Using NDVI Time Series Curve Change Rate to Estimate Winter Wheat Yield

An algorithm uses the Normalized Difference Vegetation Index (NDVI) time series curve to compute NDVI change rate (CR) for every 8-day over a period (2008-2018) from Moderate-resolution Imaging Spectroradiometer (MODIS) data. The indices of CR variables within the winter wheat growing season were correlated with the end of the season winter wheat yield. A strong correlation (Pearson correlation coefficient = -0.48) was found in day of year 153-161 (filling) and the CR_153-161 was used to build a univariate regression model. Stepwise multiple linear regression was then used and then 16 CR variables were selected to construct the multiple regression model. The two models are good at predicting yield. The prediction results of two models were compared with official agricultural statistics showing that the RMSE = 578.21 kg ha^-1/457.38 kg ha^-1 and MRE=18.54%/14.56% Remote sensing of NDVI-CR, therefore, is a valuable tool for estimating winter wheat yield well in advance of harvest.