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As an important indicator of cropping intensity, Cropping Index (CI) is defined as the number of peaks in the Vegetation Index (VI) time-series curve in a year. The existing CI mapping algorithms (e.g., the cross-fitting and the second order difference algorithm) are vulnerable to noise contained in the VI time series and need a priori knowledge and some extra constraints which could not be directly derived from the VI time-series data. In this paper, a shape-matching method is developed which can map CI directly from the preprocessed VI time-series data without the de-noising processes. This shape-matching method utilizes a decision-making process to find out the true peaks in the VI time-series curve based on a rank order mathematical morphology algorithm. The processing procedure involves five steps: (a) determination of the temporal moving window size, (b) detection of local maximum/minimum points, (c) exclusion of false maximum/minimum points, (d) determination of the threshold for the minimum growth amplitude, and (e) mapping of CI. This shape-matching method only needs two input parameters, the temporal moving window size and the threshold for the minimum growth amplitude, which can be both directly derived from the VI time-series data with some selected test pixels. Moreover, the response of the shape-matching method is relatively insensitive to the exact values of its design parameters, making it more flexible and effective in adapting to other regions. This new method is applied to map the CIs in Jiangsu Province, China, in 2010, based on the Enhanced Vegetation Index (EVI) time-series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) product. The overall CI mapping accuracy for the shape-matching method is 80 percent, which is much higher than the CI mapping accuracy of 60 percent for the second order difference algorithm. This shape-matching method can be further applied to other regions with a grid-search for its optimal parameters using some test pixels.