Reconstruction of Vegetation Index Time Series Based on Self-Weighting Function Fitting from Curve Features

Abstract

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications.