Abstract: Vegetation clumping index (CI) is a structural parameter to characterize the spatial clustering degree of vegetation canopy leaves, which plays an important role in extracting other vegetation parameters and in modeling the land surface． Currently, remote sensing CI products has been limited to few multiangular satellite-borne sensors, compared to other remote sensing products (e.g., leaf area index, LAI)． Therefore, the study on long time series of CI products still lack． This in return causes the fact that many models do not consider the cyclical variability of CI product in many applications． Based on the global long-time series (2001-2019) and monthly CI products developed and produced by the author’s research team, this paper uses the Fourier decomposition technique to explore the interannual periodic variation of 19-year CI product in a global scale． The methods are as follows: First, the CI product is preprocessed by screening out and smoothening the so-called outliers, and filling the gaps, in order to generate a relatively complete year-to-year time series of CI product with the effective pixel proportion accounting for 75.58% of the vegetation area in the global scale． Assuming that the interannual variation of the time-series CI product can be decomposed into a sequence composed of multiple sets of cosine wave signals with possible additive noise of random distribution, discrete Fourier transform is used to extract the cosine wave with the largest amplitude (main wave) in the time series, which is further used as the main index to characterize the interannual periodic variation frequency, the period and amplitude of main waves (i.e., mT and mA ) is analyzed and further cross-compared with the annual phenological (NumCycles, NC) and peak time (Peak_1) of MODIS phenological products (MCD12Q2)． Also, the anti-noise performance of this method is tested using simulation data and high-quality typical CI pixels． The results show that the pixel proportions with mT = 12 are significantly larger than that in other periods, accounting for 76.22% of the study area． This indicates that the interannual variation with the 12-month period length is the most significant among various periodic variations of the CI time frequencies． The validation results show that the period with mT = 12 in the study area highly coincide with the annual phenological period of MODIS phenological products with NC = 1, with the validation accuracy reaching to 96%． For the pixel with mT = 12, the low-peak month of the main wave is close to the peak value of the MODIS phenological product and the average difference between them is about 1.37 months． In general, the low-peak month of CI product (i.e., the month with the most clumping vegetation foliage) is earlier than the peak value of the MODIS phenological product that is probably related to the greenness of vegetation foliage． This implies that the maximum clumping status is probably earlier to maximum greenness status for the vegetation foliage． This study provides evidences for the improved understanding of the interannual periodic variation for vegetation foliage clumping effect．