Persistent and widespread increase of vegetation cover, identified as greening, has been observed in areas of the planet over late 20th century and early 21st century by satellite-derived vegetation indices. It is difficult to verify whether these regions are net carbon sinks or sources by studying vegetation indices alone. In this study, we investigate greening trends in Eastern China (EC) and corresponding trends in atmospheric CO2 concentrations. We used multiple vegetation indices including NDVI and EVI to characterize changes in vegetation activity over EC from 2003 to 2016. Gap-filled time series of column-averaged CO2 dry air mole fraction (XCO2) from January 2003 toMay 2016, based on observations from SCIAMACHY, GOSAT, and OCO-2 satellites, were used to calculate XCO2 changes during growing season for 13 years. We derived a relationship between XCO2 and surface net CO2 fluxes from two inversion model simulations, CarbonTracker and Monitoring Atmospheric Composition and Climate (MACC), and used those relationships to estimate the biospheric CO2 flux enhancement based on satellite observed XCO2 changes. We observed significant growing period (GP) greening trends in NDVI and EVI related to cropland intensification and forest growth in the region. After removing the influence of large urban center CO2 emissions, we estimated an enhanced XCO2 drawdown during the GP of-0.070 to-0.084 ppm yr-1. Increased carbon uptake during the GP was estimated to be 28.41 to 46.04 Tg C, mainly from land management, which could offset about 2-3% of EC's annual fossil fuel emissions. These results show the potential of using multi-satellite observed XCO2 to estimate carbon fluxes from the regional biosphere, which could be used to verify natural sinks included as national contributions of greenhouse gas emissions reduction in international climate change agreements like the UNFCC Paris Accord.
CITATION STYLE
He, Z., Lei, L., Zeng, Z. C., Sheng, M., & Welp, L. R. (2020). Evidence of carbon uptake associated with vegetation greening trends in Eastern China. Remote Sensing, 12(4). https://doi.org/10.3390/rs12040718
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