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Improving water status prediction of winter wheat using multi-source data with machine learning

文献类型： 外文期刊

作者： Shi, Bo 1 ; Yuan, Yifan 1 ; Zhuang, Tingxuan 1 ; Xu, Xuan 1 ; Schmidhalter, Urs 2 ; Ata-UI-Karim, Syed Tahir 3 ; Zhao, Ben 4 ; Liu, Xiaojun 1 ; Tian, Yongchao 1 ; Zhu, Yan 1 ; Cao, Weixing 1 ; Cao, Qiang 1 ;

作者机构： 1.Nanjing Agr Univ, Natl Engn & Technol Ctr Informat Agr, MOE Engn & Res Ctr Smart Agr, Collaborat Innovat Ctr Modern Crop Prod Cosponsore, Nanjing 210095, Peoples R China

2.Tech Univ Munich, Dept Plant Sci, Freising Weihenstephan, Germany

3.Univ Tokyo, Grad Sch Agr & Life Sci, Bunkyo, Tokyo 1138657, Japan

4.Chinese Acad Agr Sci, Farmland Irrigat Res Inst, Key Lab Crop Water Use & Regulat, Minist Agr, Xinxiang 453099, Peoples R China

关键词： Canopy water content; Plant water content; Canopy equivalent water thickness; Hyperspectral; Data fusion; Precision water management

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.722； 五年影响因子：6.384 ）

ISSN： 1161-0301

年卷期： 2022 年 139 卷

页码：

收录情况： SCI

摘要： Water and nitrogen (N) are the most important factors limiting crop productivity. Effective monitoring of the water status of winter wheat under different N treatments is imperative for precision irrigation in improved crop management. Hyperspectral remote sensing is widely used for monitoring the crop water status. However, changes in canopy architecture during ontogeny lead to poor inversion of crop properties and limit the use of spectral indices. This study aimed to improve the water status prediction of winter wheat using multi-source data with machine learning (ML). Two multi-irrigation levels (0, 120, 240, 360 mm) and N rates (75, 225 kg N ha-1) experiments were conducted during the 2019-2021 wheat growing seasons under field conditions using a rainout shelter. Hyperspectral, soil, plant, and climate data were evaluated with two feature selection methods. Selected results were chosen as input variables for prediction models by using three ML algorithms. By constructing the normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI), the DSI(2015, 2375), NDSI(2175, 2245), and RSI(720, 1200) showed the strongest correlation with canopy water content (CWC), plant water content (PWC), and canopy equivalent water thickness (CEWT), respectively. The best feature selection method and data were delivered by the Pearson correlation coefficient together with the soil, plant, and climate data. The best performing ML algorithm for CWC and PWC prediction was RF, while SVM was the best ML algorithm for CEWT prediction. The R2 of the optimal models ranged from 0.86 to 0.97. These models with multi-source data significantly improved the prediction accuracy of the water status and can thus assist in precision irrigation management of winter wheat.

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