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Traditional method of visiting field and surveying farmers to estimate crop yield has been considered inefficient and impractical especially in cases when fields are not easily accessible. Remote sensing techniques, therefore, has been utilize to overcome these obstacles with good success. Normalize Difference Vegetation Index (NDVI) based models are considered to be most effective and utilized technique in crop yield assessment and can provide up to field level assessment. This research utilize MODIS based 250m daily NDVI to estimate corn yield in 4 Agricultural Statistics Districts (ASD) in Iowa state. Corn is considered the primary crop in the US accounting for 90% of the total feed grains, hence utilized to be study in this research. Linear regression model was derived between NDVI curve and corn yield using all counties within the 4 ASD between years 2000 to 2014. The regression model showed statistically significant relation between NDVI curve and corn yield with coefficient of deterministic (R-square) over 0.80 in all 4 ASD. Similarly validating the model using new 2015 yield, the average predictability error was between 5 to 7 percent.
Shrestha et al. (Fri,) studied this question.