TY  - Data
T1  - China Full Coverage XCO2 Daily Dataset Based on DSC-DF-LGB (2015-2020)
A1  - None
DO  - 10.5281/zenodo.13352556
PY  - 2025
DA  - 2025-01-24
PB  - National Cryosphere Desert Data Center
AB  - In recent years, the concentration of atmospheric carbon dioxide in China has been increasing year by year. Satellite observation is the main means of obtaining atmospheric carbon dioxide concentration. However, currently, spaceborne sensors used to measure atmospheric carbon dioxide have a narrow observation range and cannot obtain spatially and temporally continuous atmospheric carbon dioxide concentrations. Therefore, this dataset proposes a daily full coverage XCO2dataset generation method based on the DSC-DF-LGB (Deep Separable Convolutional Neural Network and Deep Forest concatenated with LightGBM) model to obtain the spatiotemporal distribution of atmospheric carbon dioxide in China. The purpose of establishing the DSC-DF-LGB model is to train the mapping relationship between OCO-2 XCO2retrieval and related variables (reanalysis of XCO2, vegetation parameters, human factors, altitude, and meteorological parameters). The model was used to generate a daily 0.1 ° full coverage XCO2 dataset in China from 2015 to 2020. The XCO2dataset with full coverage and high resolution can provide data support for carbon source and sink research
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/47111df7-d758-42f9-9ebb-3a53fcd41113
ER  -