数字农科院2.0

Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices

文献类型： 外文期刊

作者： Xu, Hao;Yin, Hongfei;Liu, Jia;Wang, Lei;Feng, Wenjie;Song, Hualu;Fan, Yangyang;Qi, Kangkang;Liang, Zhichao;Li, Wenjie;Zhang, Xiaohu;Zhang, Rongjuan;Wang, Shuai

作者机构：

关键词： crop yield;phenology;random forest;long short-term memory

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCIE（2025版）

摘要： In the context of climate change and the development of sustainable agricultural, crop yield prediction is key to ensuring food security. In this study, long-term vegetation and meteorological indices were obtained from the MOD09A1 product and daily weather data. Three types of time series data were constructed by aggregating data from an 8-day period (DP), 9-month period (MP), and six growth periods (GP). And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. Results showed that the average root mean squared error (RMSE) of the RF model in each province was 0.5 Mg/ha lower than that of the LSTM model. Both the RF and LSTM prediction accuracies increased with the later growth stages data. Partial dependence plots showed that the influence degree of DVI on yield was above 2 Mg/ha. When the time length of the feature variables was shortened to MP or GP, the growing degree days (GDD), average minimum temperature (AveTmin), and effective precipitation (EP) showed stronger nonlinear relationships with the statistical yields.

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