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Atmospheric Optical Remote Sensing
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Atmospheric Optical Remote Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1467

Special Issue Editor

Prof. Dr. Chunmin Zhang

E-Mail Website
Guest Editor
The Institute of Space Optics, Xi’an Jiaotong University, Xi’an 710049, China
Interests: atmospheric optical remote sensing; spectral imaging technology; design and manufacture of state-of-the-art optical instrument; data and image processing; application of optical techniques in remote sensing

Special Issue Information

Dear Colleagues,

The Special Issue “Atmospheric Optical Remote Sensing” serves as a platform to showcase cutting-edge research and advancements in the field of atmospheric science, particularly focusing on the utilization of optical remote sensing techniques. We welcome submissions covering a wide range of topics related to optical remote sensing techniques and their applications in atmospheric studies. Atmospheric optical remote sensing plays a pivotal role in understanding the complex dynamics of the Earth’s and planetary atmospheres, including the behavior of aerosols, clouds, and greenhouse gases. Through the integration of optical instruments and methodologies, researchers can gather valuable insights into atmospheric composition, dynamics, and processes. This issue aims to showcase innovative methodologies, advanced instrumentation, and novel algorithms for retrieving crucial atmospheric parameters. Additionally, we encourage submissions that explore the integration of ground-based, airborne, and satellite remote sensing data to provide a comprehensive understanding of atmospheric processes. Contributions may include original research articles, reviews, and case studies demonstrating the practical utility of optical remote sensing in environmental monitoring, weather forecasting, and climate research. Through this Special Issue, we seek to foster collaboration among researchers and advance knowledge in atmospheric science by leveraging the capabilities of optical remote sensing technologies.

Prof. Dr. Chunmin Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • atmospheric remote sensing
  • atmospheric optical imaging
  • atmospheric optical properties
  • optical remote sensing data processing
  • climate monitoring

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Published Papers (2 papers)

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Research

23 pages, 998 KiB  
Article
Bayesian Adaptive Extended Kalman-Based Orbit Determination for Optical Observation Satellites
by Yang Guo, Qinghao Pang, Xianlong Yin, Xueshu Shi, Zhengxu Zhao, Jian Sun and Jinsheng Wang
Sensors 2025, 25(8), 2527; https://doi.org/10.3390/s25082527 - 17 Apr 2025
Viewed by 224
Abstract
As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data [...] Read more.
As the number of satellites and amount of space debris in Low-Earth orbit (LEO) increase, high-precision orbit determination is crucial for ensuring the safe operation of spacecraft and maintaining space situational awareness. However, ground-based optical observations are constrained by limited arc-segment angular data and dynamic noise interference, and the traditional Extended Kalman Filter (EKF) struggles to meet the accuracy and robustness requirements in complex orbital environments. To address these challenges, this paper proposes a Bayesian Adaptive Extended Kalman Filter (BAEKF), which synergistically optimizes track determination through dynamic noise covariance adjustment and Bayesian a posteriori probability correction. Experiments demonstrate that the average root mean square error (RMSE) of BAEKF is reduced by 34.7% compared to the traditional EKF, effectively addressing EKF’s accuracy and stability issues in nonlinear systems. The RMSE values of UKF, RBFNN, and GPR also show improvement, providing a reliable solution for high-precision orbital determination using optical observation. Full article
(This article belongs to the Special Issue Atmospheric Optical Remote Sensing)
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13 pages, 4333 KiB  
Article
Free-Space to SMF Integration and Green to C-Band Conversion Based on PPLN
by Takahiro Kodama, Kiichiro Kuwahara, Ayumu Kariya and Rikizo Ikuta
Sensors 2024, 24(24), 8162; https://doi.org/10.3390/s24248162 - 21 Dec 2024
Viewed by 799
Abstract
In this study, we experimentally demonstrate a PPLN-based free-space to SMF (single-mode fiber) conversion system capable of efficient long-wavelength down-conversion from 518 nm, optimized for minimal loss in highly turbid water, to 1540 nm, which is ideal for low-loss transmission in standard SMF. [...] Read more.
In this study, we experimentally demonstrate a PPLN-based free-space to SMF (single-mode fiber) conversion system capable of efficient long-wavelength down-conversion from 518 nm, optimized for minimal loss in highly turbid water, to 1540 nm, which is ideal for low-loss transmission in standard SMF. Leveraging the nonlinear optical properties of periodically poled lithium niobate (PPLN), we achieve a wavelength conversion efficiency of 1.6% through difference frequency generation while maintaining a received optical signal-to-noise ratio of 10.4 dB. Our findings underscore the potential of integrating PPLN-based wavelength conversion with fiber optic networks, offering a viable solution for next-generation optical sensor systems that demand real-time, low-latency, and reliable data transmission. This work represents a significant advancement in developing robust and efficient optical sensor technologies, addressing the challenges associated with long-distance transmission and broad-linewidth light sources in optical remote sensing applications. Full article
(This article belongs to the Special Issue Atmospheric Optical Remote Sensing)
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