Harmonizing atmospheric ozone column concentrations over the Tibetan Plateau from 2005 to 2022 using OMI and Sentinel-5P TROPOMI: A deep learning approach

作者：Changjiang Shi, Zhijie Zhang, Shengqing Xiong, Wangang Chen, Wanchang Zhang, Qian Zhang, Xingmao Wang · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.103808 · 被引用次数：9 · 研究领域：Atmospheric Ozone and Climate、Atmospheric and Environmental Gas Dynamics、Air Quality Monitoring and Forecasting

Atmospheric ozone plays a pivotal role in Earth's climate system, influencing solar radiation absorption in the stratosphere and regulating ultraviolet light reaching the surface. Accurate monitoring of ozone concentration is crucial for environmental assessments, air quality monitoring, and climate change studies. The Ozone Monitoring Instrument (OMI) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) provide valuable data for such monitoring. While OMI offers a long data record since 2004, but its effectiveness is hindered by its limitations in spatial resolution and signal-to-noise ratio, stemming from satellite hardware and retrieval algorithms. Sentinel-5P TROPOMI provides higher spatial resolution and improved signal-to-noise ratio, nevertheless, data record from it is rather short. Harmonizing these two datasets by taking the best use of their specific advantages is essential for creating a comprehensive and accurate atmospheric ozone concentration dataset. To maximize the advantages of these multi-source data products, our method utilizes a neural network to learn the mapping relationship between OMI and Sentinel-5P TROPOMI ozone column concentration products, constructing a harmonized model that optimizes the spatial and temporal sequence of historical OMI ozone column concentrations while considering topographic factors. The reconstructed ozone column concentration product is a long time series with the high spatial resolution and accuracy charact...