Flood is a kind of natural disaster that is extremely harmful and occurs frequently. To reduce losses caused by the hazards, it is urgent to monitor the disaster area timely and carry out rescue operations efficiently. However, conventional space observers cannot achieve sufficient spatiotemporal resolution. As spaceborne GNSS-R technique can observe the Earth’s surface with high temporal and spatial resolutions; and it is expected to provide a new solution to the problem of flood hazards. During 19–21 July 2021, Henan province, China, suffered a catastrophic flood and urban waterlogging. In order to test the feasibility of flood disaster monitoring on a daily basis by using GNSS-R observations, the CYGNSS (Cyclone Global Navigation Satellite System) Level 1 Science Data were processed for a few days before and after the flood to obtain surface reflectivity by correcting the analog power. Afterwards, the flood was monitored and mapped daily based on the analysis of changes in surface reflectivity from spaceborne GNSS-R mission. The results were evaluated based on the image from MODIS (Moderate Resolution Imaging Spectroradiometer) data, and compared with the observations of SMAP (Soil Moisture Active Passive) in the same period. The results show that the area with high CYGNSS reflectivity corresponds to the flooded area monitored by MODIS, and it is also in high agreement with SMAP. Moreover, CYGNSS can achieve more detailed mapping and quantification of the inundated area and the duration of the flood, respectively, in line with the specific situation of the flood. Thus, spaceborne GNSS-R technology can be used as a method to monitor floods with high temporal resolution.

The freeze-thaw (F/T) process plays a significant role in climate change and ecological systems. The soil F/T state can now be determined using microwave remote sensing. However, its monitoring capacity is constrained by its low spatial resolution or long revisit intervals. In this study, spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data with high temporal and spatial resolutions were used to detect daily soil F/T cycles, including completely frozen, completely thawed, and F/T transition states. Firstly, the calibrated Cyclone GNSS (CYGNSS) reflectivity was used for soil F/T classification. Compared with those of Soil Moisture Active and Passive F/T data and in-situ data, the detection accuracies of CYGNSS reach 75.1 and 81.4%, respectively. Subsequently, the changes in spatial characteristics were quantified, including the monthly occurrence days of the soil F/T state. It is found that the completely frozen and completely thawed states have opposite spatial distributions, and the F/T transition states distribute from the east to the west and then back to the east of the Qinghai-Tibet Plateau, which may be due to varying diurnal temperatures in different seasons. Finally, the first day of thawing, last day of thawing, and thawing period of the F/T year were analyzed in terms of the changes in temporal characteristics. The temporal variation of thawing is mainly different between the western and eastern parts of the Tibetan Plateau, which is in agreement with the spatial variation characteristics. The results demonstrate that the CYGNSS can accurately detect the F/T state of near-surface soil in the daily scale. Moreover, it can complement traditional remote sensing missions to improve the F/T detection capability. It can also expand the applications of GNSS-R technology and provide new avenues for cryosphere research.

The global soil moisture (SM) retrievals by the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) are significantly influenced by the presence of water bodies. The traditional method is to build a grid based on the location of satellite sampling points and determine the presence or absence of water bodies. In this paper, we propose a water body removal method for global spaceborne GNSS-R SM retrievals that combines water bodies and buffers derived from the marginal areas around water bodies as mask data, thus achieving accurate removal of the water body and avoiding margin effects. To verify the effectiveness of the proposed method, the Cyclone GNSS (CYGNSS) data with two different spatial resolutions (36 km and 3 km) were used for SM retrieval, and the Soil Moisture Active and Passive (SMAP) Radiometer SM as well as the International Soil Moisture Network (ISMN) were used as references. Results show that the correlation coefficient (R) and root mean square error (RMSE) of the 36 km grid are 0.50 and 0.057 cm ³ /cm ³ , respectively, while the R and RMSE of the 3 km grid are 0.68 and 0.041 cm ³ /cm ³ , respectively. Such performances are better than the traditional method. Moreover, the method proposed in this paper preserves more grids. Take the 3 km spatial resolution for example, it preserves 2.2 fold grids more than the traditional water body removal method. In the comparison with SMAP SM, the overall improvement of RMSE by using the water body removal method proposed in this paper is 16.3% (8.2% for the traditional method). In the in-situ validation, the overall improvement of RMSE is 19.4% (-1.2% for the traditional method). Therefore, in the future high spatial resolution SM retrieval, the water body removal method proposed in this paper can preserve the maximum area and effectively eliminate the influence of water bodies on SM retrieval.

Global soil moisture (SM) mapping at high spatial and temporal resolution contributes significantly to various hydrologic and meteorological researches. This work presents an algorithm for combining fine-resolution Global Navigation Satellite System Reflectometry (GNSS-R) observations from the Cyclone Global Navigation Satellite System (CYGNSS) and coarse-resolution SM estimates from the Soil Moisture Active Passive (SMAP) mission to estimate SM at 3 km resolution. In practice, the expression for downscaled 3 km SM is derived from the mathematical double-scale SM equations based on the linear assumption between SM and reflectivity, and the CYGNSS signal-to-noise ratio data are leveraged to compensate the differences in heterogeneity between the two scales. Experimental validation over 150 in-situ sites shows strong consistency between the SMAP/CYGNSS 3 km SM estimates and the in-situ measurements, with a median correlation coefficient of 0.806 and a median unbiased root-mean-square error of 0.038 cm³/cm³. The contributions of this work are twofold: 1) introducing the normalized signal-to-noise ratio to account for the deviations in double-scale coefficients, which depends on the variations in vegetation and surface roughness between 36 km and 3 km scales, without relying on any ongoing knowledge of ancillary data; and 2) achieving daily 3 km SM estimations at a quasi-global scale, and providing a new way for enhancing the temporal and spatial resolution of SMAP SM.

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) observations are effective for extensively monitoring soil moisture (SM). Reliance on gridded data from other remote sensing systems is the general way of GNSS-R SM retrieval. Increasing the grid size improves temporal coverage but decreases spatial resolution, which may mask changes in SM within the grid. Conversely, finer grids result in higher spatial resolution but less temporal resolution, leading to discontinuities in SM temporal dynamics. Therefore GNSS-R observation gridding cannot achieve high spatiotemporal resolutions simultaneously. To leverage the potential of GNSS-R technology in SM retrieval with a high spatiotemporal resolution, this study introduced a reconstruction method for spaceborne GNSS-R observations to generate gridded observations with both high temporal and spatial resolution. This method was used to build a pixel-by-pixel multivariate temporal fitting model that incorporates the characteristics of GNSS-R observations and the associated driving factors. Specifically, this study utilizes Cyclone GNSS (CYGNSS) sliding average observations to supplement daily trend observations and integrates ancillary data from other satellite missions as adjustments to daily variability, which generates spatiotemporally seamless CYGNSS observations. The reconstructed CYGNSS observations were then utilized to provide daily quasi-global SM retrievals on a 9-km grid. The root mean square error (RMSE) of the SM retrievals was 0.052 cm³/cm³, which are comparable to the accuracy of the SM retrievals conducted prior to reconstruction. Similarly, independent evaluations of local in situ sites demonstrated that the RMSE and correlation (R) of the SM retrieval were 0.051 cm³/cm³ and 0.77, respectively. The SM from the reconstructed CYGNSS observations also captured local wet and dry dynamics, which is consistent with the performance of the SM retrieved from the original CYGNSS observations. Notably, the temporal resolution was improved by 258% over the original CYGNSS observations at 9 km. Therefore, we argue that the method used in this study addresses the problem of mutual constraints between spatial and temporal resolutions in quasi-global CYGNSS SM retrieval without loss of SM retrieval accuracy. Furthermore, the observation reconstruction method developed in this study may be a promising reference for monitoring other geophysical parameters with high spatiotemporal resolution in the GNSS-R domain.

China's Tianmu-1, equipped with the Global Navigation Satellite System Reflectometry (GNSS-R) payload, has formed a constellation of 22 small satellites since its batch launch in 2023. Since the TM-1 constellation has the largest number of GNSS-R satellites to date, TM-1 observations provide powerful samples from the Earth's surface. This study is the first to utilize ten months of TM-1 GNSS-R observations for land (soil moisture (SM), freeze-thaw (F/T), waterbody) and ocean (sea ice, sea surface winds, and significant wave height (SWH)) parameter retrievals. Compared with the reference satellite data, the root mean square error (RMSE) and correlation (R) of the TM-1 SM retrieval were 0.035 cm³/cm³ and 0.61, respectively. Compared with in-situ data, the RMSE and R of the TM-1 SM retrieval were 0.050 cm³/cm³ and 0.83, respectively. The accuracies of the TM-1 F/T retrievals were 96.5% and 92.0% compared with the reference satellite data and in-situ data, respectively. The accuracy of TM-1 water body detection exceeded 97% when compared to optical remote sensing products. TM-1 sea ice detection accuracy exceeds 98% compared to microwave radiometer products. The RMSE for TM-1 sea surface wind speed is 2.09 m/s with an R of 0.82, and the RMSE for SWH is 0.65 m with an R of 0.86. Additionally, the RMSE of TM-1 wind speed retrievals compared to NDBC buoy wind speeds is 1.84 m/s, with an R of 0.82. The RMSE of TM-1 SWH compared to the NDBC SWH is 0.46 m, with an R of 0.86. Both land and ocean remote sensing retrievals demonstrate the excellent performance of the TM-1 GNSS-R constellation. As a result, the TM-1 GNSS-R constellation holds promise as a valuable tool for Earth observation.