A soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. However, achieving this higher accuracy comes with associated costs, complexity, and resource requirements, thus limiting widespread adoption. Furthermore, there is a lack of comprehensive frameworks for developing and utilizing soil spectral libraries (SSLs) to make predictions for specific samples. While calibration samples are necessary, there is the need to optimize SSL development through strategically determining the quantity, location, and timing of these samples based on the quality of the information in the library. This research aimed to develop a spatially optimized SSL and propose a use-framework tailored for predicting soil properties for a specific farmland context. Consequently, the Moroccan SSL (MSSL) was established utilizing a stratified spatially balanced sampling design, using six environmental covariates and FAO soil units. Subsequently, various criteria for calibration sample selection were explored, including a spatial autocorrelation of spectra principal component (PC) scores (spatial calibration sample selection), spectra similarity memory-based learner (MBL), and selection based on environmental covariate clustering. Twelve soil properties were used to evaluate these calibration sample selections to predict soil properties using the near infrared (NIR) and mid infrared (MIR) ranges. Among the methods assessed, we observed distinct precision improvements resulting from spatial sample selection and MBL compared to the use of the entire MSSL. Notably, the Lin’s Concordance Correlation Coefficient (CCC) values using the spatial calibration sample selection was improved for Olsen extractable phosphorus (OlsenP) by 41.3% and Mehlich III extractable phosphorus (P_M3) by 8.5% for the MIR spectra and for CEC by 25.6%, pH by 13.0% and total nitrogen (Tot_N) by 10.6% for the NIR spectra in reference to use of the entire MSSL. Utilizing the spatial autocorrelation of the spectra PC scores proved beneficial in identifying appropriate calibration samples for a new sample location, thereby enhancing prediction performance comparable to, or surpassing that of the use of the entire MSSL. This study signifies notable advancement in crafting targeted models tailored for specific samples within a vast and diverse SSL.

中文翻译：

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用于改进土壤特性预测的摩洛哥土壤光谱库使用框架：评估地统计方法


任何光谱仪生成的土壤光谱都需要一个校准模型来估计土壤特性。为了获得最佳结果，假设局部校准的模型提供更准确的预测。然而，实现这种更高的准确性会带来相关的成本、复杂性和资源要求，从而限制了广泛采用。此外，缺乏开发和利用土壤光谱库 （SSL） 对特定样本进行预测的综合框架。虽然校准样本是必需的，但需要根据库中信息的质量战略性地确定这些样本的数量、位置和时间，从而优化 SSL 开发。本研究旨在开发一种空间优化的 SSL，并提出一个为预测特定农田环境的土壤特性而量身定制的使用框架。因此，摩洛哥 SSL （MSSL） 采用分层空间平衡抽样设计，使用六个环境协变量和 FAO 土壤单位。随后，探索了校准样本选择的各种标准，包括光谱主成分 （PC） 分数的空间自相关（空间校准样本选择）、光谱相似性基于记忆的学习者 （MBL） 和基于环境协变量聚类的选择。使用 12 种土壤特性来评估这些校准样品选择，以使用近红外 （NIR） 和中红外 （MIR） 范围预测土壤特性。在评估的方法中，我们观察到与使用整个 MSSL 相比，空间样本选择和 MBL 带来的精度有明显的提高。 值得注意的是，使用空间校准样品选择的 NIR 光谱的 Olsen 可萃取磷 （OlsenP） 和 Mehlich III 可萃取磷 （P_M3） 的 Lin's 相关系数 （CCC） 值提高了 41.3%，CEC 提高了 25.6%，pH 值提高了 13.0%，总氮 （Tot_N） 提高了 10.6%，相对于整个 MSSL 的使用。事实证明，利用光谱 PC 分数的空间自相关有助于为新样品位置确定合适的校准样品，从而提高与使用整个 MSSL 相当或超过使用的预测性能。这项研究标志着在为庞大多样的 SSL 中的特定样本制作目标模型方面取得了显着进展。