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Tianmu-1 Constellation: Advancements in Atmospheric, Ionospheric and Surface Remote Sensing...
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Tianmu-1 Constellation: Advancements in Atmospheric, Ionospheric and Surface Remote Sensing Using GNSS-RO and GNSS-R

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4571

Editors

Dr. Shengpeng Yang

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Nanjing University of Information and Technology, Nanjing, 210044, China
Interests: GNSS RO applications; data assimilation; numerical weather forecast
Dr. Hugo Carreno-Luengo

E-Mail Website
Guest Editor
NASA CYGNSS Mission, Climate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
Interests: GNSS-reflectometry; microwave radiometry; bistatic scattering; SmallSats; planetary sciences; water cycle; carbon cycle
Special Issues, Collections and Topics in MDPI journals
Dr. Feixiong Huang

E-Mail Website
Guest Editor
National Space Science Center, Chinese Academy of Sciences (NSSC/CAS), Beijing 100190, China
Interests: GNSS-R; ocean wind; data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Tianmu-1 constellation represents a significant advancement in commercial satellite systems dedicated to Earth observations. Rapidly deployed through multiple launches starting in late 2021 and now numbering 23 satellites, Tianmu-1 carries the GNOS-M payload with multi-GNSS-compatible (BDS, GPS, Galileo, GLONASS) Radio Occultation (RO) and GNSS-Reflectometry (GNSS-R). The GNSS-RO component provides crucial global, all-weather, high-vertical-resolution atmospheric profiles for improving numerical weather prediction (NWP) and climate monitoring. Simultaneously, the GNSS-R component offers valuable data for sensing surface characteristics such as soil moisture and sea states. This constellation’s ongoing expansion and unique integrated GNSS remote sensing nature present substantial opportunities and challenges for the remote sensing community.

This Special Issue aims to gather and showcase cutting-edge research focused on the Tianmu-1 constellation. We encourage submissions covering its full spectrum of activities, from fundamental aspects encompassing precise orbit determination (POD) and instrument data processing chain development (for both RO and GNSS-R) to advanced geophysical parameter retrieval, data assimilation techniques, and diverse scientific applications. Investigating the capabilities, data quality, and innovative uses of this new constellation directly aligns with the scope of Remote Sensing, which fosters advancements in remote sensing technologies, satellite missions, data processing algorithms, and their applications in understanding the Earth’s system.

We welcome submissions of original research articles and comprehensive review papers. Suggested topics include, but are not limited to, the following:

  • Precise Orbit Determination (POD) strategies and performance for Tianmu-1 satellites.
  • Development, calibration, and validation of Tianmu-1 GNSS-RO data processing chains.
  • Retrieval algorithms for atmospheric parameters (e.g., temperature, pressure, humidity, electron density) from Tianmu-1 RO data.
  • Assimilation of Tianmu-1 RO data into NWP, climate, and ionospheric models.
  • Meteorological, climatological, and space weather applications using Tianmu-1 RO observations.
  • Development, calibration, and validation of Tianmu-1 GNSS-R data processing chains.
  • Retrieval algorithms for geophysical parameters (e.g., soil moisture, sea surface height, wind speed, sea ice characteristics) from Tianmu-1 GNSS-R data.
  • Assimilation of Tianmu-1 GNSS-R data into land surface, oceanographic, or atmospheric models.
  • Hydrological, oceanographic, cryospheric, and terrestrial applications using Tianmu-1 GNSS-R observations.
  • Synergistic studies utilizing both GNSS-RO and GNSS-R data from Tianmu-1.
  • Validation and assessment of Tianmu-1 data products.

Dr. Shengpeng Yang
Dr. Hugo Carreno-Luengo
Dr. Feixiong Huang
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-anonymized 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

  • GNSS-RO applications
  • data assimilation
  • numerical weather forecast
  • ocean wind

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Published Papers (3 papers)

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21 pages, 12290 KB  
Article
Land Surface Reflection Differences Observed by Spaceborne Multi-Satellite GNSS-R Systems
by Xiangyue Li, Xudong Tong and Qingyun Yan
Remote Sens. 2025, 17(23), 3807; https://doi.org/10.3390/rs17233807 - 24 Nov 2025
Cited by 3 | Viewed by 1130
Abstract
With the accelerated launch of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) satellites, GNSS-R has gradually emerged as an important technique for remote sensing. However, due to its pseudo-random observation mode, the use of a single system makes it difficult to provide continuous [...] Read more.
With the accelerated launch of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) satellites, GNSS-R has gradually emerged as an important technique for remote sensing. However, due to its pseudo-random observation mode, the use of a single system makes it difficult to provide continuous spatiotemporal coverage over a specific area within the short term. Although interpolation methods can partially alleviate the coverage gaps, their application is limited by accuracy and reliability constraints, which still restrict the practical use of GNSS-R in terrestrial surface monitoring. To address this issue, conducting joint analyses and data fusion of multi-satellite GNSS-R observations has become an important approach to improving the continuity and accuracy of surface monitoring. However, systematic studies on the integration of multi-satellite GNSS-R data remain relatively limited. Moreover, differences in orbital inclination, antenna design, and signal bandwidth among various spaceborne GNSS-R systems lead to discrepancies in their land observations. Therefore, this study systematically analyzes the reflectivity differences among multiple GNSS-R satellites (e.g., the Cyclone Global Navigation Satellite System (CYGNSS), Fengyun-3 (FY-3), and Tianmu-1 (TM-1)) under consistent surface roughness and land cover conditions, with the aim of providing a theoretical and methodological foundation for the fusion and integrated application of multi-satellite GNSS-R data. The results show that, except for desert regions, the spatial distribution of the correlation coefficients from the least squares fitting of reflectivity between different spaceborne GNSS-R satellites exhibits a pattern similar to that of an established variable, i.e., the vegetation–roughness composite variable (VR), with higher inter-system correlations occurring in areas characterized by lower VR values. Significant reflectivity deviations were observed near water bodies and river networks, such as the Amazon, Paraná, Congo, Niger, Nile, Ganges, Mekong, and Yangtze, where both the fitting intercepts and biases are relatively large. In addition, the reflectivity correlations between CYGNSS–TM-1 and CYGNSS–FY-3 are both strongly influenced by surface vegetation cover type. As the correlation increases, the proportion of non-vegetated and forested areas decreases, while that of grasslands, shrublands, and cropland/vegetation mosaics increases. Analysis of inter-system reflectivity correlations across different land cover types indicates that forested areas exhibit low-to-moderate correlations but maintain stable structural characteristics, whereas wooded areas show moderate correlations slightly lower than those of forests. Grasslands, shrublands, and croplands are mainly distributed within regions of moderate surface roughness and correlation, among which croplands have the highest proportion of highly correlated grids, demonstrating the greatest potential for multi-source data fusion. Wetlands display high roughness and low correlation, largely influenced by dynamic water variations, while bare soils show low roughness (0.2–0.4) but still weak correlations. Full article
(This article belongs to the Special Issue Tianmu-1 Constellation: Advancements in Atmospheric, Ionospheric and Surface Remote Sensing Using GNSS-RO and GNSS-R)
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22 pages, 5146 KB  
Technical Note
Quality Assessment and Observation Error Estimation of Tianmu-1 GNSS Radio Occultation Bending Angle and Refractivity Retrievals
by Li Wang, Shengpeng Yang, Buwei Yao and Li He
Remote Sens. 2026, 18(15), 2572; https://doi.org/10.3390/rs18152572 - 4 Aug 2026
Viewed by 200
Abstract
As China’s first commercial low Earth orbit meteorological constellation, Tianmu-1 (TM) radio occultation (RO) provides high-density observations, with approximately 30,000 profiles per day globally, offering new opportunities for research in observationally sparse regions. TM RO bending angle and refractivity retrievals from June to [...] Read more.
As China’s first commercial low Earth orbit meteorological constellation, Tianmu-1 (TM) radio occultation (RO) provides high-density observations, with approximately 30,000 profiles per day globally, offering new opportunities for research in observationally sparse regions. TM RO bending angle and refractivity retrievals from June to December 2023 are evaluated using ERA5 reanalysis, global radiosonde observations, and COSMIC-2 RO as reference datasets. TM observation errors are further estimated using the three-cornered hat (3CH) method. The results show that, in the upper troposphere and lower stratosphere, TM bending angle biases are within ±0.19%, ±0.40%, and ±0.37% relative to ERA5 (8–30 km), radiosonde (8–25 km), and COSMIC-2 (8–30 km), respectively. The corresponding refractivity biases are within ±0.09%, ±0.21%, and ±0.14%, respectively. Both bending angle and refractivity from TM RO exhibit high accuracy and precision. The observation errors estimated using the 3CH method show a clear latitudinal dependence. The observation errors in the tropics (±30° latitude) are larger than those in the middle and high latitudes. This study quantitatively characterizes the accuracy, precision, and error structure of TM RO bending angle and refractivity, providing a quantitative basis for future quality control and observation error specification in numerical weather prediction data assimilation. Full article
(This article belongs to the Special Issue Tianmu-1 Constellation: Advancements in Atmospheric, Ionospheric and Surface Remote Sensing Using GNSS-RO and GNSS-R)
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27 pages, 89001 KB  
Technical Note
Retrieval of Sea Ice Concentration and Thickness During the Arctic Freezing Period from Tianmu-1 Based on Machine Learning
by Xin Xu, Lijian Shi, Bin Zou, Peng Ren, Yingni Shi, Tao Zeng, Xiaoqing Lu, Qi Tang, Shuhan Hu, Shiyuan Qiu, Jiahua Li, Yilin Liu, Xin Liu and Zongqiang Liu
Remote Sens. 2026, 18(2), 237; https://doi.org/10.3390/rs18020237 - 11 Jan 2026
Cited by 1 | Viewed by 1460
Abstract
Sea ice concentration (SIC) and thickness (SIT) are critical variables for polar research. In this study, the potential of Tianmu-1 GNSS-R observations for retrieving Arctic SIC and SIT is explored using machine learning algorithms. XGBoost demonstrated superior accuracy and efficiency in the comparison [...] Read more.
Sea ice concentration (SIC) and thickness (SIT) are critical variables for polar research. In this study, the potential of Tianmu-1 GNSS-R observations for retrieving Arctic SIC and SIT is explored using machine learning algorithms. XGBoost demonstrated superior accuracy and efficiency in the comparison of the three methods. For SIC retrieval, 14 parameters from Tianmu-1 were employed directly, whereas SIT retrieval incorporated additional auxiliary parameters, including SIC, sea ice salinity (S), and temperature (T). Among the different GNSS systems, GLO achieved the lowest RMSE for SIC, at 7.750%, whereas GAL performed comparatively poorly, with an RMSE of 10.475%. In SIT retrieval, the GPS and BDS yielded the smallest RMSE values of 0.276 m and 0.278 m, respectively, while GLO resulted in a slightly higher RMSE of 0.309 m. Daily retrievals of both the SIC and SIT were conducted from 18 October 2023 to 12 April 2024, with consistently stable evaluation metrics throughout the freezing season. In high-concentration regions, the retrieved SIC and SIT closely matched the reference data, whereas larger errors occurred in marginal ice zones and coastal areas. This study reveals the potential of Tianmu-1 to complement existing satellite missions in Arctic sea ice monitoring during the freezing period. Full article
(This article belongs to the Special Issue Tianmu-1 Constellation: Advancements in Atmospheric, Ionospheric and Surface Remote Sensing Using GNSS-RO and GNSS-R)
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