Abstract. Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. However, the spatial-temporal monitoring of soil organic carbon (SOC) requires more efficient data acquisition. The use of soil Vis-NIR spectroscopy is a promising research field in this context. However, the interpretation of the recorded spectral signal with regards to SOC is not trivial due to the complexity of the soil matrix, and factors affecting the measurements under field conditions. A model-building process is required to relate the spectral signal to the SOC content. For this study, spectral on-the-go proximal measurements and soil sampling were conducted on a long-term field experiment (LTE) located in the state of Saxony-Anhalt, Germany. SOC values ranged between 14–25 g kg⁻¹ due to different fertilization treatments. Partial least squares regression (PLSR) models were built on behalf of spectral laboratory and field measurements conducted with two spectrometers and preprocessed by various methods. A data correction of the field data was done with three different approaches: linear transformation, piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). The models were then thoroughly interpreted with regards to spectral wavelength importance using regression coefficients (RC) and variable importance in projection scores (VIP). The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of spectrometers with a differing spectral resolution for soil Vis-NIR measurements under varying soil conditions revealed shifts in wavelength importance. Still, some wavelengths related to SOC were extracted (560 nm, 1330 nm, 1400 nm, 1720 nm, and 1900 nm) by various preprocessing methods and were highly important in models trained on both, laboratory, and field measurements. Furthermore, we showed, that the correction of spectral field data with spectral laboratory measurements improved the predictive performance of the models built on behalf of the proximal on-the-go sensing measurements.

中文翻译：

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土壤光谱能否有助于农业土壤的土壤有机碳监测？

摘要。用于农业用途的土壤中的碳固存有助于减缓气候变化。然而，土壤有机碳（SOC）的时空监测需要更有效的数据采集。在这种情况下，土壤可见近红外光谱的使用是一个很有前途的研究领域。然而，由于土壤基质的复杂性以及影响现场条件下测量的因素，对记录的关于 SOC 的光谱信号的解释并非易事。需要一个模型构建过程来将频谱信号与 SOC 内容相关联。在这项研究中，在德国萨克森-安哈尔特州的长期现场实验 (LTE) 上进行了光谱移动近端测量和土壤采样。SOC 值介于 14–25 g kg ^-1之间由于施肥处理不同。偏最小二乘回归 (PLSR) 模型是代表光谱实验室建立的，并使用两个光谱仪进行现场测量，并通过各种方法进行预处理。使用三种不同的方法对现场数据进行数据校正：线性变换、分段直接标准化 (PDS) 和外部参数正交化 (EPO)。然后使用回归系数 (RC) 和投影分数中的变量重要性 (VIP) 对模型的光谱波长重要性进行彻底解释。详细的波长重要性分析揭示了使用土壤光谱进行 SOC 监测的挑战。在不同土壤条件下使用具有不同光谱分辨率的光谱仪进行土壤 Vis-NIR 测量揭示了波长重要性的变化。尽管如此，通过各种预处理方法提取了一些与 SOC 相关的波长（560 nm、1330 nm、1400 nm、1720 nm 和 1900 nm），并且在实验室和现场测量训练的模型中非常重要。此外，我们表明，使用光谱实验室测量对光谱场数据进行校正可以提高代表近端移动传感测量而构建的模型的预测性能。