Multisource Remote Sensing Data Fusion and Assimilation in Atmospheric Observations

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (30 March 2024) | Viewed by 1900

Special Issue Editors

Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
Interests: data assimilation; multisource data fusion; geostatistics; machine learning; spatio-temporal data analysis
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: spatiotemporal statistics; data assimilation; numerical optimization; environmental remote sensing; geoinformatics

Special Issue Information

Dear Colleagues,

This Special Issue mainly explores “advanced” or “novel” data fusion and data assimilation techniques for atmospheric observations concerning air pollution, air temperature, precipitation, wind, etc. The availability of multi-resolution remote sensing data has promoted the development of different data fusion and assimilation. The aim of this issue is to collect new ideas on data fusion, data assimilation, machine learning, geostatistics, and quality assessment. The authors will be able to express their creativity without restrictions and ensure the scientific rigor of research. This Special Issue is, therefore, intended to strongly encourage creative endeavors in theory and practice. We are looking for techniques that may bring challenges and lead to breakthroughs, for example, the new methods in the initial experimental stage that have made basic advancements and potentially contributed to a paradigm shift. Recent developments, applications, and evaluations of remote sensing and observation techniques for atmospheric observations are preferred. Required topics include but are not limited to:

  • Multi-source data fusion;
  • Data assimilation into physical simulation;
  • Machine learning;
  • Deep learning with interpretability;
  • Baysian statistics;
  • Geostatistics;
  • Microwave remote sensing;
  • Spatio-temporal analysis;
  • Multi-resolution analysis;
  • Quality assessment;
  • Model calibration;
  • Data validation.

Dr. Jianhui Xu
Dr. Hong Shu
Guest Editors

Manuscript Submission Information

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Keywords

  • data fusion of different atmospheric observations
  • data Assimilation
  • machine learning
  • deep learning
  • geostatistics
  • multi-resolution analysis
  • microwave remote sensing data
  • machine learning interpretability in data fusion
  • spatio-temporal analysis of atmospheric observations
  • atmosphere environmental quality assessment

Published Papers (2 papers)

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Research

24 pages, 26513 KiB  
Article
Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature
by Pinghao Wu, Jiacheng Liang, Jianhui Xu, Kaiwen Zhong, Hongda Hu and Jian Zuo
Atmosphere 2023, 14(12), 1813; https://doi.org/10.3390/atmos14121813 - 12 Dec 2023
Viewed by 665
Abstract
This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface [...] Read more.
This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface land surface temperature (LST), as indicated by the change detection difference map obtained using iteratively reweighted multivariate alteration detection (IR-MAD). The evaluation statistics revealed that the DE_CBST optimization scheme achieves superior change detection outcomes. In comparison to the results of Deeplab V3+, the precision indicator demonstrated a 2.5% improvement, while the commission indicator exhibited a reduction of 2.5%. Furthermore, when compared to those of the CBST framework, the F1 score showed a notable enhancement of 6.3%, and the omission indicator exhibited a decrease of 8.9%. Moreover, DE_CBST optimization improves the identification accuracy of water in unchanged areas on the basis of Deeplab V3+ classification results and significantly improves the classification effect on bare land in changed areas on the basis of CBST classification results. In addition, the following conclusions are drawn from the discussion on the correlation between ground object categories and LST on a fine-scale: (1) the correlation between land use categories and LST all have good results in GTWR model fitting, which shows that local LST has a high correlation with the corresponding range of the land use category; (2) the changes of the local LST were generally consistent with the changes of the overall LST, but the evolution of the LST in different regions still has a certain heterogeneity, which might be related to the size of the local LST region; and (3) the local LST and the land use category of the corresponding grid cells did not show a completely consistent correspondence relationship. When discussing the local LST, it is necessary to consider the change in the overall LST, the land use types around the region, and the degree of interaction between surface objects. Finally, future experiments will be further explored through more time series LST and land use data. Full article
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25 pages, 4882 KiB  
Article
Assimilation and Evaluation of the COSMIC–2 and Sounding Data in Tropospheric Atmospheric Refractivity Forecasting across the Yellow Sea through an Ocean–Atmosphere–Wave Coupled Model
by Sheng Wu, Jiayu Song, Jing Zou, Xiangjun Tian, Zhijin Qiu, Bo Wang, Tong Hu, Zhiqian Li and Zhiyang Zhang
Atmosphere 2023, 14(12), 1776; https://doi.org/10.3390/atmos14121776 - 30 Nov 2023
Viewed by 672
Abstract
In this study, a forecasting model was developed based on the COAWST and atmospheric 3D EnVar module to investigate the effects of assimilation of the sounding and COSMIC–2 data on the forecasting of the revised atmospheric refraction. Three groups of 72 h forecasting [...] Read more.
In this study, a forecasting model was developed based on the COAWST and atmospheric 3D EnVar module to investigate the effects of assimilation of the sounding and COSMIC–2 data on the forecasting of the revised atmospheric refraction. Three groups of 72 h forecasting tests, with assimilation of different data obtained for a period of one month, were constructed over the Yellow Sea. The results revealed that the bias of the revised atmospheric refraction was the lowest if both the sounding and COSMIC–2 data were assimilated. As a result of the assimilation of the hybrid data, the mean bias reduced by 6.09–6.28% within an altitude of 10 km, and the greatest reduction occurred below the altitude of 3000 m. In contrast, the test that assimilated only the sounding data led to an increase in bias at several levels. This increased bias was corrected after the introduction of the COSMIC–2 data, with the mean correction of 1.6 M within the middle and lower troposphere. During the typhoon period, the improvements in the assimilation were more significant than usual. The improved forecasts of the revised atmospheric refraction were mainly due to the moisture changes within the middle and lower troposphere, while the changes in the upper troposphere were influenced by multiple factors. Full article
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