irrigated regions: the Midwestern region depending on the Ogallala Aquifer, the downstream basin of the Mississippi, and the southwestern region of Georgia. In contrast, it tended to overestimate corn grain yield around the outlying regions of the U.S. Corn Belt, specifically, the East Coast, North Dakota, Minnesota, Wisconsin, and Missouri. The estimation accuracy of the proposed model differed depending on the region. However, the annual variation of state level corn grain yield could be detected with high accuracy, especially in the major corn producing states.

Original languageEnglish (US)
Pages (from-to)215-231
Number of pages17
JournalRemote Sensing of Environment
StatePublished - Apr 15 2013
Externally publishedYes


  • Crop phenology
  • Green LAI
  • Shape-model fitting
  • U.S. Corn Belt

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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