A Review of Anthropogenic Ground-Level Carbon Emissions Based on Satellite Data

Abstract

The severity of the global warming issue emphasizes the critical importance of utilizing carbon satellite data to estimate ground-level carbon dioxide emissions. However, existing reviews have not kept pace with the latest research developments. Therefore, this article provides an overview of relevant work in the global carbon emissions field to address this knowledge gap. Through visual analysis using Citespace software, the article outlines two methods for quantifying carbon dioxide: 1) ground-level observations; and 2) satellite remote sensing. Despite the unique advantages of ground-level observations, satellite remote sensing is crucial for its extensive spatial coverage and long-term continuity in understanding carbon cycling, drawing significant attention. In addition, the article integrates the application of machine learning in the carbon emissions field, dividing it into two parts: Direct estimation based on ground emission inventory data and estimation of ground-level carbon emissions based on carbon satellite data. This innovative approach combines satellite observational data with ground data to accurately estimate the current ground-level carbon emissions with robust spatial distribution characteristics.

Keywords

• Carbon satellite
• machine learning
• retrieval algorithm
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