土壤是陆地碳循环的中枢, 充分发挥土壤固碳潜力有助于减缓全球气候变化。 土壤有机碳 (SOC) 的高度分异性同时体现在空间和垂直分布上, 但是许多前期研究往往只考虑了空间分异, 而忽略了垂直分异。 尤其在青藏高原这种高寒山区, 土壤样品采集难度较大且费用昂贵。 可见近红外 (Vis-NIR) 光谱作为传统土壤实验室化学分析的辅助手段, 能够较为快速和精准地估测SOC含量。 但是土壤水分等环境因素会掩盖或改变SOC的Vis-NIR光谱吸收特征进而削弱模型预测精度。 外部参数正交化 (EPO) 和分段直接标准化 (PDS) 算法可以有效校正水分对光谱的影响, 但其在野外新鲜土柱上的表现还不得而知。 本研究旨在探索不同水分影响校正算法对野外剖面土壤光谱的校正能力, 对采自中国青藏高原海拔2 900～4 500 m色季拉山的共26个1 m深土柱。 沿深度以5 cm×5 cm为测量单元, 从各单元中心采集共计386个野外原状湿样Vis-NIR光谱, 并在实验室内测得相应386个研磨干样的Vis-NIR光谱以及SOC含量。 经EPO和PDS算法校正土壤水分对光谱的影响后, 通过随机森林建立土壤光谱和SOC含量的定量预测模型, 并使用靴襻法评估不同校正处理下预测模型的不确定。 土柱整体及垂直分布的精度结果表明, 经PDS法转换的农田和草地土柱湿样光谱均表现出良好的水分校正效果, 而EPO法仅对农田土柱有效。 水分影响校正算法在不同土壤深度上也存在显著差异, EPO和PDS对农田和草地表层样本的水分校正均效果明显。 两种校正方法的效果显示出地类和土层深度的依赖性。 本研究为利用Vis-NIR光谱技术在高寒山区野外快速准确估算土壤碳含量的垂直分异提供了必要参考。

The terrestrial carbon cycle is the most important constitution and plays a prominent role in global carbon cycle, and soil carbon sequestration makes an important contribution to the global climate change. The full soil profile is a highly dynamic component of the ecosystem, with pronounced depth-dependent processing of soil organic carbon (SOC), such that accumulations and losses of carbon above ground are on different temporal trajectories than changes below ground. But many studies do not consider the spatial variability of soil properties in the vertical direction, especially in the Qinghai-Tibet Plateau, mainly because of the difficulties and expense of collecting soil material in that kind of terrain and transporting it to the laboratory. Visible near-infrared reflectance spectroscopy (Vis-NIR) is an increasingly popular measurement method that is enabling the rapid, real-time and accurate proximal sensing of soil properties, including SOC. However, thesoil moisture content has been shown to affect soil spectra, might mask or alter the absorption features of SOC. EPO and PDS are two effective methods to correct soil spectra effects, but we still unknow the feasibility of those two methods on fresh profile samples. In this study, we compared EPO and PDS on a set of 26 soil cores (1 m depths and 5 cm diameter) in the Sygera Mountains on the Qinghai-Tibet Plateau, China. Spectra were acquired from fresh, vertical faces 5 cm×5 cm in the area from the centers of the cores to give 386 spectra in all. We also got the spectra and SOC contents from the 386 dry samples. The statistical models were built to predict of the SOC in the samples from the spectra by Random Forest. The bootstrap was used to assess the uncertainty of the predictions by the EPO and PDS. Our results show that PDS is an effective strategy to mitigate the effects of soil water content on vis-NIR spectra for the fresh soil core samples from arable and grassland. While EPO neither shown significantly out performed those wet core samples from grassland. There were somewhat differences along with the profile on prediction accuracy of SOC between EPO and PDS. Both EPO and PDS show significantly available on surface layers of samples from arable and grassland. The EPO and PDS illustrated the dependence of land use type and soil depth. Our work would be a benefit to therapid and accurate estimation of the vertical partitioning of SOC content in the field in alpine mountains using Vis-NIR spectroscopy.