Information on soil properties is crucial for soil preservation, the improvement of food security, and the provision of ecosystem services. In particular, for the African continent, spatially explicit information on soils and their ability to sustain these services is still scarce. To address data gaps, infrared spectroscopy has achieved great success as a cost-effective solution to quantify soil properties in recent decades. Here, we present a mid-infrared soil spectral library (SSL) for central Africa (CSSL) that can predict key soil properties, allowing for future soil estimates with a minimal need for expensive and time-consuming wet chemistry. Currently, our CSSL contains over 1800 soil samples from 10 distinct geoclimatic regions throughout the Congo Basin and along the Albertine Rift. For the analysis, we selected six regions from the CSSL, for which we built predictive models for total carbon (TC) and total nitrogen (TN) using an existing continental SSL (African Soil Information Service, AfSIS SSL; n=1902) that does not include central African soils. Using memory-based learning (MBL), we explored three different strategies at decreasing degrees of geographic extrapolation, using models built with (1) the AfSIS SSL only, (2) AfSIS SSL combined with the five remaining central African regions, and (3) a combination of AfSIS SSL, the remaining five regions, and selected samples from the target region (spiking). For this last strategy we introduce a method for spiking MBL models. We found that when using the AfSIS SSL only to predict the six central African regions, the root mean square error of the predictions (RMSE_pred) was between 3.85–8.74 and 0.40–1.66 g kg⁻¹ for TC and TN, respectively. The ratio of performance to the interquartile distance (RPIQ_pred) ranged between 0.96–3.95 for TC and 0.59–2.86 for TN. While the effect of the second strategy compared to the first strategy was mixed, the third strategy, spiking with samples from the target regions, could clearly reduce the RMSE_pred to 3.19–7.32 g kg⁻¹ for TC and 0.24–0.89 g kg⁻¹ for TN. RPIQ_pred values were increased to ranges of 1.43–5.48 and 1.62–4.45 for TC and TN, respectively. In general, predicted TC and TN for soils of each of the six regions were accurate; the effect of spiking and avoiding geographical extrapolation was noticeably large. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the Congo Basin region compared to using the continental AfSIS SSL alone; thus, analyses of other soils in central Africa will be able to profit from a more diverse spectral feature space. Given these promising results, the library comprises an important tool to facilitate economical soil analyses and predict soil properties in an understudied yet critical region of Africa. Our SSL is openly available for application and for enlargement with more spectral and reference data to further improve soil diagnostic accuracy and cost-effectiveness.

中文翻译：

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中非土壤光谱库：新的土壤红外库和地理预测分析

土壤特性信息对于土壤保护、改善粮食安全和提供生态系统服务至关重要。特别是对于非洲大陆，关于土壤及其维持这些服务的能力的空间明确信息仍然很少。为了解决数据空白，近几十年来，红外光谱作为一种经济高效的土壤特性量化解决方案取得了巨大成功。在这里，我们提出了一个用于中非 (CSSL) 的中红外土壤光谱库 (SSL)，它可以预测关键的土壤特性，从而在对昂贵且耗时的湿化学的需求最小的情况下进行未来的土壤估计。目前，我们的 CSSL 包含来自整个刚果盆地和艾伯丁裂谷沿线的 10 个不同地理气候区域的 1800 多个土壤样本。为了分析，我们从 CSSL 中选择了六个区域，n = 1902 ) 不包括中非土壤。使用基于记忆的学习 (MBL)，我们探索了三种不同的策略以降低地理外推的程度，使用构建的模型 (1) 仅 AfSIS SSL，(2) AfSIS SSL 与其余五个中非地区相结合，以及 (3 ) AfSIS SSL、其余五个区域和来自目标区域的选定样本的组合（尖峰）。对于最后一个策略，我们引入了一种用于尖峰 MBL 模型的方法。我们发现，当仅使用 AfSIS SSL 预测六个中非地区时，预测的均方根误差 (RMSE _pred ) 在 3.85–8.74 和 0.40–1.66  g kg ^{-1 之间}分别为 TC 和 TN。性能与四分位距 (RPIQ _pred )的比率范围在 TC 的 0.96-3.95 和 TN 的 0.59-2.86 之间。虽然与第一个策略相比，第二个策略的效果好坏参半，但第三个策略，加入来自目标区域的样本，可以明显地将TC的 RMSE _pred降低到 3.19-7.32  g kg ^-1和 0.24-0.89  g kg ^{- 1}为 TN。RPIQ_预计值TC 和 TN 的值分别增加到 1.43-5.48 和 1.62-4.45 的范围。总体上，6个地区土壤的TC和TN预测值均准确；尖峰和避免地理外推的效果显着。我们得出的结论是，与单独使用大陆 AfSIS SSL 相比，我们的 CSSL 增加了宝贵的土壤多样性，可以改善对刚果盆地地区的预测；因此，对中部非洲其他土壤的分析将能够从更多样化的光谱特征空间中受益。鉴于这些有希望的结果，该库包含一个重要工具，可促进经济土壤分析和预测非洲一个未充分研究但至关重要的地区的土壤特性。