充分挖掘大样本土壤光谱库中有效信息，建立普适性强的土壤全氮(TN)含量反演模型，是高光谱分析的重要应用方向之一。研究采用偏最小二乘回归(PLSR)全局建模、局部加权回归(LWR)和模糊K均值聚类结合PLSR(FKMC-PLSR)局部建模三种方法，分别建立了来自中国西藏、新疆、黑龙江、海南等13个省采集的17种土类1661个土壤样本TN值的高光谱反演模型，并对浙江省104个水稻土样本进行模型验证。结果表明，在大样本下PLSR全局模型对高TN值待预测样本存在低估现象，导致整体预测精度偏低； LWR和FKMC-PLSR局部模型比PLSR全局模型能够更为准确地反演TN含量。研究结果可为利用大样本光谱数据库建立稳定性和普适性较高的土壤TN含量预测模型提供参考。

Building universal deduction models for predicting the soil total nitrogen (TN) content by using data mining of large soil spectral libraries is one of the most important applications of hyperspectral analysis. In this study, 1661 soil samples representing 17 soil types from 13 provinces of China (e.g., Tibet, Xinjiang, Heilongjiang and Hainan) are employed for modeling the soil TN content using global partial least squares regression (PLSR), locally weighted regression (LWR) and fuzzy K-means clustering combined with PLSR (FKMC-PLSR). Another 104 paddy soil samples collected from Zhejiang Province are used to validate the established models. Results showed that when predicting soil TN from a large dataset, global PLSR underestimates high values of TN, which generates an overall low prediction accuracy. By contrast, LWR and FKMC-PLSR perform better than global PLSR. It is suggested that the results can provide useful information for establishing robust and universal models for soil TN prediction using large soil spectral libraries.