Machine-learning reconstruction of high-resolution XCO2 over Thailand (2015–2024) using integrated satellite and environmental data

Udomphan Nacksriphod

doi:10.1016/j.atmosenv.2026.122214

Machine-learning reconstruction of high-resolution XCO2 over Thailand (2015–2024) using integrated satellite and environmental data

2026-07-06

0

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AI

U

Udomphan Nacksriphod

K

Korntip Tohsing *

P

Pramet Kaewmesri

I

Itsara Masiri

S

Sumaman Buntoung

S

Somjet Pattarapanitchai

DOI：10.1016/j.atmosenv.2026.122214

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• Machine learning reconstructed monthly XCO2 at 0.05° resolution over Thailand during 2015–2024. • Random Forest outperformed five other models, with R2 = 0.979 and RMSE = 1.074 ppm. • Northern Thailand showed higher XCO2 associated with biomass burning and land use. • Summer peaks and rainy-season minima reflect seasonal biospheric CO2 uptake. • The 10-year record reveals a sustained decadal increase in CO2 over Thailand.

Machine-learning reconstruction of high-resolution XCO2 over Thailand (2015–2024) using integrated satellite and environmental data

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