Analysis method for influence factors of carbon dioxide concentration prediction at any time-space position

Abstract

The invention provides an analysis method for influence factors of carbon dioxide concentration prediction at any time-space position. The method comprises the following steps of: on the basis of sparse and non-uniform satellite carbon dioxide column concentration observation data, corresponding environmental factors including a ground cover factor, a climate and weather factor and a combustion emission factor, and an XGBoost algorithm, constructing a model for simulating spatial and temporal distribution of carbon dioxide in a region, and extracting carbon dioxide temporal and spatial distribution trend variables; and realizing quantitative evaluation of environmental factor sensitivity by utilizing the constructed regional carbon dioxide spatial and temporal distribution model and a global sensitivity analysis algorithm, and quantitatively determining influence degrees and magnitudes of various environmental factors influencing regional carbon dioxide spatial and temporal distribution according to a sensitivity analysis result. Compared with a traditional method, the method can simulate regional carbon dioxide concentration distribution with high precision, and achieve quantitative evaluation of the importance degrees of the environmental variables.

Description

technical field [0001] The invention relates to the field of remote sensing greenhouse gas monitoring, in particular to a machine learning-based method for simulating the temporal and spatial distribution of atmospheric carbon dioxide and a global sensitivity analysis method for influencing factors. Background technique [0002] Carbon dioxide is the most important greenhouse gas and the biggest contributor to global warming. Accurately understanding the temporal and spatial distribution of carbon dioxide concentration and its changing trend is very important for understanding and mitigating the greenhouse effect. Satellite observations can accurately provide ground-level carbon dioxide information with a certain temporal and spatial resolution and relatively long time-series observations. However, due to the influence of clouds and aerosols, there are vacancies in satellite observation data in practical applications, and it is difficult to accurately analyze the spatial and ...

AI Technical Summary

Problems solved by technology

The influencing factors are complex and the process is complicated, and there are many related studies

However, most of them are qualitative analysis or correlation analysis between environmental factors and carbon dioxide concentration, and there are few quantitative evaluation methods for the degree of influence of multiple factors, and it is impossible to realize quantitative analysis of the contribution and impact of different environmental factors on atmospheric carbon dioxide concentration.

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