Automatic distortion-suppressed time series fitting method for irregular sampled NDVI

doi:10.1080/01431161.2020.1755739

2020-06-30

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DOI：10.1080/01431161.2020.1755739

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Time series Normalized Difference Vegetation Index (NDVI) fitting, which describes the discrete time series observations with a mathematical model, can remove the residual noise, fill in the missing data, and produce a noise-free continuous time series; therefore, numerous time series fitting methods have been developed, but most of them focused on regularly sampled, noise-suppressed data, as NDVI time series derived from medium-spatial remote sensing images has irregular sampled and distortion-intensive characteristic, limiting the applicability of traditional methods. To address this problem, an automatic distortion-suppressed time series fitting method for irregular sampled NDVI is proposed in this paper. First, the observation quality is evaluated: (I) the observations on a fully clear image are identified as clear observations. (II) According to the prior knowledge that the NDVI is underestimated, the observations in the upper convex hull and above the line of two temporal-adjacent clear observations (identified by fully clear image and upper convex) are labelled as clear observations. (III) As the temporal evolution of NDVI is a slow and continuous process, deep-V observations, which has experienced an abrupt decrease and increase consecutively, are identified as noisy observations. Second, different weights are assigned to observations based on the quality assessment result, and the NDVI time series is weighted fitted. To highlight the observations above the fitting line and suppress those below the fitting line, the weights are updated according to the direction and distance between the actual observation and fitting prediction value. Then, fitting and reweighting processes are repeated until a stable fitting result is obtained. We use images acquired by the Multi-Spectral Imager (MSI) onboard Sentinel-2 satellite in Shouxian, Anhui Province to construct NDVI time series and test the method. The result demonstrates that our method can suppress the distortion and realize time series fitting automatically, compared with the state of art method, our method can obtain reasonably overestimation of the NDVI, providing an alternative for fitting irregular sampled NDVI.

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