Canopy nitrogen content (CNC) is a crucial variable for plant health, influencing photosynthesis and growth. An optimized, scalable approach for spatially explicit CNC quantification using Sentinel-2 (S2) data is presented, integrating PROSAIL-PRO simulations with Gaussian Process Regression (GPR) and an Active Learning technique, specifically the Euclidean distance-based diversity (EBD) approach for selective sampling. This hybrid method enhances training dataset efficiency and optimizes CNC models for practical applications. Two GPR models based on PROSAIL-PRO variables were evaluated: a protein-based model (Cprot-LAI) and a chlorophyll-based model (Cab-LAI). Both models, implemented in Google Earth Engine (GEE), demonstrated strong performance and outperformed other machine learning methods, including kernel ridge regression, principal component regression, neural network, weighted k-nearest neighbors regression, partial least squares regression and least squares linear regression. Validation results showed moderate to good accuracies: NRMSECprot−LAI = 16.76%, RCprot−LAI2 = 0.47; NRMSECab−LAI = 18.74%, RCab−LAI2 = 0.51. The models revealed high consistency for an independent validation dataset of the Munich-North-Isar (Germany) test site, with R2 values of 0.58 and 0.71 and NRMSEs of 21.47% and 20.17% for the Cprot-LAI model and Cab-LAI model, respectively. The models also demonstrated high consistency across growing seasons, indicating their potential for time series analysis of CNC dynamics. Application of the S2-based mapping workflow across the Iberian Peninsula, with estimates showing relative uncertainty below 30%, highlights the model’s broad applicability and portability. The optimized EBD-GPR-CNC approach within GEE supports scalable CNC estimation and offers a robust tool for monitoring nitrogen dynamics.

中文翻译：

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在 Google Earth Engine 中优化来自 Sentinel-2 的冠层氮映射的混合模型


冠层氮含量 （CNC） 是植物健康的关键变量，影响光合作用和生长。提出了一种使用 Sentinel-2 （S2） 数据进行空间显式 CNC 量化的优化、可扩展的方法，将 PROSAIL-PRO 仿真与高斯过程回归 （GPR） 和主动学习技术集成，特别是用于选择性采样的欧几里得基于距离的分集 （EBD） 方法。这种混合方法提高了训练数据集的效率，并优化了 CNC 模型的实际应用。评估了两种基于 PROSAIL-PRO 变量的 GPR 模型：基于蛋白质的模型 （Cprot-LAI） 和基于叶绿素的模型 （Cab-LAI）。在 Google Earth Engine （GEE） 中实现的这两个模型都表现出强大的性能，并且优于其他机器学习方法，包括内核岭回归、主成分回归、神经网络、加权 k 最近邻回归、偏最小二乘回归和最小二乘线性回归。验证结果显示中等到良好的准确性：NRMSECprot-LAI = 16.76%，RCprot-LAI2 = 0.47;NRMSECab-LAI = 18.74%，RCab-LAI2 = 0.51。模型显示慕尼黑-北伊萨尔（德国）测试场的独立验证数据集具有高度一致性，Cprot-LAI 模型和 Cab-LAI 模型的 R2 值为 0.58 和 0.71，NRMSE 分别为 21.47% 和 20.17%。这些模型还显示出跨生长季节的高度一致性，表明它们具有对 CNC 动力学进行时间序列分析的潜力。基于 S2 的制图工作流在整个伊比利亚半岛的应用，估计显示相对不确定性低于 30%，突出了该模型的广泛适用性和可移植性。 GEE 中优化的 EBD-GPR-CNC 方法支持可扩展的 CNC 估计，并为监测氮气动力学提供了强大的工具。