Dynamics and Drivers of Vegetation Phenology in Three-River Headwaters Region Based on the Google Earth Engine

Abstract

Phenology shifts over time are known as the canary in the mine when studying the response of terrestrial ecosystems to climate change. Plant phenology is a key factor controlling the productivity of terrestrial vegetation under climate change. Over the past several decades, the vegetation in the three-river headwaters region (TRHR) has been reported to have changed greatly owing to the warming climate and human activities. However, uncertainties related to the potential mechanism and influence of climatic and soil factors on the plant phenology of the TRHR are poorly understood. In this study, we used harmonic analysis of time series and the relative and absolute change rate on Google Earth Engine to calculate the start (SOS), end (EOS), and length (LOS) of the growing season based on MOD09A1 datasets; the results were verified by the observational data from phenological stations. Then, the spatiotemporal patterns of plant phenology for different types of terrain and basins were explored. Finally, the potential mechanism involved in the influence of climatic and soil factors on the phenology of plants in the TRHR were explored based on the structural equation model and Pearson’s correlation coefficients. The results show the remotely sensed monitoring data of SOS (R2 = 0.84, p < 0.01), EOS (R2 = 0.72, p < 0.01), and LOS (R2 = 0.86, p < 0.01) were very similar to the observational data from phenological stations. The SOS and LOS of plants possessed significant trends toward becoming advanced (Slope < 0) and extended (Slope > 0), respectively, from 2001 to 2018. The SOS was the earliest and the LOS was the longest in the Lancang River Basin, while the EOS was the latest in the Yangtze River Basin owing to the impact of climate change and soil factors. Meanwhile, the spatial patterns of SOS, EOS, and LOS have strong spatial heterogeneity at different elevations, slopes, and aspects. In addition, the results show that the drivers of plant phenology have basin-wide and stage differences. Specifically, the influence of soil factors on plant phenology in the Yangtze River Basin was greater than that of climatic factors, but climatic factors were key functional indicators of LOS in the Yellow and Lancang river basins, which directly or indirectly affect plant LOS through soil factors. This study will be helpful for understanding the relationship between the plant phenology of the alpine wetland ecosystem and climate change and improving the level of environmental management.

Specific Drivers and Responses to Land Surface Phenology of Different Vegetation Types in the Qinling Mountains, Central China

“…Previous studies showed that the interaction of meteorological, soil, and biological factors influenced the interannual variability of LSP [6,54]. Our results suggest that SWP is the most important driver of interannual variations in SOS and EOS across the QMs (Figure 8).…”

Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Tibetan Plateau from 1979 to 2018

“…Soil moisture content is directly influenced by precipitation and plays a crucial role in determining plant available water capacity and photosynthetic processes [63]. At the same time, adequate soil moisture ensures nutrient transport and accelerates the mineralization of organic matter in the soil [64,65].…”

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

“…The simple Kriging method was used to interpolate the meteorological data to raster data with a spatial resolution of 500 m. We selected the MODIS dataset of GPP products (MOD17A2H006) from the National Aeronautics and Space Administration (NASA) Earth Observation System Data and Information System (https://www.earthdata.nasa.gov, accessed on 10 June 2021). The data period is from 2001 to 2019, with a spatial resolution of 500 m and a temporal resolution of 8 d. The MODIS GPP dataset was developed based on the Light Use Efficiency model, and its reliability has been validated in various studies [47][48][49] and has been used in studies in the QPT [45]. Seasonal GPP for spring (March to May), summer (June to August), autumn (September to November) and winter (December to February) were synthesized based on 8-day GPP data, respectively.…”