Global Navigation Satellite System reflectometry (GNSS-R) has emerged as a valuable tool for Earth observations, providing low-cost measurements with high spatiotemporal resolution. This study presents the first application of Spire Global Inc. near-nadir (NN) GNSS-R observations for sea ice detection and sea ice extent (SIE) mapping, covering the Arctic and Antarctic. The delay and Doppler maps (DDMs) of Spire NN GNSS-R show a significant difference over various surfaces. For a test period from February to July 2024, results show that the Arctic’s SIE decreases from 584600 to 333400 km2, and the Antarctic’s SIE increases from 142825 to 584750 km2. Compared to Advanced Microwave Scanning Radiometer 2 (AMSR2) estimates, Spire NN GNSS-R retrievals achieve a probability of detection (Pd) of 94%–96% and the probability of false alarm (Pfa) and probability of error (Pe) below 0.23%. These results demonstrate the high sensitivity, accuracy, and operational potential of Spire NN GNSS-R for sea ice remote sensing.

Spaceborne GNSS reflectometry (GNSS-R) has emerged as an effective tool for observing soil moisture content at relatively high temporal and spatial resolutions. This study presents an initial evaluation of using reflection signals captured by Spire Global’s GNSS-R satellites for global soil moisture monitoring, based on the sample data on Nov 1, 2023. The land surface is covered by ~16.7% when gridding the good-quality reflection samples into a map with a resolution of 36 km. Moreover, the Spire reflectivity observations of an example track over the region without dense vegetation show a good agreement with the SMAP soil moisture measurements, with a correlation coefficient of 0.71. The Spire soil moisture retrievals have a root mean square difference (RMSD) of 0.066 m3/m3, compared to the SMAP observations. More accurate Spire soil moisture measurements are expected after correcting the impact of vegetation, surface roughness, and signal incidence angles.

CYGNSS satellites record the power of GNSS reflection signals in delay and Doppler shift bins. Myriad delay Doppler maps (DDM) have been collected since 2016. Research has shown that reflection range delays can be derived from CYGNSS DDMs and their metadata for satellite altimetry. However, the performance of these DDMs in estimating inland water levels has not been thoroughly evaluated. This study leverages the coherent reflection-dominated DDMs from 2020 to 2022 to estimate water levels at five lakes. Compared to radar altimetric (RA) observations, the CYGNSS results have an overall bias of ~2.0 m, a root mean square difference (RMSD) of ~3.1 m, and an unbiased RMSD (ubRMSD) of ~2.4 m, when combining measurements from all study sites over the three-year period. During 2020–2021, the bias is lower at 1.1 m, the RMSD is ~1.9 m, and the ubRMSD is reduced to ~1.6 m. Smaller bias, RMSD, and ubRMSD values are also observed in 2022 nighttime data. However, the daytime data from 2022 show higher water level estimations compared to RA observations with a bias of 4.7 m, a RMSD of 6.1 m, and an ubRMSD of ~3.9 m. The CYGNSS overestimations are linked to an increase in ionospheric total electron content. This study demonstrates the feasibility of spaceborne GNSS-R for measuring inland water levels and highlights the usefulness of the current version of CYGNSS DDMs in monitoring reservoirs and inundation events characterized by substantial water level changes.

This paper presents a quantitative analysis of spaceborne GNSS-R signal coherence dependence on satellite elevation angles at specular reflection points over sea ice and ice sheets in Greenland and Antarctica using TechDemoSat-1 delay Doppler maps. Over sea ice and ice sheets, the probability of coherent reflections is high at low elevation angles and decreases with increasing elevation angles, as expected. For sea ice, the coherence rate is ~96% at elevation angles of$20-43^{\circ}$, then drops to a minimum of ~25% near nadir. Over the Greenland ice sheet, the maximum coherence rate is 86% at the elevation angle of$47^{\circ}$, and the minimum is 21% at$73^{\circ}$. In Antarctica, the coherence rate reaches a maximum of 90% at$48^{\mathrm{o}}$and a minimum of 36% at$72^{\circ}$. The findings provide a quantitative characterization of the GNSS-R coherency over sea ice and ice sheets and are useful for future GNSS-R mission designs.

Coherent GNSS reflections over land predominantly occur over surface water bodies. This study presents a method to jointly use carrier phases and signal strengths of reflected signals to identify coherent reflections and applies it to the 50-Hz GNSS-R measurements from Spire Global Cubesats and CYGNSS microsatellites to map inland water bodies. A coherence detector was first developed using the circular statistics of carrier phase noises, identifying the input samples as coherent, semi-coherent, or incoherent. For any given track of data, we used this coherence detector to iteratively assess the coherency levels of the samples by a moving time window, then derived the coherency levels with the highest confidence. The circular statistics-based semi-coherent reflections with signal strengths above the prescribed threshold were regarded as coherent. The specular reflection points of the coherent reflections represent the locations of surface water. This method was applied to the Spire data to obtain the surface water extents for 1951 lakes and the CYGNSS data for 113 lakes. Compared to Global Surface Water Explorer observations, around 90% of the disagreements of the Spire data-based surface water boundaries are less than 0.73 km with a mean of 0.28 km and a standard deviation of 0.24 km. As for CYGNSS, ~90% of the disagreements are less than 0.43 km with a mean value of 0.18 km and a standard deviation of 0.16 km. The possible error sources are mainly fractional surface water, nearly flat and saturated ground surface, background land cover, and GNSS-R geometry.

Permafrost has been warming and thawing in the last decades in response to global warming. Monitoring the changes of the active layer and near-surface permafrost is crucial for revealing their dynamics. GNSS interferometric reflectometry (GNSS-IR) is a relatively new technique for studying frozen ground dynamics. In this paper, we summarized the major achievements in applying GNSS-IR to the signal-to-noise ratio data recorded by the continuous GNSS sites in permafrost areas in the Arctic and Qinghai-Tibet Plateau. We identified 23 sites in the Arctic permafrost regions which are suitable for GNSS-IR studies. We used the GNSS-IR-retrieved surface elevation changes to investigate the multi-year, interannual, and seasonal changes of the frozen ground. We improved the commonly-used GNSS-IR algorithm for estimating soil moisture in permafrost areas by mitigating the bias introduced by seasonal surface deformation. We also analogized GNSS-IR to InSAR, and advocated for a synergy between their observations to obtain improved, quantitative, and insightful measurements of the ever-warming frozen ground.