Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally resolved data from the Swiss Soil Monitoring Network (NABO). Given that less than 2 % of the samples in the SSL originate from organic soils, we aimed to develop both an efficient calibration sampling scheme and accurate modeling strategy to estimate the soil carbon (SC) contents of heterogeneous samples between 0 and 2 m depth from 26 locations within two drained peatland regions (School of Agricultural, Forest and Food Sciences (HAFL) data set; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of the SSL.We used partial least square regressions (PLSRs), together with five repetitions of a location-grouped, 10-fold cross-validation, to predict SC ranging from 1 % to 52 % in the local HAFL data set. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL combined with local samples (2), commonly referred to as spiking, and subsets of local and SSL samples optimized for the peatland target sites using the resampling local (RS-LOCAL) algorithm (3). Using local and RS-LOCAL calibrations with at least five local samples, we achieved similar validation results for predictions of SC up to 52 % (R² = 0.93 to 0.97; bias = -0.07 to 1.65; root mean square error (RMSE) = 2.71 % to 3.89 % total carbon; ratio of performance to deviation (RPD) = 3.38 to 4.86; and ratio of performance to interquartile range (RPIQ) = 4.93 to 7.09). However, calibrations using RS-LOCAL only required five or 10 local samples for very accurate models (RMSE = 3.16 % and 2.71 % total carbon, respectively), while purely local calibrations required 50 samples for similarly accurate results (RMSE < 3 % total carbon). Of the three approaches, the entire SSL spiked with local samples for model calibration led to validations with the lowest performance in terms of R², bias, RMSE, RPD and RPIQ. Hence, we show that a simple and comprehensible modeling approach, using RS-LOCAL together with a SSL, is an efficient and accurate strategy when using infrared spectroscopy. It decreases field and laboratory work, the bias of SSL spiking approaches and the uncertainty of local models. If adequately mined, the information in the SSL is sufficient to predict SC in new and independent study regions, even if the local soil characteristics are very different from the ones in the SSL. This will help to efficiently scale up the acquisition of quantitative soil information over space and time.

中文翻译：

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使用中红外土壤光谱库量化温带泥炭地的土壤碳

获取土壤信息的传统实验室方法对于评估关键土壤特性、土壤功能和时空生态系统服务仍然很重要。红外光谱建模可以以具有成本效益和及时的方式将这些方法联系起来并大规模扩展用于许多土壤特征。在瑞士，只有 10% 到 15% 的农业土壤已被充分绘制以服务于空间决策支持系统，因此迫切需要快速定量土壤表征。当前瑞士土壤光谱库（SSL；n  = 4374) 在中红外范围内包括来自生物多样性监测计划 (BDM) 的土壤样本，这些样本排列在瑞士各地的规则间隔网格中，以及来自瑞士土壤监测网络 (NABO) 的时间分辨数据。鉴于 SSL 中不到 2% 的样品来自有机土壤，我们旨在开发一种有效的校准采样方案和准确的建模策略，以估计 0 到 2 m 深度之间的异质样品的土壤碳 (SC) 含量两个排水泥炭地区域内的 26 个位置（农业、森林和食品科学学院 (HAFL) 数据集；n  = 116）。重点是通过有效挖掘 SSL 的光谱信息，最大限度地减少对新参考分析的需求。我们使用偏最小二乘回归 (PLSR) 以及重复的 5 次位置分组、10 倍交叉验证来预测在本地 HAFL 数据集中，SC 范围从 1% 到 52%。我们比较了不同校准方案的验证性能，包括本地模型 (1)、使用整个 SSL 结合本地样本的模型 (2)，通常称为尖峰，以及使用以下方法针对泥炭地目标站点优化的本地和 SSL 样本子集重采样本地 (RS-LOCAL) 算法 (3)。使用至少有五个局部样本的局部和 RS-LOCAL 校准，我们对高达 52% 的 SC 预测获得了类似的验证结果（R ²  = 0.93到 0.97；偏差 =  -0.07 到 1.65；均方根误差 (RMSE)  =  2.71 % 至 3.89 % 总碳；性能偏差比 (RPD)  =  3.38 至 4.86；和性能与四分位距的比值 (RPIQ)  =  4.93 到 7.09）。然而，使用 RS-LOCAL 的校准只需要 5 或 10 个本地样本即可获得非常准确的模型（RMSE 分别 为 3.16 % 和 2.71 % 总碳），而纯本地校准需要 50 个样本才能获得类似准确的结果（RMSE  <  3 % 总碳） ）。在这三种方法中，整个 SSL 使用本地样本进行模型校准导致验证在R ²方面的性能最低、偏差、RMSE、RPD 和 RPIQ。因此，我们表明，在使用红外光谱时，将 RS-LOCAL 与 SSL 结合使用的简单易懂的建模方法是一种有效且准确的策略。它减少现场和实验室工作、SSL 尖峰方法的偏差和局部模型的不确定性。如果充分挖掘，SSL 中的信息足以预测新的独立研究区域的 SC，即使当地土壤特征与 SSL 中的土壤特征非常不同。这将有助于有效地扩大时空定量土壤信息的获取。