A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products

Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) daily VI data, temporal compositing methods are commonly used produce composite minimize negative influence of noise over given time interval. However, series with high resolution were preferred by many applications such as phenology change detections. This study presents novel strategy named DAVIR-MUTCOP (DAily Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference index (NDVI) time-series reconstruction resolution. The core is combination advantages both original temporally products, selecting more observations quality through variation corrected data. was applied reconstruct high-quality NDVI using MODIS multi-temporal products two areas continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska 2001 2012 forty-six AmeriFlux evenly distributed across CONUS 2006 2010. In these areas, also compared several methods, Harmonic Analysis Time-Series (HANTS) observations, Savitzky–Golay (SG) filtering mask auxiliary SG results showed that significantly improved reconstructed series. It performed best reconstructing space (coefficient determination (R2 = 0.93 ~ 0.94) between ground-observed LAI). presented highest robustness accuracy parameter 0.99 1.00, bias 0.001, root mean square error (RMSE) 0.020). Only this study; nevertheless, proposed universal potential way other VIs or operational sensors, e.g., AVHRR VIIRS.