A Simplified Data Assimilation Method for Reconstructing Time-Series MODIS NDVI Data

Normalized difference vegetation index (NDVI) is the most widely used vegetation index due to its simplicity, ease of application, and wide-spread familiarity. Time-series NDVI products have been proven to be a powerful tool to learn from past events, monitor current natural-resource conditions, extract canopy biophysical parameters and forecast terrestrial ecosystems on different scales. However, the current NDVI product is still spatiotemporally discontinuous mainly due to cloud cover, seasonal snow and atmospheric variability. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. Results indicate that the newly developed method is easy and effective in reconstructing high-quality MODIS NDVI time series.

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