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• W3024408228 abstract "In this article, we generate a regional mapping of space-borne carbon dioxide (CO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> ) 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 (XCO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> ) 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 XCO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> 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 CO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> (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 XCO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> 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 XCO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> . This article is one of the initial attempts to generate Level-3 XCO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> mapping of OCO-2 through a data fusion approach using emission data sets." @default.
• W3024408228 created "2020-05-21" @default.
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• W3024408228 date "2020-12-01" @default.
• W3024408228 modified "2023-10-02" @default.
• W3024408228 title "Prediction of Satellite-Based Column CO₂ Concentration by Combining Emission Inventory and LULC Information" @default.
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• W3024408228 doi "https://doi.org/10.1109/tgrs.2020.2985047" @default.
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