Abstract: In this paper, Unit Feature Space (UFS) Classification method is applied to analyse rainfall intensity field by using multi-spectral GMS satellite imagery. This method can interactively carry out coordinated analysis between measured rainfall amount per hour and multi-spectral satellite measuring information and can exactly segment every rainfall intensity sample cluster in spectral feature space, thus providing a feasible way to establish statistical relationship between satellite measurements and rainfall intensity grade. To reduce as much indetermi nateness and error that may occur as possible in classifying data around the division, an fuzzy separation formula was given on the membership principles of fuzzy set theory. A rainfall intensity category matrix is formulated based on the calculation and comparison of occurrence probabilities of clear sky, cloudy (rainless), light, moderate, heavy and hard rain in each UFS and that is just the decision rule for rainfall intensity field analysis by using multi-spectral GMS satellite imagery. Tests of more than 500 samples of rainfall occurrence show that the IRVIS rainf all int ensity category matrix yielded the accuracy of 70% or so for all categories of hard, heavy, moderate and light rain samples and total precision of 73.88% for measured rainfall intensity categ orization from nearly 1400 samples with cloud cover available. After the separation of the samples into three broad kinds, i.e., no rainfall, light-moderate and heavy-torrential rainf all, the measurement hit rate and analysis success ratio were considerably raised and total accuracy arrives at the precision of 84.49%. Though the accuracy of IR-WV or IR1-TIR2-IR1 rainfall int ensity category matrix is lower in a sort of way than that of IR-VIS rainf all intensity category matrix, the total precision is also up to 75% for three grade analyses of no rainfall, light-moderate and heavy-hard rainfall.