基于长短期记忆网络的冬小麦连续时序叶面积指数预测

doi:10.3964/j.issn.1000-0593(2020)03-0898-07

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摘要：连续时序的叶面积指数（LAI）可反映冬小麦长势的变化情况，预测冬小麦未来时段的LAI对指导田间管理决策具有重要作用。以WOFOST（World Food Studies）为代表的作物生长模型可通过模拟冬小麦的生长发育过程对未来LAI曲线进行预测，但其预测过程依赖于未来的气象数据等难以获取的输入参数。由于冬小麦的LAI时序变化具有连续性和规律性的特点，可通过深度学习方法仅以历史LAI为输入参数对未来LAI进行预测，但深度学习方法需要大量样本参与训练，训练样本的稀缺性限制了其在LAI预测方面的实际应用。针对上述问题，通过数据同化方法将遥感数据与WOFOST模型相结合，采用SCE（Shuffled Complex Evolution）算法最小化校正后的MODIS LAI产品与模型模拟LAI之间差值来优化作物模型初始参数，从而构建出京津冀地区15年的逐日冬小麦LAI数据集。在该数据集基础上，利用长短期记忆网络（LSTM）分别建立了不同输入历史LAI天数的多个冬小麦预测模型，探究了不同预测模型表达冬小麦生育期中LAI变化规律的能力。结果表明：基于LSTM网络的预测模型都能较好进行冬小麦LAI未来曲线变化的预测，其中当模型输入LAI长度为20时，预测冬小麦从返青到成熟阶段的LAI精度最高，其决定系数（R²）、均方根误差（RMSE）值分别为0.986 5和0.183 6。对于冬小麦生长各个阶段，预测模型对于返青至开花期的预测精度高，开花至成熟期的预测精度稍有降低。总体而言，构建训练数据集的方法对于深度学习方法在相似问题中的应用具有借鉴意义，建立的预测模型验证了LSTM网络对于冬小麦时序LAI曲线具有较好的预测能力，为预测作物未来时序LAI提供了一种有效的方法。

关键词：叶面积指数；长短期记忆网络；WOFOST模型；数据同化；MODIS LAI遥感影像

Abstract：The continuous time series of Leaf Area Index (LAI) can reflect the growth of winter wheat, and the prediction of future LAI is important for guiding agricultural production. The crop growth models, such as the World Food Studies (WOFOST), can predict the future LAI by simulating the growth and development of winter wheat. But the simulation depends on numerous input parameters, such as future meteorological data, which is difficult to obtain. Due to the continuity and regularity of LAI variations of winter wheat, the future LAI can be predicted with historical LAI through deep learning methods. However, deep learning methods require a large number of samples with labels to build training dataset. The scarcity of training dataset limits the application of deep learning methods in practice. To solve the above problems, we used data assimilation framework to combine remote sensing data with WOFOST model and constructed 15-year time series dataset of winter wheat LAI in Hebei province. Shuffled Complex Evolution (SCE) algorithm was applied to minimize difference between corrected MODIS LAI and simulated LAI for optimizing initial parameters of WOFOST. Based on the dataset, multiple LAI prediction models with different input lengths of historical LAI were established by using the Long Short-Term Memory (LSTM). The abilities of different prediction models to delineate LAI variations of winter wheat were evaluated. Results showed that the LSTM-based models can predict the future LAI of winter wheat effectively. The prediction model with an input length of 20 days achieved the highest accuracy. and RMSE of the prediction model were 0.986 5 and 0.183 6 after winter wheat returned green. For different stages of winter wheat growth, the accuracy was higher before winter wheat bloomed and reduced slightly after winter wheat bloomed. Therefore, it could be concluded that the method of constructing training dataset proposed in this study could be a reference for the application of deep learning methods in similar problems. The prediction models built in this study also verified the effectiveness of the LSTM, which provided a helpful way for predicting the future LAI of crops.

Key words：Leaf area index; Long short-term memory; World food studies; Data assimilation; MODIS LAI remote sensing image

收稿日期: 2019-01-06 修订日期: 2019-04-21

中图分类号:

TP79

基金资助: 高分辨率对地观测系统重大专项（11-Y20A16-9001-17/18），国家自然科学基金项目（41771371），国家自然科学基金国际合作项目（61661136006）资助

通讯作者: 秦其明 E-mail: qmqinpku@163.com

作者简介: 龙泽昊，1994年生，北京大学遥感与地理信息系统研究所硕士研究生 e-mail：longzehao@pku.edu.cn

引用本文:

龙泽昊，秦其明，张添源，许伟. 基于长短期记忆网络的冬小麦连续时序叶面积指数预测[J]. 光谱学与光谱分析, 2020, 40(03): 898-904.
LONG Ze-hao, QIN Qi-ming, ZHANG Tian-yuan, XU Wei. Prediction of Continuous Time Series Leaf Area Index Based on Long Short-Term Memory Network: a Case Study of Winter Wheat. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 898-904.

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