An increasing number of soil spectral libraries are being developed at larger extents, including at national, continental, and global scales. However, the prediction accuracy of these libraries was often fairly poor when used on local scales. This study evaluates different strategies to improve the model accuracy of a regional spectral library for soil organic carbon prediction in four different local target areas. In total, five strategies using the Partial least squares regression (PLSR) were compared, including the use of local, spiked-regional, spiked-subset-regional and two localized models (memory based learning (MBL) and localized PLSR). MBL derives a new local prediction model based on a subset of the regional spectra similar to the new sample to be predicted. A localized PLSR calibrates a linear regression model using projected scores of the local samples derived from a pre-trained regional PLSR model. Validation results showed that the performances of the spiked models were not much better than those derived from the local and localized models. With >20 local samples, the localized PLSR model performed better than both the local and spiked-regional models. MBL model achieved similar performance to the localized PLSR model. Nevertheless, the accuracy of the models was heavily affected by both the spectral similarity of the data and the statistics of the predictand. Therefore, we recommend localizing the prediction models. Our results also suggest that spiking affected the regression coefficients of the PLSR models but not the loadings, which enabled the compression of spectra data into informative PLS scores. If the local spectra have low similarity to the regional spectral library, building a local spectral library would be more beneficial, assuming the sample size is large enough (>30).

越来越多的土壤光谱库正在更大范围内开发，包括在国家、大陆和全球范围内。然而，当在局部尺度上使用时，这些库的预测精度通常相当差。本研究评估了提高四个不同局部目标区域土壤有机碳预测区域光谱库模型精度的不同策略。总的来说，比较了使用偏最小二乘回归 (PLSR) 的五种策略，包括使用局部、尖峰区域、尖峰子集区域和两个局部模型（基于记忆的学习 (MBL) 和局部 PLSR）。MBL 基于与待预测新样本相似的区域光谱子集，推导出新的局部预测模型。本地化 PLSR 使用源自预训练区域 PLSR 模型的本地样本的预测分数校准线性回归模型。验证结果表明，spiked 模型的性能并不比源自局部和局部模型的性能好多少。局部样本超过 20 个时，局部 PLSR 模型的性能优于局部和尖峰区域模型。MBL 模型实现了与本地化 PLSR 模型相似的性能。然而，模型的准确性受到数据的光谱相似性和预测结果的统计数据的严重影响。因此，我们建议本地化预测模型。我们的结果还表明，尖峰影响了 PLSR 模型的回归系数，但不影响载荷，这使得能够将光谱数据压缩为信息丰富的 PLS 分数。如果局部光谱与区域光谱库的相似度较低，假设样本量足够大（> 30），构建局部光谱库会更有利。