Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models

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dc.contributor.authorPais, S.M.
dc.contributor.authorBhattacharjee, S.
dc.contributor.authorAnand Kumar, M.
dc.contributor.authorChen, J.
dc.date.accessioned2026-02-04T12:25:31Z
dc.date.issued2024
dc.description.abstractOne of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO<inf>2</inf>). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO<inf>2</inf> concentration. This work aims to map CO<inf>2</inf> concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO<inf>2</inf> dry mole fraction, called XCO<inf>2</inf>, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO<inf>2</inf> using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.
dc.identifier.citationIEEE Geoscience and Remote Sensing Letters, 2024, 21, , pp. 1-5
dc.identifier.issn1545598X
dc.identifier.urihttps://doi.org/10.1109/LGRS.2024.3379204
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/21433
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAnalytical models
dc.subjectData fusion
dc.subjectGreenhouse gases
dc.subjectInterpolation
dc.subjectLearning systems
dc.subjectOrbits
dc.subjectRemote sensing
dc.subjectSatellites
dc.subject>
dc.subjectComputational modelling
dc.subjectData resampling
dc.subjectDown-scaling
dc.subjectGap filling
dc.subjectKriging
dc.subjectLand cover
dc.subjectMachine-learning
dc.subjectODIAC
dc.subjectOrbiting carbon observatory-2
dc.subjectPredictive models
dc.subjectRegressor
dc.subjectResampling
dc.subjectSIF<sub xmlns:ali="
dc.subjectSpatial resolution
dc.subjectXCO<sub xmlns:ali="
dc.subjectXmlns:mml="
dc.subjectXmlns:xlink="
dc.subjectXmlns:xsi="
dc.subjectCarbon dioxide
dc.subjectartificial intelligence
dc.subjectcarbon cycle
dc.subjectdownscaling
dc.subjectestimation method
dc.subjecthuman activity
dc.subjectinterpolation
dc.subjectkriging
dc.subjectmachine learning
dc.subjectsatellite imagery
dc.subjectspatiotemporal analysis
dc.titleDownscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models

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