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Accurate determination of crop phenology is key to field management and decision making. The existing research on phenology based on remote sensing data is mainly phenology monitoring, which cannot realize the prediction of phenology. In this paper, we propose a method to predict the maturity date (MD) of winter wheat based on a combination of phenology monitoring method and accumulated temperature. The method is divided into three steps. First, 2-band Enhanced Vegetation Index (EVI2) time series data were generated using the moderate-resolution imaging spectroradiometer (MODIS) reflectance data at 8-day intervals; then, the time series were reconstructed using polynomial fitting and the heading date (HD) of winter wheat was extracted using the maximum method. Secondly, the average cumulative temperature required for winter wheat to go from HD to MD was calculated based on historical phenological data and meteorological data. Finally, the timing of winter wheat HD and the current year's Meteorological Data were combined to predict winter wheat MD. The method was used to predict the MD of winter wheat in Hebei in 2018 and was validated with data from the phenology station and the Modis Land Cover Dynamics (MCD12Q2) product. The results showed that the coefficient of determination (R2) for predicting MD using this method was 0.48 and 0.74, the root mean square error (RMSE) was 7.03 and 4.91 days, and Bias was 4.93 and −3.59 days, respectively. In summary, the method is capable of predicting winter wheat MD at the regional scale. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction. [ABSTRACT FROM AUTHOR]