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Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW-LSTM Combination Method and MODIS Time Series Data

文献类型： 外文期刊

作者： Zhao, Fa 1 ; Yang, Guijun 1 ; Yang, Hao 1 ; Zhu, Yaohui 1 ; Meng, Yang 1 ; Han, Shaoyu 1 ; Bu, Xinlei 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China

2.Anhui Univ, Sch Elect & Informat Engn, Hefei 230601, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

4.Anhui Polytech Univ, Sch Elect Engn, Wuhu 241000, Peoples R China

关键词： normalized difference vegetation index (NDVI); prediction; dynamic time warping (DTW); LSTM; MODIS

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2021 年 13 卷 22 期

页码：

收录情况： SCI

摘要： 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.

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