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

a r t i c l e i n f o 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, 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. 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. The United States (U.S.) is the world's largest grain exporter. The total amount of corn exported in 2010/2011 was 45 million tons, which accounted for 49.2% of the world corn trade for that period (USDA/FAS, 2012). Thus, estimating grain yield over large areas as early as possible in the season is essential for not only the U.S. corn producers but also decision makers of food importing countries. Various techniques based on remotely sensed data have been employed for assessment of crop yield (Bauer, 1975; Nellis et al., 2009). Idso et al. (1977) used infrared thermometers to observe leaf temperature during the reproductive stage and then developed the " stress degree day (SDD) " concept for predicting wheat grain yield. Since remote sensing based crop phenology detection was an integral part of the SDD approach, it was suggested that the seasonal profile of albedo (Idso et al., 1977) and the Simple Ratio (Hatfield, 1983) could be used for defining the reproductive stage. A wide variety of remote sensing based indicators were used in a simple regression between vegetation indices (VIs) and yield. Kogan (1997) developed the Vegetation …