Abstract
Remote sensing phenology retrieval can remedy the deficiencies in field investigations and has the advantage of catching the continuous characteristics of phenology on a large scale. However, there are some discrepancies in the results of remote sensing phenological metrics derived from different vegetation indices based on different extraction algorithms, and there are few studies that evaluate the impact of different vegetation indices on phenological metrics extraction. In this study, three satellite-derived vegetation indices (enhanced vegetation index, EVI; normalized difference vegetation index, NDVI; and normalized difference phenology index, NDPI; calculated using surface reflectance data from MOD09A1) and two algorithms were used to detect the start and end of growing season (SOS and EOS, respectively) in the Tibetan Plateau (TP). Then, the retrieved SOS and EOS were evaluated from different aspects. Results showed that the missing rates of both SOS and EOS based on the Seasonal Trend Decomposition by LOESS (STL) trendline crossing method were higher than those based on the seasonal amplitude method (SA), and the missing rate varied using different vegetation indices among different vegetation types. Also, the temporal and spatial stabilities of phenological metrics based on SA using EVI or NDPI were more stable than those from others. The accuracy assessment based on ground observations showed that phenological metrics based on SA had better agreements with ground observations than those based on STL, and EVI or NDVI may be more appropriate for monitoring SOS than NDPI in the TP, while EOS from NDPI had better agreements with ground-observed EOS. Besides, the phenological metrics over the complex terrain also presented worse performances than those over the flat terrain. Our findings suggest that previous results of inter-annual variability of phenology from a single data or method should be treated with caution.
Highlights
Vegetation phenology can reflect the response of terrestrial ecosystem to climate change and is critical for understanding the effects of these changes on the carbon cycle (Zhang et al, 2004; Xie and Li, 2020a), water cycle (Yu et al, 2018), and energy exchange (Shen et al, 2014b) of terrestrial ecosystems
The SOS derived from different vegetation indices (VIs) using the same method have consistent patterns in the east, but inconsistent patterns were exhibited in the middle and northwest
It is shown that the missing rates of phenological metrics (Figure 3; Supplementary Figure S2) based on seasonal amplitude method (SA) for each VI were lower than those based on Seasonal Trend Decomposition by locally weighted regression (LOESS) (STL); as the vegetation index value was relatively lower in the Tibetan Plateau (TP), the seasonal trend line may not cross with part of the vegetation index time series curve and lead to missing of phenological metrics
Summary
Vegetation phenology can reflect the response of terrestrial ecosystem to climate change and is critical for understanding the effects of these changes on the carbon cycle (Zhang et al, 2004; Xie and Li, 2020a), water cycle (Yu et al, 2018), and energy exchange (Shen et al, 2014b) of terrestrial ecosystems. Many studies have explored other VIs to indicate the growing season transitions, such as normalized difference phenology index (NDPI), which is designed to best distinguish vegetation from the background (i.e., soil and snow) as well as to minimize the difference among the backgrounds (Wang et al, 2017a). These parameters provide more precise information on the phenological changes of vegetation and have been widely used because of the convenient acquisition of multiple remote sensing data and its indicative function of physical and biological processes related to vegetation dynamics at global and regional scales (Xie et al, 2021b)
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