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

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 t...

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Autores principales:	Fa Zhao, Guijun Yang, Hao Yang, Yaohui Zhu, Yang Meng, Shaoyu Han, Xinlei Bu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	normalized difference vegetation index (NDVI) prediction dynamic time warping (DTW) LSTM MODIS Science Q
Acceso en línea:	https://doaj.org/article/81aae3657daa43ed92761ff20d95cedb
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spelling	oai:doaj.org-article:81aae3657daa43ed92761ff20d95cedb2021-11-25T18:55:11ZShort and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data10.3390/rs132246602072-4292https://doaj.org/article/81aae3657daa43ed92761ff20d95cedb2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4660https://doaj.org/toc/2072-4292The 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.Fa ZhaoGuijun YangHao YangYaohui ZhuYang MengShaoyu HanXinlei BuMDPI AGarticlenormalized difference vegetation index (NDVI)predictiondynamic time warping (DTW)LSTMMODISScienceQENRemote Sensing, Vol 13, Iss 4660, p 4660 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	normalized difference vegetation index (NDVI) prediction dynamic time warping (DTW) LSTM MODIS Science Q
spellingShingle	normalized difference vegetation index (NDVI) prediction dynamic time warping (DTW) LSTM MODIS Science Q Fa Zhao Guijun Yang Hao Yang Yaohui Zhu Yang Meng Shaoyu Han Xinlei Bu Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data
description	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.
format	article
author	Fa Zhao Guijun Yang Hao Yang Yaohui Zhu Yang Meng Shaoyu Han Xinlei Bu
author_facet	Fa Zhao Guijun Yang Hao Yang Yaohui Zhu Yang Meng Shaoyu Han Xinlei Bu
author_sort	Fa Zhao
title	Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data
title_short	Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data
title_full	Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data
title_fullStr	Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data
title_full_unstemmed	Short and Medium-Term Prediction of Winter Wheat NDVI Based on the DTW–LSTM Combination Method and MODIS Time Series Data
title_sort	short and medium-term prediction of winter wheat ndvi based on the dtw–lstm combination method and modis time series data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/81aae3657daa43ed92761ff20d95cedb
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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

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