VNIR公司
土壤碳
土壤科学
环境科学
土壤水分
均方误差
表土
土工试验
偏最小二乘回归
氮气
碳纤维
总有机碳
环境化学
化学
遥感
数学
统计
高光谱成像
地质学
算法
复合数
有机化学
作者
Qinghu Jiang,Qianxi Li,Xinggang Wang,Yu Wu,Xiaolu Yang,Feng Liu
出处
期刊:Geoderma
[Elsevier]
日期:2017-05-01
卷期号:293: 54-63
被引量:61
标识
DOI:10.1016/j.geoderma.2017.01.030
摘要
Soil organic carbon (SOC) and total nitrogen (TN) play major roles in soil quality and the global carbon budget. They can be measured rapidly and cost-effectively via visible and near-infrared reflectance (VNIR) spectroscopy. However, the reliability of this method is questionable because of the effects of heterogeneity. At present, only a few publications have addressed the effect of soil layers on model applicability, especially for highly heterogeneous soils in forest ecosystems. In the current work, we evaluated the performance of VNIR spectroscopy in estimating SOC and TN contents of soils collected from a mixed mountain forest in Central China. We also investigated the applicability of spectroscopic models between soil layers. We then further explored the possibility of using spiking with extra-weighting to improve model applicability. To achieve such objectives, we evaluated the applicability accuracy of the initial models (global and layered models) and modified models (spiked models with and without extra-weighting). Results showed that all the initial models successfully predicted SOC and TN. That is, for SOC, R2P ranged from 0.79 to 0.90, ratio of performance to inter-quartile range (RPIQ) ranged from 3.07 to 3.97, and the root mean square error (RMSEP) ranged from 0.54% to 0.88%; for TN, R2P ranged from 0.66 to 0.86, RPIQ ranged from 2.12 to 3.78, and RMSEP ranged from 0.05% to 0.08%. However, the prediction accuracies were seriously reduced when the model constructed from the top soil layer was used to predict the sub-surface soil properties, and vice versa. In terms of model applicability, our results demonstrated that spiking improved the applicability of the initial calibrations (RMSEP and absolute prediction bias were obviously reduced) and that the accuracy was further improved when the spiking subset was extra-weighted. When the extra-weighting reached a certain level, the accuracies remained stable or slightly reduced. Our results illustrated that spiking alone and spiking with extra-weighing are effective approaches to improve model applicability in the VNIR estimation of SOC and TN between different soil layers in a highly heterogeneous forest. This approach is potentially useful in rapidly quantifying and monitoring soil carbon and nitrogen pools in heterogeneous landscapes.
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