Abstract. Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling 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 the relatively low representation of organic soils and their organo-mineral diversity in the SSL, we aimed to develop both an efficient calibration sampling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous samples between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations. We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local samples (2) and 15 subsets of local and SSL samples using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local samples, we achieved similar validation results for predictions of SC up to 52 % (R² = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local samples using RS-LOCAL only required 5 local samples for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 samples for similarly accurate results (RMSE R², bias and RMSE. Hence, we show that a simple and comprehensible modelling 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 a 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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通过有效利用中红外土壤光谱库中的变化预测土壤碳

摘要。传统的获取土壤信息的实验室方法对于评估随时间和空间变化的关键土壤特性，土壤功能和生态系统服务仍然很重要。红外光谱建模可以以经济高效且及时的方式将这些方法链接并大规模扩展以用于许多土壤特性。在瑞士，仅绘制了10％至15％的农业土壤，足以为空间决策支持系统服务，这迫切需要对土壤进行快速定量表征。当前的瑞士土壤光谱库（SSL; n （= 4374）包括来自生物多样性监测计划（BDM）的土壤样品，这些样品以固定的间距分布在整个瑞士的网格中，以及来自瑞士土壤监测网络（NABO）的时间分辨数据。鉴于SSL中有机土壤及其有机矿物质多样性的代表性相对较低，我们旨在开发一种有效的校准采样方案和精确的建模策略，以估算深度在0 m至2 m之间的非均质样品的土壤碳（SC）含量。来自两个排水泥炭地区域内的26个位置（HAFL数据集；n = 116）。重点是通过有效地挖掘SSL实例及其目标特征表示的光谱信息，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）并结合5次重复（按位置分组），10倍交叉验证（CV）来预测局部HAFL数据集中的SC范围从1％到52％。我们比较了涉及本地模型（1），使用掺有本地样本的整个SSL的模型（2）和使用RS-LOCAL算法的15个本地样本和SSL样本的子集的模型的验证性能（3）。使用至少5个本地样本的本地和RS-LOCAL校准，我们获得了高达52％的SC预测的相似验证结果（R ² = 0.94-0.96，偏差= -0.6-1.5，RMSE = 2.6％至3.5％的总碳。但是，对于非常精确的模型（RMSE = 2.9％总碳），使用RS-LOCAL进行的代表性SSL和本地样品的校准仅需要5个本地样品，而对于类似的准确结果，本地校准需要50个样品（RMSE R ²，偏差和RMSE。因此，我们表明，使用红外光谱法时，将RS-LOCAL与SSL结合使用的简单且易于理解的建模方法是一种有效且准确的策略。它减少了现场和实验室工作，减少了SSL派生方法的偏差以及本地模型的不确定性。如果充分挖掘，即使本地土壤特征与SSL的特征非常不同，SSL中的信息也足以预测新的独立研究区域的SC。这将有助于在空间和时间上有效地扩大对定量土壤信息的获取。