Articles | Volume 26, issue 20
https://doi.org/10.5194/acp-26-14793-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-26-14793-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2026
Research article |
| 21 Oct 2026
| 21 Oct 2026
Assessing the consistency of XCO2 products from DQ-1 ACDL and passive satellites using a unified comparison framework
Yike Wang
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Ge Han
CORRESPONDING AUTHOR
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Perception and Effectiveness Assessment for Carbon-neutrality Efforts, Engineering Research Center of Ministry of Education, Institute for Carbon Neutrality, Wuhan University, Wuhan, China
Hongyuan Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No.129, Wuhan 430079, China
Yiyang Huang
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Kangjie Zhou
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Wei Gong
Electronic Information School, Wuhan University, Wuhan, China
Wuhan Institute of Quantum Technology, Wuhan, China
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Huayi Wang, Ge Han, and Zhipeng Pei
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B3-2026, 1471–1476, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-1471-2026, https://doi.org/10.5194/isprs-archives-XLIX-B3-2026-1471-2026, 2026
Jinchun Yi, Yiyang Huang, Ge Han, Hongyuan Zhang, Zhipeng Pei, Haotian Luo, Yichi Zhang, Tianqi Shi, Siwei Li, and Wei Gong
Atmos. Chem. Phys., 26, 8475–8504, https://doi.org/10.5194/acp-26-8475-2026, https://doi.org/10.5194/acp-26-8475-2026, 2026
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This study presents a new way to estimate human-caused carbon dioxide emissions from cities using satellite observations. It combines measurements of nitrogen dioxide and carbon dioxide to better track emissions from urban areas. By applying this approach to several major cities with different environments, the study shows that combining multiple satellite observations can reduce uncertainty and provide more reliable city-level emission estimates.
Zhe Tong, Boming Liu, Xin Ma, Jianping Guo, Haowei Zhang, Haoyu Dong, Ge Han, Yingying Ma, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-73, https://doi.org/10.5194/essd-2026-73, 2026
Revised manuscript accepted for ESSD
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Near-surface wind is crucial for weather and wind energy studies. This work uses physically constrained machine learning combined with satellite wind observations to generate a wind speed profile dataset within the global atmospheric boundary layer. The dataset accurately depicts the spatial distribution and variation of global wind speeds with improved accuracy, finer vertical detail, and reduced data gaps, supporting boundary-layer meteorology, climate studies, and wind energy applications.
Wanqin Zhong, Xin Ma, Yingxu Wu, Chenglong Li, Tianqi Shi, Wei Gong, and Di Qi
Earth Syst. Sci. Data, 17, 7169–7201, https://doi.org/10.5194/essd-17-7169-2025, https://doi.org/10.5194/essd-17-7169-2025, 2025
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This work addresses a critical observational gap in the Southern Ocean — one of the most important regions for carbon uptake — by integrating comprehensive Argo float observations with historical ship-based measurements. Our findings demonstrate the feasibility of using machine learning models to integrate observations, and support in-depth analyses of carbon transport and storage mechanisms. This can foster broader utilization of Argo floats data in ocean carbon research.
Jinchun Yi, Yiyang Huang, Zhipeng Pei, and Ge Han
Atmos. Chem. Phys., 25, 13687–13710, https://doi.org/10.5194/acp-25-13687-2025, https://doi.org/10.5194/acp-25-13687-2025, 2025
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The inventory overestimated emissions in Beijing and Riyadh by 10–20 % and underestimated emissions in Cairo by 10–30 %. The biosphere flux has a 20–40 % impact on emissions in Beijing during certain periods. Moreover, using night-time and day-time biosphere flux data separately can improve the simulation accuracy of the same orbit by 20–70 % compared to using daily average data.
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
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Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Shikuan Jin, Yingying Ma, Zhongwei Huang, Jianping Huang, Wei Gong, Boming Liu, Weiyan Wang, Ruonan Fan, and Hui Li
Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023, https://doi.org/10.5194/acp-23-8187-2023, 2023
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To better understand the Asian aerosol environment, we studied distributions and trends of aerosol with different sizes and types. Over the past 2 decades, dust, sulfate, and sea salt aerosol decreased by 5.51 %, 3.07 %, and 9.80 %, whereas organic carbon and black carbon aerosol increased by 17.09 % and 6.23 %, respectively. The increase in carbonaceous aerosols was a feature of Asia. An exception is found in East Asia, where the carbonaceous aerosols reduced, owing largely to China's efforts.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
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Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Tianqi Shi, Zeyu Han, Ge Han, Xin Ma, Huilin Chen, Truls Andersen, Huiqin Mao, Cuihong Chen, Haowei Zhang, and Wei Gong
Atmos. Chem. Phys., 22, 13881–13896, https://doi.org/10.5194/acp-22-13881-2022, https://doi.org/10.5194/acp-22-13881-2022, 2022
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CH4 works as the second-most important greenhouse gas, its reported emission inventories being far less than CO2. In this study, we developed a self-adjusted model to estimate the CH4 emission rate from strong point sources by the UAV-based AirCore system. This model would reduce the uncertainty in CH4 emission rate quantification accrued by errors in measurements of wind and concentration. Actual measurements on Pniówek coal demonstrate the high accuracy and stability of our developed model.
Shikuan Jin, Yingying Ma, Cheng Chen, Oleg Dubovik, Jin Hong, Boming Liu, and Wei Gong
Atmos. Meas. Tech., 15, 4323–4337, https://doi.org/10.5194/amt-15-4323-2022, https://doi.org/10.5194/amt-15-4323-2022, 2022
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Aerosol parameter retrievals have always been a research focus. In this study, we used an advanced aerosol algorithms (GRASP, developed by Oleg Dubovik) to test the ability of DPC/Gaofen-5 (the first polarized multi-angle payload developed in China) images to obtain aerosol parameters. The results show that DPC/GRASP achieves good results (R > 0.9). This research will contribute to the development of hardware and algorithms for aerosols
Haowei Zhang, Boming Liu, Xin Ma, Ge Han, Qinglin Yang, Yichi Zhang, Tianqi Shi, Jianye Yuan, Wanqi Zhong, Yanran Peng, Jingjing Xu, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-215, https://doi.org/10.5194/essd-2022-215, 2022
Preprint withdrawn
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Obtaining highly accurate and high-resolution spatiotemporal maps of carbon dioxide concentration distributions is crucial for promoting the study of the carbon cycle, and carbon emissions assessed by top-down theory. The official discrete satellite data provided by Gosat-2, OCO-2, and OCO-3 have data voids and relatively low efficiency. Here, we present carbon dioxide cover dataset, an innovative methodology to obtain XCO2 maps of high spatiotemporal resolution by using satellite data.
X. Xia, Z. Zhu, T. Zhang, G. Wei, and Y. Ji
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 545–550, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-545-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-545-2022, 2022
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
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Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Yingying Ma, Yang Zhu, Boming Liu, Hui Li, Shikuan Jin, Yiqun Zhang, Ruonan Fan, and Wei Gong
Atmos. Chem. Phys., 21, 17003–17016, https://doi.org/10.5194/acp-21-17003-2021, https://doi.org/10.5194/acp-21-17003-2021, 2021
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The vertical distribution of the aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter of less than 2.5 μm (PM2.5). However, the traditional linear model cannot consider the influence of multiple meteorological variables sufficiently, which then causes low inversion accuracy. In this study, the machine learning algorithms which can input multiple features are used to solve this constraint.
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Short summary
This study establishes a unified framework to assess the consistency of DQ-1 (Da Qi-1) ACDL (Aerosol and Carbon Dioxide Lidar) XCO2 with passive satellite products while considering sampling and column-definition differences. DQ-1 agrees well with TCCON (Total Carbon Column Observing Network) and shows consistent spatial patterns with OCO-2, OCO-3 (Orbiting Carbon Observatory), GOSAT, and GOSAT-2 (Greenhouse gases Observing SATellite) over selected regions. It provides complementary XCO2 observations where passive measurements are limited, including nighttime and high latitudes, supporting future joint carbon monitoring and inversion studies.
This study establishes a unified framework to assess the consistency of DQ-1 (Da Qi-1) ACDL...
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