Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions
文献类型： 外文期刊
作者： Fan, Yiguang 1 ; Feng, Haikuan 1 ; Liu, Yang 1 ; Feng, Hao 5 ; Yue, Jibo 6 ; Jin, Xiuliang 7 ; Chen, Riqiang 1 ; Bian, Mingbo 1 ; Ma, Yanpeng 1 ; Yang, Guijun 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
2.Northwest A&F Univ, Coll Informat Engn, Xianyang 712100, Shaanxi, Peoples R China
3.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Jiangsu, Peoples R China
4.China Agr Univ, Key Lab Agr Informat Acquisit Technol, Minist Agr & Rural Affairs, Beijing 100083, Peoples R China
5.Beijing Wuzi Univ, Beijing 101126, Peoples R China
6.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou 450002, Peoples R China
7.Chinese Acad Agr Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Inst Crop Sci, Beijing 100081, Peoples R China
关键词： Potato; Hierarchical linear modeling; Environmental factors; Proximal reflectance; Nitrogen
期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.5； 五年影响因子：5.9 ）
ISSN： 1161-0301
年卷期： 2024 年 161 卷
页码：
收录情况： SCI
摘要： The use of remote sensing technologies to monitor the nitrogen nutrient status of crops is gradually becoming a more sensible choice, as traditional methods are time-consuming, labor-intensive, and destructive. However, most predictive models utilizing remote sensing data are statistical rather than mechanistic, making them difficult to extend at interannual and regional scales. This study explored the interannual and regional transferability of the potato plant nitrogen content (PNC) prediction models, which combined environmental variables (EVs, e.g. temperature, precipitation, etc.) with proximal hyperspectral vegetation indices (VIs). Two methodologies were implemented to fuse EVs and VIs. The first involved a multiple regression analysis utilizing a multivariate linear model and a random forest approach, with VIs and EVs treated as independent variables, respectively. The second, a hierarchical linear model (HLM), employed EVs to dynamically adjust the relationship between VIs and PNC for different experimental sites. The predictive outcomes demonstrated that (i) the conventional method relying solely on optical VIs exhibited limited accuracy and stability in interannual and regional PNC forecasting; (ii) albeit the multivariate regression approach significantly enhanced model accuracy within the calibration set, its scalability across years and regions remained suboptimal; (iii) the HLM method exhibited high precision and scalability across years and regions, with R 2 , RMSE, and NRMSE values of 0.68, 0.50 %, and 19.68 % in the validation set, respectively. Those findings corroborate that using a two-tier HLM method can automatically adjust for discrepancies in VIs response to PNC through EVs, thereby enhancing the model's stability. Provided that remote sensing data and EVs are sustainably acquired over the potato growth cycle, it will provide a particularly promising approach to potato nitrogen diagnostics as a decision-making tool for regional application of nitrogen fertilizer.
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