Abstract Canopy nitrogen (N) influences carbon (C) uptake by vegetation through its important role in photosynthetic enzymes. Global Vegetation Models (GVMs) predict C assimilation, but are limited by a lack spatial canopy N input. Mapping canopy N has been done in various ecosystems using remote sensing (RS) products, but has rarely considered environmental variables as additional predictors. Our research objective was to estimate spatial patterns of canopy N in European forests and to investigate the degree to which including environmental variables among the predictors would improve the models compared to using remotely sensed products alone. The environmental variables included were climate, soil properties, altitude, N deposition and land cover, while the remote sensing products were vegetation indices and NIR reflectance from MODIS and MERIS sensors, the MOD13Q1 and MTCI products, respectively. The results showed that canopy N could be estimated both within and among forest types using the random forests technique and calibration data from ICP Forests with good accuracy (r2 = 0.62, RRMSE = 0.18). The predicted spatial pattern shows higher canopy N in mid-western Europe and relatively lower values in both southern and northern Europe. For all subgroups tested (All plots, Evergreen Needleleaf Forest (ENF) plots and Deciduous Broadleaf Forest (DBF) plots), including environmental variables improved the predictions. Including environmental variables was especially important for the DBF plots, as the prediction model based on remotely sensed data products predicted canopy N with the lowest accuracy.

中文翻译：

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使用随机森林方法使用遥感和环境变量绘制欧洲森林中的冠层氮

摘要 冠层氮 (N) 通过其在光合酶中的重要作用影响植被对碳 (C) 的吸收。全球植被模型 (GVM) 预测 C 同化，但受到缺乏空间冠层 N 输入的限制。已经使用遥感 (RS) 产品在各种生态系统中绘制了冠层 N 的地图，但很少将环境变量视为额外的预测因子。我们的研究目标是估计欧洲森林中冠层 N 的空间模式，并调查与单独使用遥感产品相比，在预测变量中包含环境变量在多大程度上可以改进模型。包括的环境变量包括气候、土壤特性、海拔高度、氮沉降和土地覆盖、而遥感产品分别是 MODIS 和 MERIS 传感器、MOD13Q1 和 MTCI 产品的植被指数和 NIR 反射率。结果表明，可以使用随机森林技术和来自 ICP 森林的校准数据以良好的精度（r2 = 0.62，RRMSE = 0.18）估计森林类型内部和之间的冠层 N。预测的空间格局显示，中西欧的冠层 N 值较高，而南欧和北欧的值相对较低。对于所有测试的子组（所有地块、常绿针叶林 (ENF) 地块和落叶阔叶林 (DBF) 地块），包括环境变量都改善了预测。包括环境变量对于 DBF 图尤其重要，