Detecting the Spatiotemporal Variation of Vegetation Phenology in Northeastern China Based on MODIS NDVI and Solar-Induced Chlorophyll Fluorescence Dataset

作者：Ruixin Zhang, Yuke Zhou, Tianyang Hu, Wenbin Sun, Shuhui Zhang, Jiapei Wu, Han Wang · 发表于：Sustainability · 年份：2023 · DOI：10.3390/su15076012 · 被引用次数：14 · 研究领域：Remote Sensing in Agriculture、Land Use and Ecosystem Services、Species Distribution and Climate Change

Vegetation phenology is a crucial biological indicator for monitoring changes in terrestrial ecosystems and global climate. Currently, there are limitations in using traditional vegetation indices for phenology monitoring (e.g., greenness saturation in high-density vegetation areas). Solar-induced chlorophyll fluorescence (SIF), a novel remote sensing product, has great potential in depicting seasonal vegetation dynamics across various regions with different vegetation covers and latitudes. In this study, based on the GOSIF and MODIS NDVI data from 2001 to 2020, we extracted vegetation phenological parameters in Northeastern China by using Double Logistic (D-L) fitting function and the dynamic threshold method. Then, we analyzed the discrepancy in phenological period and temporal trend derived from SIF and NDVI data at multiple spatiotemporal scales. Furthermore, we explored the response of vegetation phenology to climate change and the persistence of phenological trends (Hurst exponent) in Northeastern China. Generally, there is a significant difference in trends between SIF and NDVI, but with similar spatial patterns of phenology. However, the dates of key phenological parameters are distinct based on SIF and MODIS NDVI data. Specifically, the start of season (SOS) of SIF started later (about 10 days), and the end of season (EOS) ended earlier (about 36 days on average). In contrast, the fall attenuation of SIF showed a lag process compared to NDVI. This implies that the ac...