One-Step High-Quality NDVI Time-Series Reconstruction by Joint Modeling of Gradual Vegetation Change and Negatively Biased Atmospheric Contamination

Abstract

The normalized difference vegetation index (NDVI) can reflect the plant life cycle of growth and senescence and has become a widely used tool for many applications related to phenology, ecology, and environment. However, unwanted disturbance from cloud, snow, and other atmospheric effects greatly lowers the NDVI quality and hinders its further application. In this article, differing from the previous research attempting to approach the upper NDVI envelope by local adjustment or threshold-related iteration, a novel one-step global variational reconstruction (OGVR) method for NDVI time series is proposed via joint modeling of the gradual vegetation change and negatively biased atmospheric contamination. Two versions of the proposed method are designed for processing NDVI data with or without auxiliary flag information. Long-term and global-scale Advanced Very High Resolution Radiometer (AVHRR) global inventory monitoring and modeling system (GIMMS) data were applied in simulated and real-data experiments to verify the proposed method. The results show that the proposed method can successfully estimate the natural vegetation change from seriously contaminated NDVI time series and can conquer the problem of continuous low-value gaps. The qualitative and quantitative comparisons with five other widely used methods indicate that the proposed method has significant advantages in terms of both effectiveness and stability.