“A study on vegetation indices has made significant progress. This study proposes a daily resolution EVI reconstruction method based on MODIS daily surface reflectance products to address the issue of a large number of missing pixels in Enhanced Vegetation Index (EVI) caused by low temporal resolution and cloud coverage. By combining MVC (Maximum Value Composite) with HANTS (Harmonic Analysis of Time Series) algorithm, the daily resolution EVI time series data of the Huang Huai Hai Plain in 2021 was successfully reconstructed. The research results indicate that this reconstruction algorithm can not only be used for the reconstruction of large-scale and long-term daily resolution EVI time series data, but also the reconstruction results have rich texture, filling in a large number of missing pixels in the original EVI and removing noise from the original EVI data. Compared with the S-G filtering method, the reconstructed EVI using the HANTS algorithm shows advantages in spatial distribution rationality and fidelity. The average annual R2 and RMSE between the reconstructed EVI and high-quality EVI pixels are 0.94 and 0.024, respectively, which are better than the S-G method's 0.73 and 0.093. This study provides new ideas and technological approaches for generating high temporal resolution EVI, which has important application value in fields such as vegetation monitoring and ecological assessment.”