2026-10-14T20:39:31Z https://www.scidb.cn/oai

10.57760/sciencedb.j00001.01523 2025-06-06T15:44:18Z

2025-06-06 A dataset of aboveground biomass carbon density in forests in Southeast China (2002-2017) doi:10.57760/sciencedb.j00001.01523 en The research used satellite observations from MODIS and SMOS to estimate aboveground carbon density changes in southern China from 2002 to 2017. A static benchmark map of carbon density for 2015 was used to train a machine learning algorithm applied to annual MODIS imagery to estimate carbon density changes. SMOS low-frequency passive microwave data at 25 kmx 25 km resolution was used to independently assess biomass changes and soil moisture trends from 2010-2017. The carbon density dataset covers southern China at 500 m x 500 m spatial resolution from 2002-2017. It was validated against independent tree cover data at 30 m and 5.6 km resolutions. Quality control involved comparing to high-resolution 2 m satellite imagery and forestry data. The dataset quantifies the carbon sequestration impact of different forest management strategies like afforestation, harvesting, and natural regrowth. It demonstrates southern China's forests offset 33% of regional fossil fuel emissions, with implications for climate change mitigation. Carbon density; eight south-western provinces of China; multi-source remote sensing; machine learning Yuan Shuai Tong Xiaowei Fu Wei Wang Kelin Tang Xinzhai PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.11922/11-6035.csd.2024.0130.zh http://www.doi.org/doi.org/10.1038/s41467-019-13798-8 Science Data Bank