Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions

• Published in: European journal of agronomy 2024-11, Vol.161, p.127388, Article 127388
• Main Authors: Fan, Yiguang, Feng, Haikuan, Liu, Yang, Feng, Hao, Yue, Jibo, Jin, Xiuliang, Chen, Riqiang, Bian, Mingbo, Ma, Yanpeng, Yang, Guijun
• Format: Article
• Language: English
• Subjects: Environmental factors; Hierarchical linear modeling; Nitrogen; Potato; Proximal reflectance
• ISSN: 1161-0301
• DOI: 10.1016/j.eja.2024.127388

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 R2, 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. [Display omitted] •A two-tier HLM was used to couple VIs with environmental variables.•HLM can automatically adjusts VIs responses to PNC based on environmental variables.•Potato PNC was accurately predicted for different years and regions.