The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

“…Many models have been proposed for this purpose in the literature, and currently the piecewise logistic function approach (shortened to Logistic) [13], the Fourier-based approach (shortened to Fourier) [16], the Whittaker smoother (shortened to Whit) [17], and the Savitzky-Golay filter (shortened to SG) have been applied to the Indian monsoon region. Logistic was used for generating the MODIS LSP product (MCD12Q2) [13][14][15], but it was inapplicable for modeling the vegetation growth trajectories with a two-stage greenness increase [18] or sharp curve valleys.…”

“…Logistic was used for generating the MODIS LSP product (MCD12Q2) [13][14][15], but it was inapplicable for modeling the vegetation growth trajectories with a two-stage greenness increase [18] or sharp curve valleys. Fourier was chosen to extract phenology from the MERIS Terrestrial Chlorophyll Index (MTCI, 4.6 km) [16], MOD13C1 NDVI and EVI (5.6 km, 16 day), and GIMMS NDVI (8 km, 15 day) [19] in India. However, Fourier would appear as spurious oscillations in the long stable part of the vegetation growth trajectory.…”

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

“…The commonly used methods are the threshold-based technique which is divided into absolute VI threshold (e.g., Lloyd, 1990;Fischer, 1994;Myneni et al, 1997;Zhou et al, 2001) and relative threshold (e.g., White et al, 1997;Jonsson and Eklundh, 2002;Delbart et al, 2005;Karlsen et al, 2006;Dash et al, 2010), moving average (Reed et al, 1994), spectral analysis (Jakubauskas et al, 2001;Moody and Johnson, 2001), and inflection point estimation in the time series of vegetation indices (Moulin et al 1997;Zhang et al 2003;Tan et al, 2011). Various approaches in detecting phenological timing, particularly the greenup onset, are compared using the same dataset (de Beurs and Henebry, 2010;White et al, 2009).…”

“…These products include: (1) the MODIS Land Cover Dynamics Product (MCD12Q2) derived from MODIS NBAR (nadir bidirectional reflectance distribution function adjusted reflectance) EVI (enhanced vegetation index) (500m-1000m), which is the only global product that is produced on an operational basis from 2001 to present Ganguly et al, 2010); (2) the MODIS-based product generated at NASA-GSFC (Goddard Space Flight Center) in support of the North American Carbon Program, which was produced using MODIS data at a spatial resolution of 250m-500m (Morisette et al, 2009;Tan et al, 2011); (3) the MODIS phenology product being generated for the contiguous United States (CONUS) by the US Forest Service (Hargrove et al, 2009); (4) the USGS long-term 1-km AVHRR phenology product for CONUS (1989-present;Reed et al, 1994); (5) the NOAA 4-km GVIx phenology over North America from 1982(Zhang et al, 2007; (6) the global 4.6 km product for 2005 from the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) (Dash et al, 2010); and (6) the global product based on FPAR (Fraction of Photosynthetically Active Radiation) developed by the European Space Agency (Verstraete et al, 2008).…”

Evaluation of Multiple Spring Phenological Indicators of Yearly GPP and NEP at Three Canadian Forest Sites

“…Fitted curves for vegetation index (VI) time series are used (e.g., determining the transition dates from the local maxima and minima in the fitted function), including the enhanced vegetation index (EVI) [4,18,21], the normalized difference vegetation index (NDVI) [2,20,22], and the MERIS Terrestrial Chlorophyll Index (MTCI) [23]. The advantage of remote-sensing data is that they are uniquely spatially explicit, and are useful for evaluating and validating models and assumptions with a wider spatial coverage compared to flux measurements.…”