A New Method to Reconstruct MODIS EVI Time Series Data Set based on Graph Theory

The MODIS Enhanced Vegetation Index (EVI) time-series data has been widely used in many research fields such as vegetation observation, ecological environment, and global meteorological changes. However, even though the EVI time series data has undergone strict preprocessing, there are still some noises in it. Therefore, this paper develops a simple and effective method to reconstruct EVI time-series data and eliminate the noise in EVI time-series data, especially some noise caused by atmospheric clouds and snow cover. The theory of the new method is derived from graph theory, using the relationship of the Laplacian matrix to assign the weight of the pixel of the selected neighborhood window in EVI to get the fitting of the center pixel. The new method has been applied to MODIS MOD13A1 products from 2016 to 2018 and compared with the S-G filtering method, Harmonic Analysis of Time Series method, Double Logistic function method, and Asymmetric Gaussian model function method. The results show that in the desert, grassland, and woodland, the absolute difference of the leave-one verification test of the new method is the smallest, which is better than other methods; when fitting EVI time-series data of different vegetation types, the graph theory neighbor method presents a better detailed fitting curve; the RMSE values of the new method in the five vegetation types are 200.59, 46.58, 63.48, 165.47, and 40.95 respectively, which are the smallest values among the five methods and are more effective in obtaining high-fidelity and high-quality EVI time-series data. The method research in this article can provide a useful reference for the denoising of vegetation remote sensing time-series data and the study of the ecological environment. ©2022, Science Press. All right reserved.