Abstract
Remote-sensing phenology detection can compensate for deficiencies in field observations and has the advantage of capturing the continuous expression of phenology on a large scale. However, there is some variability in the results of remote-sensing phenology detection derived from different vegetation parameters in satellite time-series data. Since the enhanced vegetation index (EVI) and the leaf area index (LAI) are the most widely used vegetation parameters for remote-sensing phenology extraction, this paper aims to assess the differences in phenological information extracted from EVI and LAI time series and to explore whether either index performs well for all vegetation types on a large scale. To this end, a GLASS (Global Land Surface Satellite Product)-LAI-based phenology product (GLP) was generated using the same algorithm as the MODIS (Moderate Resolution Imaging Spectroradiometer)-EVI phenology product (MLCD) over China from 2001 to 2012. The two phenology products were compared in China for different vegetation types and evaluated using ground observations. The results show that the ratio of missing data is 8.3% for the GLP, which is less than the 22.8% for the MLCD. The differences between the GLP and the MLCD become stronger as the latitude decreases, which also vary among different vegetation types. The start of the growing season (SOS) of the GLP is earlier than that of the MLCD in most vegetation types, and the end of the growing season (EOS) of the GLP is generally later than that of the MLCD. Based on ground observations, it can be suggested that the GLP performs better than the MLCD in evergreen needleleaved forests and croplands, while the MLCD performs better than the GLP in shrublands and grasslands.
Highlights
Phenology is the study of the timing of recurring biological events, the causes of their timing with regard to biotic and abiotic forces, and the interactions among phases of the same or different species [1]
While enhanced vegetation index (EVI) and leaf area index (LAI) are the most widely used vegetation parameters that can be used for remote-sensing phenological extraction, this paper aims to assess the differences in phenological information extracted using the EVI and LAI time series and to determine whether either EVI or LAI time series performs well for all vegetation types over a large scale
The GLASS-LAI phenology product (GLP) and the MLCD have consistent patterns in the north of China, but inconsistent patterns are exhibited in the south
Summary
Phenology is the study of the timing of recurring biological events, the causes of their timing with regard to biotic and abiotic forces, and the interactions among phases of the same or different species [1]. Sprouting and flowering of plants in spring, color changes of leaves in fall, bird migration and nesting, insect hatching, and animal hibernation are all examples of phenological events [2]. Phenology is an important indicator of global change and the carbon cycle because of its direct effects on vegetation photosynthesis, carbon sequestration and land–atmosphere water and energy exchange [3]. Vegetation phenology indicates the responses of plants to seasonal and interannual variations of climate, hydrology, soil and anthropogenic factors [4]. Vegetation phenology affects the climate system by influencing the seasonality of albedo; surface roughness length; canopy conductance; and fluxes of water, energy, CO2 and biogenic volatile organic compounds [5]. The extraction of Sensors 2017, 17, 1982; doi:10.3390/s17091982 www.mdpi.com/journal/sensors
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