Coupled maize model: A 4D maize growth model based on growing degree days

文献类型： 外文期刊

第一作者： Qian, Binxiang

作者： Qian, Binxiang;Huang, Wenjiang;Ye, Huichun;Guo, Anting;Pan, Yuhao;Jin, Yin;Jiao, Quanjun;Zhang, Biyao;Qian, Binxiang;Huang, Wenjiang;Ye, Huichun;Guo, Anting;Pan, Yuhao;Jin, Yin;Jiao, Quanjun;Qian, Binxiang;Huang, Wenjiang;Ye, Huichun;Guo, Anting;Pan, Yuhao;Jin, Yin;Jiao, Quanjun;Huang, Wenjiang;Ye, Huichun;Guo, Anting;Xie, Donghui;Xie, Qiaoyun;Ruan, Chao;Xu, Tianjun;Zhang, Yong;Nie, Tiange

作者机构： Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China;Univ Chinese Acad Sci, Beijing 100049, Peoples R China;Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Earth Observat Hainan, Sanya 572029, Peoples R China;Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China;Univ Western Australia, Sch Engn, Perth, WA 6009, Australia;Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applica, Hefei, Peoples R China;Beijing Acad Agr & Forestry Sci, Maize Res Inst, Beijing, Peoples R China

关键词： Remote sensing; Maize growth model; 4D maize growth model; Maize canopy parameters; Growing degree days (GDDs); Coupled maize model

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 2022影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2023 年 212 卷

页码：

收录情况： SCI

摘要： Crop canopy parameters are critical for environmental remote sensing, describing crop phenotypes, and ensuring food security. Evaluating the effect of temperature on crop growth is crucial for estimating crop canopy pa-rameters. However, existing crop growth and plant functional-structural models cannot simultaneously model temperature responses, perform accurate dynamic simulations, and provide multi-scale computer visualizations. This limitation has hindered the application of structural models of maize plants for use in 3D radiative transfer models, crop structure evaluations, and crop phenotype descriptions. We improve the leaf/organ-level thermal-driven crop growth model (MAIZSIM) and the plant functional-structural algorithm. To address these limitations, we propose the coupled maize model, a four-dimensional (4D) growth model based on growing degree days. This model can simulate and visualize the structural parameters of the maize canopy at the organ, plant, seasonal, and population levels. The model outputs three-dimensional (3D) predictions of the maize structure (file format.obj), enabling editing and 3D visualizations. We use maize datasets from multiple phenological periods to test the proposed model's accuracy and stability in simulating the canopy parameters at multiple levels. The results show that the normalized root mean square errors (NRMSEs) between the simulated and measured maize leaf size, area, leaf node height, and vein curve derived from the coupled maize model are below 0.1, demonstrating the model's high accuracy.

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