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Abstract
A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies.
Highlights
Global warming is projected to accompany more frequent extreme weather events, such as heavy rainfall, high temperature, and drought [1,2]
In addition to Vegetation indices (VIs) and crop growth variables, crop phenology information is required for reliable crop yield prediction because the effects of environmental conditions on crop yield differ by growth stage [16]
leaf area duration (LAD) has been reported to have positive correlation with corn yield under water and nitrogen stress conditions imposed at different growth stages [37] and under varying planting densities of three maize hybrids [38], and genetic differences in photosynthetic duration were reported to be associated with a longer grain filling duration and higher yield [39]. These findings suggest that LAD would have greater potential to represent corn yield variability in regions with diverse agroclimate and agrotechnologies compared to VIs and leaf area index (LAI) at a particular crop growth stage
Summary
Global warming is projected to accompany more frequent extreme weather events, such as heavy rainfall, high temperature, and drought [1,2]. These changes will positively affect crop production in some regions, crop production for food, feed, and fodder will be negatively affected in other regions [3,4], aggravating all dimensions of food security. Notable advances in remote sensing have enabled reliable and timely prediction of crop yields [8,9]. /or to estimate intermediate crop growth variables, such as LAI and biomass, for yield prediction [12,13,14,15]. Islam and Bala [17] used NDVI and LAI derived from remote sensing data to identify the planting and ending dates of potato, Jönsson and Eklundh [18]
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