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.

中文翻译：

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从遥感数据和环境变量预测马铃薯植物氮含量的模型在不同年份和地区的可转移性


使用遥感技术监测作物的氮养分状况正逐渐成为一种更明智的选择，因为传统方法耗时、劳动密集且具有破坏性。然而，大多数利用遥感数据的预测模型是统计的，而不是机械的，这使得它们难以在年际和区域尺度上扩展。本研究探讨了马铃薯植物氮含量 （PNC） 预测模型的年际和区域可转移性，该模型将环境变量 （EV，例如温度、降水等） 与近端高光谱植被指数 （VI） 相结合。实施了两种方法来融合 EV 和 VI。第一个涉及利用多变量线性模型和随机森林方法的多元回归分析，分别将 VI 和 EV 视为自变量。第二个是分层线性模型 （HLM），使用 EV 来动态调整不同实验地点的 VI 和 PNC 之间的关系。预测结果表明：（i） 仅依赖光学 VI 的传统方法在年际和区域 PNC 预报中表现出有限的准确性和稳定性;（ii） 尽管多元回归方法显著提高了校准集内的模型准确性，但其跨年份和跨地区的可扩展性仍然不理想;（iii） HLM 方法在不同年份和地区表现出高精度和可扩展性，验证集中的 R2、RMSE 和 NRMSE 值分别为 0.68、0.50% 和 19.68%。这些发现证实，使用两层 HLM 方法可以自动调整 VIs 通过 EV 对 PNC 的响应差异，从而提高模型的稳定性。 如果在马铃薯生长周期中可持续地获取遥感数据和 EV，它将为马铃薯氮诊断提供一种特别有前途的方法，作为区域施用氮肥的决策工具。