Global Navigation Satellite System (GNSS) radio occultation (RO), owing to its capability to provide high vertical resolution, high accuracy, calibration-free, and all-weather atmospheric observations, has been widely used in numerical weather prediction (NWP) and climate studies. As China’s first commercial GNSS RO constellation supporting all major GNSS systems, Tianmu-1 (TM-1) offers promising observations. However, its data quality and assimilation performance in NWP remain underexplored. This study first evaluates the TM-1 neutral atmospheric refractivity and bending angle profiles collected in January 2024. Compared with the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ECMWF-ERA5), refractivity fractional differences at 5–30 km have mean and standard deviation within ±0.15% and 1.31%, while bending angle differences are within ±0.55% and 1.99%. Radiosonde comparisons over 0–20 km show refractivity differences within ±0.19% and 1.93%, and bending angle differences within ±0.12% and 5.06%. Larger errors are mainly confined to the lower troposphere and low latitudes, with only minor variations across GNSS constellations. After validating data quality, TM-1 refractivity observations are assimilated using the Weather Research and Forecasting (WRF) model and WRFDA 3DVAR system to assess their impact on regional analyses and short-range forecasts over China. Model outputs are validated against ERA5 reanalysis and radiosonde observations. The results show that assimilating TM-1 refractivity data leads to root mean squared error (RMSE) reductions of ~5–10% for temperature analyses and forecasts in the mid-to-upper troposphere and near the surface, and ~5% in specific humidity in the lower troposphere. Wind impacts are mixed, with RMSE improvements ~2–5% above 600 hPa and degradation in the lower troposphere. Overall, this preliminary study confirms the high quality of TM-1 GNSS RO refractivity data and demonstrates its promising contribution in complementing current operational RO assimilation for regional NWP.

As a serious marine environmental disaster, harmful algal blooms (HABs) exhibit characteristics such as high frequency, large impact area, and increasing damage. There is an urgent need for an all-weather, high revisit rate, wide-range, and large-scale monitoring method to address these changes. In this article, we propose a method to detect the distribution and density of HABs using the mean square slope (MSS) observations from Global Navigation Satellite System-Reflectometry (GNSS-R), based on the physical explanation that the coverage of HABs leads to a reduction in wind-driven sea surface roughness. By comparing the Cyclone Global Navigation Satellite System (CYGNSS) MSS data with actual observations, the applicable threshold for HABs monitoring based on MSS is clarified, and a HABs density inversion model is constructed. The classification of two severe HABs areas (Jiaozhou Bay and the Gulf of Mexico) has been achieved with a 59% probability of detection and 1% false alarm rate. The introduction of this method enables daily scale observations of HABs, which can better reveal the spatiotemporal distribution characteristics, migration patterns, and influencing factors of HABs, providing a new means for the monitoring and integrated management of HABs.

Sea ice is regarded as an indicator of temperature change. In recent years, the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technology has made remarkable progress in sea ice detection. Delay-Doppler maps (DDMs) as one of significant observations can reflect different characteristics for sea ice and open water, and a single DDM is usually viewed as the unit of feature extraction; however, it is easily influenced by wave height, wind and other factors. Therefore, this paper proposes building scene semantic objects to enhance the reliability of observation and reflect the object characteristics. The synergism between DDMs and the spatial correlation of specular points was considered. Afterwards, histogram features were extracted to express the distribution of scattered energy. The random forest (RF) model was developed to distinguish sea ice from open water. The performance of the method by using TechDemoSat-1 (TDS-1) dataset was evaluated with the sea ice concentration products provided by OSISAF. The results show that the overall accuracy is 98.17%, which outperforms traditional observation methods.

In recent years, global navigation satellite systems-reflectometry (GNSS-R) technology has been increasingly considered for applications in sea surface monitoring. This paper presents a new method to retrieve the density of sea surface green algae by using the reflected signals of geostationary Earth orbit (GEO) satellites collected by shipborne receiver. Because GEO satellites are stationary relative to a fixed receiver on the earth’s surface, the reflected GEO satellite (GEO-R) signals are not affected by Doppler frequency or elevation angle, which can greatly simplify the modeling of the reflected power and realize continuous green algae monitoring in the same area. Specifically, the influence of green algae on GEO-R power through varying reflection coefficient and roughness was analyzed. Then, an empirical model was established to retrieve the green algae density by using the GEO-R power. Finally, the experimental data collected in the Qingdao Jiaozhou bay were used to verify the developed models, and the results show that the inversion accuracy of the green algae density model is better than 4%.

Due to the continuous intensification of human activities in the ocean, the frequent outbreaks of red tide have caused great harm to the marine environment and ecology. Thus, the rapid detection and monitoring of red tide become particularly important. At present, the main monitoring methods depend on artificial and buoy data, as well as optical satellite remote sensing. However, these methods may not be able to effectively deal with the characteristics of red tide bloom, such as suddenness and unpredictability. The global navigation satellite system-reflectometry (GNSS-R) is an emerging technology that makes use of navigation signals as a remote sensing opportunity to obtain Earth surface information. GNSS-R has already been proved to be capable of retrieving sea surface parameters (e.g., dielectric constant and sea surface roughness) closely related to the outbreak of a red tide. In this article, we proposed a new method to estimate red tide density, which utilizes an all-new model associating GNSS-R observations with sea surface red tide density. This method can remove the weather influence and greatly decrease the revisit period, which is much longer for optical red tide remote sensing methods. The Landsat-8 near-infrared data and TechDemoSat-1 (TDS-1) GNSS-R data of a red tide outbreak in the sea off the Tsingtao coast in China are used to build and test the proposed method. The results demonstrate that the correlation coefficient is 0.73, and the root mean square error of retrieved red tide density is 2.84%, which shows that the GNSS-R technology shows great potential to perform the rapid and preliminary red tide monitoring and judgment.

In recent years, GNSS reflectometry (GNSS-R) research has mainly been focused on the Global Positioning System (GPS) while the use of Geostationary Earth Orbit (GEO) satellites has received little attention. This paper investigates the GEO satellite-based GNSS-R with a focus on the application of soil moisture retrieval. A new soil moisture estimation approach using GEO signals are proposed, which is termed GEO reflectometry (GEO-R). Two empirical models (linear and second-order) are developed for signal SNR ratio based GEO-R. Experimental datasets collected from different GEO systems were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.

As a cost-effective remote sensing technique, global navigation satellite system reflectometry (GNSS-R) has recently drawn significant attention from both academia and industry. However, research on GNSS-R has mainly been focused on the global positioning system which consists of only medium earth orbit satellites, while the use of geostationary earth orbit (GEO) satellites, such as those in BeiDou navigation satellite system, has received little attention. This paper investigates the GEO-satellite-based GNSS-R with a focus on the application of soil moisture retrieval. Because GEO satellites remain static with the earth, the models of the reflected GNSS signals can be considerably simplified and their signals can be used to estimate soil moisture with a high update rate such as once per hour. Two new soil moisture estimation approaches using GEO signals are proposed, which are termed GEO interferometric reflectometry (GEO-IR) and GEO reflectometry (GEO-R). Two theoretical models (linear and second order) are developed for signal-to-noise ratio (SNR)-based GEO-IR as well as for phase-based GEO-IR. Meanwhile, two empirical models (linear and second order) are developed for signal amplitude-based GEO-R as well as for SNR ratio-based GEO-R. Experimental data sets collected from three different geographical regions were used to evaluate the proposed methods. The results demonstrate that the proposed GEO-IR and GEO-R are able to monitor soil moisture reliably under bare soil condition, augmenting GNSS-R through significantly reduced processing complexity and increased temporal coverage.

Snow is important to the ecological and climate systems; however, current snowfall and snow depth in situ observations are only available sparsely on the globe. By making use of the networks of Global Positioning System (GPS) stations established for geodetic applications, it is possible to monitor snow distribution on a global scale in an inexpensive way. In this paper, we propose a new snow depth estimation approach using a geodetic GPS station, multipath reflectometry and a linear combination of phase measurements of GPS triple-frequency (L1, L2, and L5) signals. This phase combination is geometry free and is not affected by ionospheric delays. Analytical linear models are first established to describe the relationship between antenna height and spectral peak frequency of combined phase time series, which are calculated based on theoretical formulas. When estimating snow depth in real time, the spectral peak frequency of the phase measurements is obtained, and then the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that the proposed method shows an improvement with respect to existing methods on average.