Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions

Crop growth models and remote sensing are useful tools for predicting crop
growth and yield, but each tool has inherent drawbacks when predicting
crop growth and yield at a regional scale. To improve the accuracy and
precision of regional corn yield predictions, a simple approach for
assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)
products into a crop growth model was developed, and regional yield
prediction performance was evaluated in a major corn-producing state,
Illinois, USA. Corn growth and yield were simulated for each grid using
the Crop Environment Resource Synthesis (CERES)-Maize model with minimum
inputs comprising planting date, fertilizer amount, genetic coefficients,
soil, and weather data. Planting date was estimated using a phenology
model with a leaf area duration (LAD)-logistic function that describes the
seasonal evolution of MODIS-derived leaf area index (LAI). Genetic
coefficients of the corn cultivar were determined to be the genetic
coefficients of the maturity group [included in Decision Support System
for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum
difference between the maximum LAI derived from the LAD-logistic function
and that simulated by the CERES-Maize model. In addition, the daily water
stress factors were estimated from the ratio between daily leaf
area/weight growth rates estimated from the LAD-logistic function and that
simulated by the CERES-Maize model under the rain-fed and auto-irrigation
conditions. The additional assimilation of MODIS data-derived water stress
factors and LAI under the auto-irrigation condition showed the highest
prediction accuracy and precision for the yearly corn yield prediction (R2
is 0.78 and the root mean square error is 0.75 t ha-1). The present
strategy for assimilating MODIS data into a crop growth model using
minimum inputs was successful for predicting regional yields, and it
should be examined for spatial portability to diverse agro-climatic and
agro-technology regions.