In order to address the issues arising due to the various environmental problems that are currently attracting attention, it is necessary to devise a method that enables wide-area monitoring of fluctuations in vegetation conditions such as variations in moisture and temperature and land cover changes. By taking advantage of both the high temporal resolution and the wide swath mode of multi-temporal satellite data, such as NOAA/AVHRR, MODIS, and SPOT/vegetation, it is possible to perform high-frequency monitoring of wide-area, land cover changes. However, since the multi-temporal satellite data are influenced by clouds and system noise, in many cases, they must be processed in order to accurately represent the actual surface conditions. This study describes the development of a multi-temporal, spectrum anomaly-detection method, which takes into consideration wide-area seasonal changes, based on SPOT/vegetation S10 products. To reduce the effect of clouds on the reference-year data, the spectral information of the pixels was first converted to characters, and the influence of clouds was eliminated through a time-series modeling using hidden Markov models. Since the anomaly-detection method requires a clustering of character strings, a dedicated software based on the self-organizing map algorithm was developed. The data for anomaly detection is not dependent on the information of neighboring pixels, and it is possible to detect an anomaly even if there is only one pixel. By applying this method, we were able to detect a burned scar in Far East Russia. Once the parameters necessary for the calculation of the anomaly-detection score are obtained, anomaly-detection processing at 10-day intervals can be performed using a personal computer.