Soil available phosphorus (P)and available potassium (K)don’t possess direct spectral response in the near infrared (NIR)region.They are predictable because of their correlation with spectrally active constituents (organic matter,carbonates, clays,water,etc.).Such correlation may of course differ between the soil sample sets.Therefore,the NIR calibration models with fixed structure are difficult to achieve good prediction performances for soil P and K.In this work,the method of recursive partial least squares (RPLS),which is able to update the model coefficients recursively during the prediction process,has been applied to improve the predictive abilities of calibration models.This work compared the performance of partial least squares re-gression (PLS),locally weighted PLS (LW-PLS),moving window LW-PLS (LW-PLS2)and RPLS for the measurement of soil P and K.The entire data set of 194 soil samples was split into calibration set and prediction set based on soil types.The calibra-tion set was composed of 120 Anthrosols samples,while the prediction set included 29 Ferralsols samples,23 Anthrosols sam-ples and 22 Primarosols samples.The best prediction results were obtained by the RPLS model.The coefficient of determination (R 2 )and residual prediction deviation (RPD)were respectively 0.61,0.76 and 1.60,2.05 for soil P and K.The results indicate that RPLS is able to learn the information from the latest modeling sample by recursively updating the model coefficients.The proposed method RPLS has the advantages of wider applicability and better performance for NIR prediction of soil P and K com-pared with other methods in this work.%土壤速效磷与速效钾在近红外区没有直接与它们相关的吸收峰，只能借助与其他拥有直接吸收峰物质（有机质，碳酸盐，粘土矿物，水分等）之间的相关关系而被近红外光谱技术所预测。这种相关关系会随着土壤样品构成的不同而不断变化，因此采用固定结构的近红外光谱模型很难对速效磷与速效钾取得较好的预测效果。提出采用递归偏最小二乘法（RPLS）在预测过程中递归更新土壤速效磷与速效钾的回归系数，以提高模型的预测能力；比较了偏最小二乘法（PLS），局部加权 PLS（LW-PLS），滑动窗口 LW-PLS（LW-PLS2）和 RPLS 对于土壤速效磷与速效钾含量的预测结果。194份土壤样品根据土壤类型分为建模集与预测集：建模集包含120份人为土样品；预测集则包含29份铁铝土样品，23份人为土样品和22份初育土样品。结果表明：RPLS 模型取得了最优的预测结果，获得的决定系数（R 2）分别为0．61与0．76，预测相对分析误差（RPD）分别为1．60与2．05。说明 RPLS 通过不断更新模型的回归系数，能够适应新加入建模集样品的信息。相比于其他方法，预测精度更高，适用范围更广。
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