Abstract

The capabilities of Sentinel-2 optical multispectral satellite data for modeling nitrogen (N) and carbon (C) contents, their ratio (C : N), and stocks in the litter horizons of forest soils were assessed. The study was conducted in the Bryansk Forest Nature Reserve and its buffer zone. The organic horizon samples were taken on 33 plots selected with due account for the tree species diversity of the reserve’s forests. Two layers of the organic horizon—L and FH—were sampled separately. The main variables for geospatial modeling were derived from a time series of eight Sentinel-2 multi-seasonal satellite images. Basic terrain characteristics and pixel coordinates were also added to variables’ stack. We used random forest to build regression models and the corresponding standard methods to assess their performance. The best results were obtained for the C : N ratio: the coefficient of determination R² = 0.71 with a scaled root-mean-square error RMSE = 12.5% in the L layer, and R² = 0.83 with RMSE = 10.6% in the FH layer. For other models, the values of R² ranged from 0.23 to 0.61, and the RMSE ranged from 15.8 to 48.6% with the least reliable results for the N and C stocks. Satellite-based variables were most informative for the contents of N and C, and, notably, for the C : N ratio. The most significant periods in the time series were early spring, summer, and snowy winter. To conclude, Sentinel-2 satellite imagery can be successfully used for estimation and mapping the contents and stocks of N and C in the forest soil organic horizon as a free and relevant alternative to thematic data on the species composition and related properties of stands.

中文翻译：

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基于Sentinel-2多季节卫星图像的森林凋落物层中氮，碳含量和储量的地理空间模拟

摘要

评估了Sentinel-2光学多光谱卫星数据建模土壤土壤中氮（N）和碳（C）含量，它们的比率（C：N）和库的能力。这项研究是在Bryansk森林自然保护区及其缓冲区进行的。有机层地平线样品是在33个样地上采集的，这些样地已适当考虑了保护区森林的树种多样性。有机层的两个层次-L和FH-分别进行了采样。地理空间建​​模的主要变量来自八个Sentinel-2多季节卫星图像的时间序列。基本地形特征和像素坐标也已添加到变量的堆栈中。我们使用随机森林来构建回归模型，并使用相应的标准方法来评估其性能。在L层中，R ² = 0.71，具有均方根误差缩放比例，RMSE = 12.5％，在FH层中，R ² = 0.83，RMSE = 10.6％。对于其他型号，R ²的值范围从0.23到0.61，RMSE范围从15.8到48.6％，其中N和C股票的结果最不可靠。基于卫星的变量对于N和C的含量最为有用，尤其是对于C：N的比率。时间序列中最重要的时期是早春，夏季和下雪的冬天。总而言之，Sentinel-2卫星图像可以成功地用于估算和测绘森林土壤有机层中氮和碳的含量和储量，作为有关林分物种组成和相关特性的专题数据的免费且相关的替代方法。