Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

Abstract

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R 2 = 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R 2 > 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest

“…PRI depends explicitly on the fraction of absorbed PAR that is dissipated as heat [6] and its use has been shown to improve the estimates of carbon fluxes in leaves and canopies using remotely-sensed data [7,8]. Recently, the power of this index has been demonstrated empirically using MODIS ocean band 11 (526-536 nm) [9,10] and has been shown to be a useful proxy for tracking phenology in the boreal ecosystem [10]. MODIS is currently the only space-based instrument which is providing global data in this spectral region on a near daily basis.…”

Impacts of Climate on Spatiotemporal Variations in Vegetation NDVI from 1982–2015 in Inner Mongolia, China

“…Vegetation change dynamics are important indicators for assessing these eco-environmental changes. Long-time series data of vegetation cover are often used to detect the dynamic vegetation changes and extract change characteristics and patterns [8][9][10]. Vegetation indices, such as the normalized difference vegetation index (NDVI), are important characteristic parameters for revealing surface vegetation features [11].…”

Assessment of Spatio-Temporal Patterns of Black Spruce Bud Phenology across Quebec Based on MODIS-NDVI Time Series and Field Observations

“…For example, SIF, which is mostly driven by plant fluorescence yield and by the amount of incoming photosynthetically active radiation (PAR), is an indicator of plant carbon uptake (gross primary production, photosynthetic light-use efficiency). Consequently, SIF-and other phenological indicators based on plant physiology [43,44] define, for example, the start of the growing season (SOS) as the beginning of photosynthetic activity (e.g., date of spring recovery in evergreen trees). In contrast, with our approach, SOS is defined as the bud break date which, from a spectral perspective, will result in visible changes in canopy color, namely greenness.…”