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Progress in Remote Sensing of Low-Altitude Wind Field Detection

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 603

Special Issue Editors


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Guest Editor
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: remote sensing; coherent Doppler lidar; wind camera
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
Interests: cloud radar and its application; remote sensing of cloud and precipitation properties; zenithal meteorological radar and its application; Doppler wind lidar and its application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The low-altitude atmosphere is a complex and highly variable environment, where phenomena such as wind shear, microburst, and turbulence pose significant challenges and safety risks. The accurate detection of low-altitude wind fields is critically important for a wide range of fields, including aviation safety, wind energy, and meteorological forecasting. Furthermore, as the low-altitude economy develops rapidly, the importance of low-altitude wind field detection has become even more prominent.

This Special Issue calls for original research, reviews, methodology papers, and case studies that demonstrate progress in remote sensing of low-altitude wind field detection. This Special Issue’s scope includes, but is not limited to, the following topics:

  • Advances in wind detection instruments, such as wind lidar, weather radars, and UAV-mounted wind sensors.
  • Machine learning algorithms for wind field retrieval and forecasting.
  • Method and model of 2D/3D wind retrieval.
  • Fusion and assimilation of multi-source wind data from multiple platforms.
  • Typical applications of low-altitude wind field remote sensing.
  • Identification and assessment of hazardous weather in low-altitude atmosphere.
  • Methods for retrieval of multiple meteorological parameters in low-altitude atmosphere.
  • Progress in boundary-layer meteorological detection.

We look forward to receiving your original research to foster a deeper understanding of the low-altitude wind environment.

Dr. Jinlong Yuan
Dr. Jiafeng Zheng
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. Remote Sensing 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 2700 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

  • wind detection
  • low-altitude economy
  • remote sensing instrument
  • wind shear
  • turbulence
  • machine learning

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Published Papers (2 papers)

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Research

18 pages, 11045 KB  
Article
Characteristics of the Wind Field and Low-Level Jets in the Middle and Lower Troposphere over Chengdu, Southwest China
by Tao Du, Chen Wang, Xiaoyu Hu, Pengfei Tian, Yan Ren, Yunfan Song and Jiajing Du
Remote Sens. 2026, 18(11), 1744; https://doi.org/10.3390/rs18111744 - 29 May 2026
Abstract
Low-level jets (LLJs) play an important role in the transport of heat, water vapor, and atmospheric pollutants. Based on one year (1 September 2023 to 31 August 2024) of tropospheric wind profiler radar (RWP) observations at the Wenjiang Meteorological Observation Base in Chengdu, [...] Read more.
Low-level jets (LLJs) play an important role in the transport of heat, water vapor, and atmospheric pollutants. Based on one year (1 September 2023 to 31 August 2024) of tropospheric wind profiler radar (RWP) observations at the Wenjiang Meteorological Observation Base in Chengdu, this study systematically investigates the wind field structure in the middle and lower troposphere over the Chengdu region and the vertical distribution and evolution characteristics of LLJs. The effective detection height of the RWP reaches at least 7.4 km throughout the year, demonstrating good consistency with concurrent radiosonde data. Horizontal wind speed accelerates markedly above 3 km, with the strongest vertical gradient observed in winter. In the lower layer, the prevailing wind direction is primarily controlled by mountain-valley breezes; with increasing altitude, the westerly belt gradually becomes the dominant wind system. Within the atmospheric boundary layer (below 1 km), the wind field exhibits a distinct diurnal cycle: easterly winds dominate in the afternoon, shifting to northerly winds at night. Surface wind speed peaks in the afternoon, whereas upper-level wind speed peaks at night. The occurrence frequency of LLJs is highest in July (26.3% for LLJ-1), followed by April (17.8%). The prevailing wind directions of LLJs are north-northeasterly and northeasterly, and jet core heights are mainly distributed between 0.7 and 1.9 km. For weaker LLJs (LLJ-1 and LLJ-2), both frequency and intensity are higher at night than during the day, peaking at 22:00. These findings deepen our understanding of boundary layer dynamics over complex basin terrain and provide a high-resolution observational benchmark for model improvements and weather warnings. Full article
(This article belongs to the Special Issue Progress in Remote Sensing of Low-Altitude Wind Field Detection)
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22 pages, 1569 KB  
Article
Miniaturized Coherent Doppler Wind Lidar with Self-Compensating Harris Hawks Optimization Algorithm for Low-Altitude UAV-Borne Wind Sensing
by Xu Zhang, Zhifeng Lin, Ran Wang, Siyuan Hu, Yiyang Zheng, Di Mo and Changjun Ke
Remote Sens. 2026, 18(11), 1739; https://doi.org/10.3390/rs18111739 - 28 May 2026
Abstract
With the rapid development of low-altitude UAVs, accurate wind detection is crucial for ensuring flight safety and enabling broader applications. To address this need, this paper introduces a highly integrated CDWL system specifically designed for compact UAV platforms. The system incorporates a self-compensating [...] Read more.
With the rapid development of low-altitude UAVs, accurate wind detection is crucial for ensuring flight safety and enabling broader applications. To address this need, this paper introduces a highly integrated CDWL system specifically designed for compact UAV platforms. The system incorporates a self-compensating Harris Hawks Optimization (SC-HHO) retrieval algorithm, which is tailored to the high-dynamic flight environment and stringent payload constraints of UAVs. This algorithm enables real-time wind retrieval with low dependence on external reference data while effectively compensating for platform motion. The performance of the proposed system was validated through the comparative experiment and the UAV-borne experiment. In the comparative experiment, the CDWL showed correlation coefficients above 0.976 in horizontal wind speed and 0.987 in horizontal wind direction relative to a benchmark airborne CDWL system, with corresponding root-mean-square errors better than 0.395 m/s and 4.135°, respectively. During the UAV-borne experiment, the CDWL retrieved platform velocity using the self-compensating mechanism, achieving a standard deviation of 0.080 m/s relative to global navigation satellite system (GNSS) measurements, and successfully acquired wind field information. These results confirm that the developed system provides a viable and practical technical solution for UAV-based remote wind sensing. Full article
(This article belongs to the Special Issue Progress in Remote Sensing of Low-Altitude Wind Field Detection)
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