Publisher Correction: Large Chinese land carbon sink estimated from atmospheric carbon dioxide data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Large Chinese land carbon sink estimated from atmospheric carbon dioxide data.

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO2) emissions and on the removal of CO2 by land carbon sinks. China is currently the single largest emitter of CO2, responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 20171. Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage2-4, which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO2, measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of -1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions5,6, with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper7. Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions.

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To promote the application of domestic high-resolution satellite data in large-scale carbon storage estimation and measurement, a total of 206 high-resolution remote sensing images covering Hunan Province were used as the data source, and the estimated minimum unit was fixed as a 0.06 hm2 square composed of multiple pixels. Through the establishment and purification of the interpretation marks, in the extraction of forest information, the pixel-based method and object-oriented classification method were used to compare. In the estimation of carbon storage of arbor forest, the robust estimate, partial least squares method and k-NN estimate were used to compare. Finally, we estimated forest carbon storage in Hunan Province and generated the distribution map of carbon density levels. The results showed that the interpretation mark based on the automatic extraction of plots could increase the extraction accuracy of arbor forest after purification. For the estimation of forest carbon storage at large-scale, the k-NN algorithm embodied a large advantage in forest information extraction and arbor forest carbon storage modeling. The average classification accuracy of the 206 scene images was 76.8%, the average RMSE was 8.95 t·hm-2, the average RRMSE was 19.1%, and the total carbon stock in Hunan Province was 22.28 Mt. The results provided effective reference for the estimation and measurement of forest carbon storage at the provincial and national scales.