Abstract. Information on soil properties is crucial for soil preservation, improving food security, and the provision of ecosystem services. Especially, 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 gained 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 1,800 soils from ten distinct geo-climatic regions throughout the Congo Basin and wider African Great Lakes region. We selected six hold-out core regions from our SSL, augmented them with the continental AfSIS SSL, which does not cover central African soils. We present three levels of geographical extrapolation, deploying Memory-based learning (MBL) to accurately predict carbon (TC) and nitrogen (TN) contents in the selected regions. The Root Mean Square Error of the predictions (RMSE_pred) values were between 0.38–0.86 % and 0.04–0.17 % for TC and TN, respectively, when using the AfSIS SSL only to predict the six regions. Prediction accuracy could be improved for four out of six regions when adding central African soils to the AfSIS SSL. This reduction of extrapolation resulted in RMSEpred ranges of 0.41–0.89 % for TC and 0.03–0.12 % for TN. In general, MBL leveraged spectral similarity and thereby predicted the soils in each of the six regions accurately; the effect of avoiding geographical extrapolation and forcing regional samples in the local neighborhood (MBL-spiking) was small. We conclude that our CSSL adds valuable soil diversity that can improve predictions for the regions compared to using the continental scale 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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填补关键空白：中非的土壤红外图书馆

摘要。有关土壤特性的信息对于土壤保存，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，仍然缺乏关于土壤的空间明晰信息及其维持这些服务的能力。为解决数据空白，近几十年来，红外光谱作为一种经济有效的定量土壤性质的解决方案而获得了巨大的成功。在这里，我们介绍了中部非洲（CSSL）的中红外土壤光谱库（SSL），该库可以预测关键的土壤性质，从而可以在不需昂贵且费时的湿化学的情况下，对未来的土壤进行估算。目前，我们的CSSL包含来自整个刚果盆地和更广阔的非洲大湖地区的10个不同地理气候区域的1,800多种土壤。我们从SSL中选择了六个保留核心区域，通过非洲AfSIS SSL（不涵盖中部非洲土壤）增强了它们的实力。我们介绍了三个层次的地理外推法，即部署基于内存的学习（MBL）以准确预测所选区域中的碳（TC）和氮（TN）含量。预测的均方根误差（RMSE_掠夺当仅使用AfSIS SSL预测六个区域时，TC和TN的值分别介于0.38–0.86％和0.04–0.17％之间。将非洲中部土壤添加到AfSIS SSL中后，可以提高六个区域中四个区域的预测准确性。外推的减少导致TC的RMSEpred范围为0.41-0.89％，TN的0.03-0.12％。通常，MBL利用光谱相似性，从而准确预测六个区域中每个区域的土壤。避免地理外推并强迫本地邻域中的区域样本（MBL加标）的效果很小。我们得出的结论是，与仅使用大陆规模的AfSIS SSL相比，我们的CSSL增加了宝贵的土壤多样性，可以改善对区域的预测。从而，对中非其他土壤的分析将能够从更加多样化的光谱特征空间中受益。鉴于这些令人鼓舞的结果，该图书馆提供了一个重要的工具，可促进对土壤进行深入研究但仍十分关键的非洲地区的经济土壤分析并预测土壤性质。我们的SSL可以公开使用，并可以使用更多光谱和参考数据进行扩展，以进一步提高土壤诊断的准确性和成本效益。