Abstract
The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.
Highlights
The vegetation phenology refers to the physiological and reproductive phenomenon of vegetation in an annual cycle, which is a robust and sensitive indicator of climate change [1,2,3,4]
PL, AG, polynomial curve function fitting (PCF), Linear, and Spline are piecewise logistic function fitting, asymmetric Gaussian function fitting, polynomial curve fitting, linear interpolation, and cubic spline interpolation, respectively; DT, MRC, and RCC are dynamic threshold, maximum rate of change, and change rate of curvature, respectively; the experiment results of bold variables are tested by the paired sample t-test
For vegetation types with apparent seasonal changes such as DBF, even though most time interpolation methods had significant effects on phenology trend estimation, the phenology trends from few specific combinations (i.e., polynomial curve function fitting and maximum rate of change based on the 16-day normalized difference vegetation index (NDVI) composite data in start of growing season (SOS) (Figure 5a), asymmetric Gaussian function fitting, and dynamic threshold 30% based on the 8-day NDVI composite data in end of growing season (EOS) (Figure 5e)) still showed no significant differences compared with the trends from daily NDVI data
Summary
The vegetation phenology refers to the physiological and reproductive phenomenon of vegetation in an annual cycle, which is a robust and sensitive indicator of climate change [1,2,3,4]. The accuracy of phenology trend estimation can be influenced by multiple variables such as geographical regions [20,21,22], vegetation types [23,24,25], and vegetation indexes [26,27,28] but mostly depends on the selection of remote sensing products, denoising methods, phenology extraction methods, and the different combinations of these factors [20,29]. Compared with denoising methods or extraction methods, the selection of datasets might be of a higher priority in vegetation dynamics monitoring [8,33] Atmosphere conditions, such as cloud, dust, and other aerosols, can adversely affect the quality of satellite remote sensing VI data. We analyzed the differences between the trends estimated from the 8-day and 16-day composite data, which would provide instructions on selecting relatively coarse temporal resolution (i.e., 16 day) data for phenology dynamics monitoring, as they are easier for collecting and storing
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