Abstract
Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R2 values were generally low (0.01–0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.
Highlights
Phenology is a key and sensitive indicator of a terrestrial ecosystem’s response to climate change through its contributions to global carbon, water, and energy cycles
The performances of the vegetation indices (VIs) such as normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), and plant phenological index (PPI) to track canopy photosynthesis varied across different landscapes in the pan-Arctic (Figure 2)
Our current results highlighted the existence of relationships between remotely sensed and gross primary productivity (GPP) based estimations of phenological metrics, but these were inconsistent among the different indices
Summary
Phenology is a key and sensitive indicator of a terrestrial ecosystem’s response to climate change through its contributions to global carbon, water, and energy cycles. Land surface phenology (LSP), which was defined as the study of the timing of recurring seasonal pattern of variation in vegetated land surfaces observed from synoptic sensors [5], is measurable by remote sensing methods [6,7,8]. Productive efforts have been made to improve the estimates of phenological transitions based on remote sensing observations [12,14,15,16], using new methods such as digital cameras [8,17], developing improved vegetation indices, and applying remotely sensed solar-induced fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2) and the Orbiting Carbon Observatory-2 (OCO-2) [18,19,20]
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