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Journal of Global Positioning Systems Vol. 22, No. 1, 2026 |
Cover |
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Journal of Global Positioning Systems
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JGPS Team Structure, Copyright and Table of Contents |
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JGPS Team Structure, Copyright
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1. Advances in Real-Time GNSS Monitoring of Earthquakes and Volcanoes in Indonesia |
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Muhammad Al Kautsar, Moh. Fifik Syafiudin, Rahmat Triyono, Priatin Hadi Wijaya, Oktadi Prayoga, Sulistiyani, Ayu Nur Safi’i, Thomas Hardy, Ajat Sudrajat,Fanny Zafira Mukti
Indonesia is among the most tectonically and volcanically active regions worldwide, where frequent earthquakes, volcanic eruptions, and long-term surface deformation pose serious risks to population centers and critical infrastructure. Reliable real-time geodetic monitoring is therefore essential for hazard assessment, early warning, and disaster mitigation. This study presents the status and recent advances in GNSS-based real-time geohazard monitoring in Indonesia, with an emphasis on precise point positioning with ambiguity resolution (PPP-AR) implemented through the GSeisRT real-time engine. GSeisRT processes 1 Hz multi-GNSS observations and generates low-latency regional satellite clock and phase bias products, enabling stable and high-precision real-time displacement monitoring. GSeisRT has been deployed within the nationwide Ina-CORS network operated by the BIG and supports continuous monitoring of earthquakes, volcanic deformation, land subsidence, and active faults. Several recent seismic events, including the 2023 Banda Sea Mw 7.1 earthquake and subsequent Mw 5.8-6.6 events, were successfully captured in real time. The derived coseismic displacements and peak ground displacements (PGDs) allow rapid earthquake magnitude determination that agrees well with estimates published by the United States Geological Survey (USGS). Performance comparisons with existing real-time PPP systems demonstrate that GSeisRT provides improved robustness and continuity, effectively suppressing spurious position jumps caused by incorrect ambiguity resolution. Additional developments, including the integration of GNSS with collocated accelerometer data and the availability of global 1 Hz real-time satellite products, further enhance short-period deformation monitoring and seismic response capability. Although challenges remain due to sparse station spacing and communication limitations in remote areas, the results confirm that advanced re-al-time GNSS technologies provide a reliable and scalable foundation for strengthening geohazard early warning and resilience in Indonesia.
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2. Flood Detection Using CYGNSS GNSS-R Observations - A Case Study of the 2022 Sindh Flood, Pakistan |
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Afaq Karim, Dongkai Yang1, Omada Friday Ojonugwa
Flood monitoring and mapping are essential
for effective disaster response, particularly in regions
frequently affected by extreme rainfall events.
Conventional flood detection methods are often
constrained by high costs, cloud cover, and limited
temporal resolution. This study investigates the
capability of Global Navigation Satellite System
Reflectometry (GNSS-R) for flood detection using
Level-1 data from the Cyclone Global Navigation
Satellite System (CYGNSS) during the severe August
2022 flood event in Sindh Province, Pakistan. Surface
reflectivity was estimated using a bistatic radar
formula, in which the corrected signal-to-noise ratio
(SNRc) was derived as a proxy for surface water
presence after quality control, incidence-angle
filtering, and outlier removal. To generate continuous
flood reflectivity fields from irregular CYGNSS
observations, a Natural Neighbor interpolation
technique based on Voronoi tessellation was applied.
Flooded areas were identified using an empirically
derived SNRc threshold of approximately 16 dB,
determined from observations over permanent water
bodies. The resulting CYGNSS-derived flood maps
show strong spatial agreement with MODIS-based
flood inundation maps. The overall accuracy achieved
is 0.819, indicating that approximately 82% of the
MODIS and CYGNSS grid cells were correctly
classified.
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3. Spatio-Temporal Analysis of Soil Moisture Dynamics Using GNSS-R and Ancillary Remote Sensing Datasets in Nigeria |
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Omada Friday Ojonugwa,Yang Dongkai,Afaq Karim,Mugabo Adolphe Izerimana
This study evaluates CYGNSS Level-3 surface soil moisture (SSM) retrievals across Nigeria using a Random Forest model trained on Level-1 reflectivity and ancillary geophysical data. All performance metrics assess the Random Forest model's ability to spatially disaggregate CYGNSS Level-3 products using ancillary data, not absolute accuracy against ground observations. The model downscales CYGNSS Level-3 SSM from ~36 km to 2 km resolution for soil moisture mapping, with error characterization conducted at 1 km resolution to capture finer-scale uncertainty patterns. Spatial cross validation across four phenological stages demonstrated robust downscaling consistency throughout 2020, yielding R² of 0.918–0.969 and RMSE of 0.014–0.028 m³/m³ relative to CYGNSS Level-3 targets. The retrievals accurately captured seasonal moisture gradients from <0.04 m³ /m³ in the northern Sahel to>
0.40 m³/m³ in the Niger Delta, while preserving riparian contrasts. Variable importance shifted seasonally from vegetation dominance to surface reflectivity control. Despite localized uncertainties over rugged terrain, sub-0.03 m³/m³ reconstruction precision relative to CYGNSS L3 was maintained without seasonal recalibration. These findings confirm that CYGNSS GNSS-R data provides operationally viable SSM monitoring for agricultural forecasting and drought early warning in data-scarce environments.
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4. Deep Learning-Driven Synergistic Integration of Satellite-Derived Water Vapor and Meteorological Radar for Enhanced Urban Precipitation Nowcasting: A Spatiotemporal Attention Architecture |
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Jing Sun,Jiale Wang,Chuang Shi,Yunchang Cao,Hong Liang,Yuyang Zhu,Wu Chen
Intensifying climate variability has escalated the frequency and severity of localized extreme precipitation events, demanding innovative forecasting methodologies to bolster urban resilience and disaster mitigation. Conventional prediction systems struggle with the rapid onset and high-impact nature of short duration intense rainfall. This investigation presents a heterogeneous data fusion architecture that synergistically combines satellite-based observations—specifically GNSS-derived Precipitable Water Vapor (PWV)—with ground-based Radar Reflectivity Composites (RRC) to advance precipitation nowcasting capabilities. GNSS-PWV serves as a sensitive indicator of atmospheric moisture accumulation dynamics, while RRC characterizes the spatial-temporal evolution of precipitation systems. We propose a Spatiotemporal Attention-Enhanced Swin U-Net (STAESwin) architecture that leverages Swin Transformer modules coupled with an integrated spatiotemporal attention mechanism to effectively assimilate these complementary data streams.
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5. A Gradient-Aware Undersampled Ionospheric Threat Model with Multi Metrics for BDSBAS |
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Hui Ren,Zhengzhen Jin,Tong Liu,Fangxin Hu,Yiping Jiang
Satellite-based augmentation systems (SBAS) broadcast grid ionospheric vertical delays (GIVDs) together with their integrity bounds, known as grid ionospheric vertical errors (GIVEs), to support accurate and integrity-assured positioning. In regions such as China, characterized by wide latitude coverage and highly dynamic ionospheric behaviour, geometric descriptors of IPP distribution alone are insufficient to capture local ionospheric variability. This limitation prevents conventional undersampled threat models from distinguishing between benign and severe conditions, leading to excessive GIVE inflation and reduced availability. This paper proposes a gradient-aware undersampled ionospheric threat model for BeiDou satellite-based augmentation system (BDSBAS) in China, referred to as the RRG-3D framework. The method extends the conventional geometry-based model by introducing a statistical ionospheric gradient metric, P90G, defined as the 90th percentile of pairwise gradients to characterize local variability.
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6. LiDAR-Inertial-UWB Integrated Localization with Quality Control and Online NLOS Mitigation for Unmanned Ground Vehicles |
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Hong Liu,Shuguo Pan,Wang Gao,Jinle Xu,Zhuoxuan Wang,Feixuan Huang
Continuous and precise localization constitutes a fundamental prerequisite for intelligent vehicles and autonomous driving, particularly in challenging environments, such as GNSS signal blockage, laser degeneration and vision illumination variations. In this paper, we propose a system that integrates data from light detection and ranging (LiDAR), inertial sensor, and ultrawideband (UWB) to achieve global positioning through filtering and smoothing techniques in diverse scenes. Firstly, we leverage an Extended Kalman Filter (EKF) to handle ranging measurements, while also online distinguishing and removing None Line of Sight (NLOS) errors. Besides, a quality control module is performed to obtain healthy UWB absolute factor in the backend. Secondly, a precise inertial navigation system (INS) mechanization algorithm is employed to achieve state updates, which also contributes to the LiDAR-inertial partly tight-coupled process. Finally, we propose a lightweight factor graph to optimally fuse marginalization factor, relative LiDAR odometry factor, UWB absolute factor and preintegration factor. We conducted extensive experiments in outdoor and NLOS scenarios using a customized platform. The results show that our integrated positioning approach enhances the accuracy of existing state-of-the-art schemes, while also enabling robust and real-time pose estimation for unmanned ground vehicles.
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| To Be Completed |
Back Cover |
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Journal of Global Positioning Systems
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