We develop an intelligent method to monitor the long-term instrumental response degradation of the Fengyun-3A (FY-3A) satellite medium-resolution spectral imager (MERSI). This method assesses the instrumental response degradation during the interval between the capturing of two images using an iteratively reweighted multivariate alteration detection (IR-MAD) algorithm to statistically select invariant pixels from the different-temporal satellite images, which are obtained from the same geographic region. First, the IR-MAD algorithm is used to analyze the invariant pixels from the image scene; subsequently, the orthogonal regression of invariant pixels from two images is conducted to obtain the relative degradation of the sensor during this interval. Next, all the long-term sequence images are processed in a similar manner and the polynomial fitting is used to obtain the relative degradation curve of the sensor over the entire period of time. Herein, we conduct this procedure using the data of FY-3A/MERSI obtained from north Africa and northwestern China, and compare the obtained results with the instrumental degradation results obtained from other relevant researches. The verification results denote that the proposed method is in good agreement with other methods (the difference is less than 2%). The results obtained in north Africa are consistent with those obtained in northwestern China (the differences are less than 1% in majority of the bands), indicating the universality and reproducibility of the proposed method.