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

• Published in: Remote sensing of environment Vol. 131; pp. 215 - 231
• Main Authors: Sakamoto, Toshihiro, Gitelson, Anatoly A., Arkebauer, Timothy J.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.04.2013; Elsevier
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Crop phenology; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Green LAI; Internal geophysics; MODIS; Shape-model fitting; Teledetection and vegetation maps; U.S. Corn Belt; New York; Indiana; United States; Kentucky; South Dakota; Minnesota; Delaware; Michigan; Nebraska; West Virginia; Iowa; Illinois; Missouri; Ohio; Wisconsin; U.S. Corn Belt; Crop phenology; Green LAI; MODIS; Shape-model fitting; time series analysis; Monocotyledones; Vegetation index; North America; Modeling; Field; Gramineae; Angiospermae; spatial variations; Cultivated plant; Variation coefficient; detection; Zea mays; Fitting; Space remote sensing; Corn; correlation; Phenology; Spermatophyta; Yield; methodology
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2012.12.017

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 7–10days 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. ► A corn yield estimation model was developed by incorporating a crop phenology detection method. ► Model calibration and validation were conducted on both field and regional scales. ► WDRVI taken 7–10days before silking stage accurately estimated corn grain yield. ► The model revealed spatial patterns of corn grain yield all over the U.S. for over a decade.