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Remote Sensing
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Near Real-Time Remote Sensing Data and Its Geoscience Applications
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Near Real-Time Remote Sensing Data and Its Geoscience Applications

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 781

Special Issue Editors

Prof. Dr. John Trinder

E-Mail Website
Guest Editor
Surveying and Geospatial Engineering, School of Civil and Environmental Engineering, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: photogrammetry and remote sensing; geospatial information systems; SAR remote sensing; feature extraction from images; sustainable development; ecosystem services
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiqi Zhang

E-Mail Website
Guest Editor
School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Interests: real-time image processing; photogrammetry and remote sensing

Special Issue Information

Dear Colleagues,

Near real-time satellite remote sensing of the earth has become a reality since response times for receiving data from existing and future purpose-built satellites can be limited to minutes.  Multispectral image and video color image data with varying resolutions from intelligent satellite systems have become available for processing for a wide range of applications, including for aspects of the environment, including agricultural growth, production, disease assessment, disaster monitoring, including floods and wildfires, atmospheric monitoring for air quality and aerosols, weather forecasting, monitoring sea and ice conditions for shipping, fire email alerts, national security, near real-time road and rail traffic usage, environmental applications including the assessment of ecosystem services and contributing to economic growth of urban and suburban areas.  Onboard processing developments for intelligent satellite systems will involve AI technologies to provide high-level information to users.

This Special Issue will welcome contributions on the design of intelligent satellite systems for near-real-time purposes, data processing for applications of near real-time data acquisition from satellites, advances in onboard processing for provision of applications-ready data to users, algorithms for processing of near-real-time video and multispectral data for information services.

Prof. Dr. John Trinder
Dr. Zhiqi Zhang
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 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. 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

  • near real-time intelligent satellite systems
  • onboard processing
  • video data capture
  • multispectral and SAR image data
  • data processing for information servicing
  • deep learning applications
  • applications-ready data

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Published Papers (2 papers)

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Research

25 pages, 10128 KiB  
Article
Jitter Error Correction for the HaiYang-3A Satellite Based on Multi-Source Attitude Fusion
by Yanli Wang, Ronghao Zhang, Yizhang Xu, Xiangyu Zhang, Rongfan Dai and Shuying Jin
Remote Sens. 2025, 17(9), 1489; https://doi.org/10.3390/rs17091489 - 23 Apr 2025
Viewed by 184
Abstract
The periodic rotation of the Ocean Color and Temperature Scanner (OCTS) introduces jitter errors in the HaiYang-3A (HY-3A) satellite, leading to internal geometric distortion in optical imagery and significant registration errors in multispectral images. These issues severely influence the application value of the [...] Read more.
The periodic rotation of the Ocean Color and Temperature Scanner (OCTS) introduces jitter errors in the HaiYang-3A (HY-3A) satellite, leading to internal geometric distortion in optical imagery and significant registration errors in multispectral images. These issues severely influence the application value of the optical data. To achieve near real-time compensation, a novel jitter error estimation and correction method based on multi-source attitude data fusion is proposed in this paper. By fusing the measurement data from star sensors and gyroscopes, satellite attitude parameters containing jitter errors are precisely resolved. The jitter component of the attitude parameter is extracted using the fitting method with the optimal sliding window. Then, the jitter error model is established using the least square solution and spectral characteristics. Subsequently, using the imaging geometric model and stable resampling, the optical remote sensing image with jitter distortion is corrected. Experimental results reveal a jitter frequency of 0.187 Hz, matching the OCTS rotation period, with yaw, roll, and pitch amplitudes quantified as 0.905”, 0.468”, and 1.668”, respectively. The registration accuracy of the multispectral images from the Coastal Zone Imager improved from 0.568 to 0.350 pixels. The time complexity is low with the single-layer linear traversal structure. The proposed method can achieve on-orbit near real-time processing and provide accurate attitude parameters for on-orbit geometric processing of optical satellite image data. Full article
(This article belongs to the Special Issue Near Real-Time Remote Sensing Data and Its Geoscience Applications)
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18 pages, 4812 KiB  
Article
A Novel Aerosol Optical Depth Retrieval Method Based on SDAE from Himawari-8/AHI Next-Generation Geostationary Satellite in Hubei Province
by Shiquan Deng, Ting Bai, Zhe Chen and Yepei Chen
Remote Sens. 2025, 17(8), 1396; https://doi.org/10.3390/rs17081396 - 14 Apr 2025
Viewed by 175
Abstract
Atmospheric aerosols play an important role in the ecological environment, climate change, and human health. Aerosol optical depth (AOD) is the main measurement of aerosols. The next-generation geostationary satellite Himawari-8, loaded with the Advanced Himawari Imager (AHI), provides observation-based estimates of the hourly [...] Read more.
Atmospheric aerosols play an important role in the ecological environment, climate change, and human health. Aerosol optical depth (AOD) is the main measurement of aerosols. The next-generation geostationary satellite Himawari-8, loaded with the Advanced Himawari Imager (AHI), provides observation-based estimates of the hourly AOD. However, a highly accurate evaluation of AOD using AHI is still limited. In this paper, we establish a Stacked Denoising AutoEncoder (SDAE) model to retrieve highly accurate AOD using AHI. We explore the SDAE to retrieve AOD by taking the ground-observed AOD as the output and taking the AHI image, the month, hour, latitude, and longitude as the input data. This approach was tested in the Hubei province of China. Traditional machine learning methods such as Extreme Learning Machines (ELMs), BackPropagation Neural Networks (BPNNs), and Support Vector Machines (SVMs) are also used to evaluate model performance. The results show that the proposed method has the highest accuracy. We also compare the proposed method with ground-observed AOD measurements at the Wuhan University site, showing good consistency between the satellite-retrieved AOD and the ground-observed value. The study of the spatiotemporal change pattern of the hourly AOD in the Hubei province shows that the algorithm has good stability in the face of changes in the angle and intensity of sunlight. Full article
(This article belongs to the Special Issue Near Real-Time Remote Sensing Data and Its Geoscience Applications)
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