THE RECONSTRUCTION OF NDVI TIME SERIES USING SPATIO-TEMPORAL INFORMATION
Due to the influence of cloud cover, atmospheric disturbance and many other factors, normalized difference vegetation index (NDVI) corrupted by noises has a negative effect on the downstream applications. To this end, researchers have developed a large number of methods to reconstruct NDVI time series. The harmonic analysis of time series (HANTS) is one of the most widely used approaches of NDVI reconstruction. In this paper, HANTS algorithm was improved by the utilization of spatio-temporal information of NDVI time series with spatial filling and filtering, which makes up the deficiency of HANTS that only uses temporal information of NDVI time series. The simulation experiments on moderate resolution imaging spectroradiometer (MODIS) NDVI time series have proved that our method has effectively improved the quantitative and qualitative reconstruction performance of HANTS algorithm.