Assessment of Vegetation Phenological Extractions Derived From Three Satellite-Derived Vegetation Indices Based on Different Extraction Algorithms Over the Tibetan Plateau

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.

The ecological value of Neotropical forest landscapes through a multicriteria approach employing spatial models

“…To exclude areas that lack vegetation over time (e.g. cities, croplands, bare spots, and snow) showing low VSI values (An et al, 2021), the VSI raster was clipped using the binary raster used for fragmentation. Finally, resilience was calculated as R = 100 - VSI, considering the complement of sensitivity as a proxy for resilience (Long et al, 2018; Seddon et al, 2016).…”

Spatial–Temporal Correlations between Soil pH and NPP of Grassland Ecosystems in the Yellow River Source Area, China

“…The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance products (MOD09A1) and MODIS NPP products (MOD17A2) were used in this study with a spatial resolution of 500 m and intervals of 8 days, which can be downloaded from the United States Geological Survey (USGS) ( ) (accessed on 4 March 2022). The MOD09A1 product provided a systematic correction for atmospheric factors including aerosols, gases, and Rayleigh scattering to estimate the surface spectral reflectance of seven bands (referred as B1∼B7) [ 27 , 28 ].…”