Using time series segmentation for deriving vegetation phenology indices from MODIS NDVI data

Characterizing vegetation phenology is a highly significant problem, due to its importance in regulating ecosystem carbon cycling, interacting with climate changes, and decision-making of croplands managements. While ground based sensors, such as the AmeriFlux sensors, can provide measurements at high temporal resolution (every hour) and can be used to accurately calculate vegetation phenology indices, they are limited to only a few sites. Remote sensing data, such as the Normalized Difference Vegetation Index (NDVI), collected using the MODerate Resolution Imaging Spectroradiometer (MODIS), can provide global coverage, though at a much coarser temporal resolution (16 days). In this study we use data mining based time series segmentation methods to derive phenology indices from NDVI data, and compare it with the phenology indices derived from the AmeriFlux data using a widely used model fitting approach. Results show a significant correlation (as high as 0.60) between the indices derived from these two different data sources. This study demonstrates that data driven methods can be effectively employed to provide realistic estimates of vegetation phenology indices using periodic time series data and has the potential to be used at large spatial scales and for long-term remote sensing data.