Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series

Abstract

Time series of normalized difference vegetation index (NDVI) are important data sources for environmental monitoring. Continuous efforts are put into their production and updating. The recently released Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data set is a consistent time series with 1/12° spatial and bi-monthly temporal resolution. It covers the time period from 1981 to 2011. However, it is unclear if vegetation density and phenology derived from GIMMS are comparable to those obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI with 250 m ground resolution. To check the consistency between GIMMS and MODIS data sets, a comparative analysis was performed. For a large European window (40 × 40°), data distribution, spatial and temporal agreement were analyzed, as well as the timing of important phenological events. Overall, only a moderately good agreement of NDVI values was found. Large differences occurred during winter. Large discrepancies were also observed for phenological metrics, in particular the start of season. Information regarding the maximum of season was more consistent. Hence, both data sets should be well inter-calibrated before being used concurrently.

A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

“…The static threshold method is limited in that it does not always apply to the entire region. In contrast, the dynamic threshold extracts the vegetation phenology by using the varying VI or LAI value obtained from various threshold algorithms [11,15,26,[28][29][30]. Although the advantage of evaluating vegetation phenology using the dynamic threshold is obvious, the accuracy of the retrieved results strongly depends on the threshold algorithms.…”

“…So far, three main types of approaches can help people identify the vegetation phenophases [8,9], which are phenological observations on the ground for individual plants or tree stands [4,10], derivative phenophases based on remote senesing data [2,6,8,[11][12][13][14][15][16][17], and plant phenology models based on climate factors (e.g., temperature, light and soil moisture, etc.) [18][19][20].…”

“…The filter approaches involve the harmonic analysis of time series (HANTS) algorithms [5,22,23], the Savitzky-Golay filtered algorithm [6,16], the Fourier filter [21,24], the Whittaker smoother [11] and the six-time polynomials [5,25,26]. The static threshold method identifies the start or end of the vegetation growing season by setting a constant VI or LAI value [27].…”

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

“…NDVI3g is appropriate for long-term studies of land surface trends in vegetation, seasonality and coupling between climate variability and vegetation over the last three decades [31,32].…”

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

“…Time-series of remotely sensed VI are valuable sources of data for cultivated and natural vegetation landscapes environmental monitoring. In [6] tion Imaging Spectro-radiometer) as a keystone satellite sensor system providing multiple products, such as MOD09A1 product, a near-daily coverage of the Earth surface, at 500 m in visible and infrared ranges. This product has also the advantage of two narrow discrete channels in the SWIR bands with a signal to noise ratio above 100 [7] which both could be useful for the monitoring of leaf water content [8].…”