农作物重金属污染监测是当今高光谱遥感研究的重要内容之一.重金属污染会引发农作物光谱畸变，但不同污染程度的作物光谱在形态上仍具有极高相似度，所以监测其畸变的关键问题之一是如何提取相似光谱间污染的弱差信息.本研究将变分模态分解(VMD)理论引入到光谱信息弱变化监测中，并结合包络线去除(CR)与欧式距离(ED)方法构建了光谱弱差信息的VMD-CR-ED测度模型，通过与光谱角(SA)与光谱相关系数(SCC)等常规光谱测度算法进行应用比较，证明VMD-CR-ED模型在有效区分玉米叶片的不同胁迫程度方面更具有优越性.同时，分析了VMD-CR-ED模型与玉米叶片光谱8个特征子波段的相关性.最后，基于检验数据验证了VMD-CR-ED测度模型在农作物铜铅污染监测方面具有可行性和可靠性.

The monitoring of heavy metal pollution in crops is one important application of hyperspectral remote sensing study. Heavy metal pollution can cause spectral distortion of crop, but the crop spectra of different pollution degrees still have very high similarity in shape, so one of the key problems in monitoring its distortion is how to extract the weak difference information between similar spectra. In this study, a VMD-CR-ED measurement model was established by introducing the variational mode decomposition (VMD) theory into weak change monitoring of spectral information and combining with the continuum removal (CR) and euclidean distance (ED) methods. The model was proved to be superior in distinguishing the different pollution degrees of corn leaves by compared with SA and SCC. At the same time, the correlation between VMD-CR-ED model and eight characteristic sub-bands of corn leaf spectrum was analyzed. Finally, based on the test data, it is proved that the VMD-CR-ED measurement model is feasible and reliable in the monitoring of copper and lead pollution of crops.