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

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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

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21 pages, 12290 KB

Open AccessArticle

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

Viewed by 342

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 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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