A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO2 Data over Taiwan

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 12; p. 2084
• Main Authors: Valerio, Alyssa, Chen, Yi-Chun, Liu, Chian-Yi, Chen, Yi-Ying, Lin, Chuan-Yao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.06.2025
• Subjects: Air pollution; Air quality; Algorithms; Climate change; data imputation; Datasets; Deep learning; Dry deposition; kriging; Land cover; Land use; Learning algorithms; Machine learning; Missing data; Neural networks; Nitrogen dioxide; Outdoor air quality; Partial differential equations; Pollutants; Quality assurance; Real time; Regions; Regression; Regression analysis; satellite data; TROPOMI; Winter; United States > US; Europe; China; Taiwan

This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO2 data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. The proposed approach combines geostatistic...