Prediction of Satellite-Based Column CO₂Concentration by Combining Emission Inventory and LULC Information

In this article, we generate a regional mapping of space-borne carbon dioxide (CO2) concentration through a data fusion approach, including emission estimates and Land Use and Land Cover (LULC) information. NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite measures the column-averaged CO2 dry air mole fraction (XCO2) as contiguous parallelogram footprints. A major hindrance of this data set, specifically with its Level-2 observations, is missing footprints at certain time instants and the sparse sampling density in time. This article aims to generate Level-3 XCO2 maps on a regional scale for different locations worldwide through spatial interpolation of the OCO-2 retrievals. To deal with the sparse OCO-2 sampling, the cokriging-based spatial interpolation methods are suitable, which models auxiliary densely-sampled variables to predict the primary variable. In this article, a cokriging-based approach is applied using auxiliary emission data sets and the principles of the semantic kriging (SemK) method. Two global high-resolution emission data sets, the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) and the Emissions Database for Global Atmospheric Research (EDGAR), are used here. The ontology-based semantic analysis of the SemK method quantifies the interrelationships of LULC classes for analyzing the local XCO2 pattern. Validations have been carried out in different regions worldwide, where the OCO-2 and the Total Carbon Column Observing Network (TCCON) measurements coexist. It is observed that the modeling of auxiliary emission data sets enhances the prediction accuracy of XCO2. This article is one of the initial attempts to generate Level-3 XCO2 mapping of OCO-2 through a data fusion approach using emission data sets.