Global navigation satellite system interferometric reflectometry (GNSS-IR) represents an extra method to detect snow depth for climate research and water cycle managing. However, using a single frequency of GNSS-IR for snow depth retrieval is often found to be challenging when attempting to achieve a high spatial and temporal sensitivity. To evaluate both the capability of the GNSS-IR snow depth retrieved by the multi-GNSS system and multi-frequency from signal-to-noise ratio (SNR) data, the accuracy of snow depth retrieval by different frequency signals from the multi-GNSS system is analyzed, and a joint retrieval is carried out by combining the multi-GNSS system retrieval results. The SNR data of the global positioning system (GPS), global orbit navigation satellite system (GLONASS), Galileo satellite navigation system (Galileo), and BeiDou navigation satellite system (BDS) from the P387 station of the U.S. Plate Boundary Observatory (PBO) are analyzed. A Lomb–Scargle periodogram (LSP) spectrum analysis is used to compare the difference in reflector height between the snow-free and snow surfaces in order to retrieve the snow depth, which is compared with the PBO snow depth. First, the different frequency retrieval results of the multi-GNSS system are analyzed. Then, the retrieval accuracy of the different GNSS systems is analyzed through multi-frequency mean fusion. Finally, the joint retrieval accuracy of the multi-GNSS system is analyzed through mean fusion. The experimental shows that the retrieval results of different frequencies of the multi-GNSS system have a strong correlation with the PBO snow depth, and that the accuracy is better than 10 cm. The multi-frequency mean fusion of different GNSS systems can effectively improve the retrieval accuracy, which is better than 7 cm. The joint retrieval accuracy of the multi-GNSS system is further improved, with a correlation coefficient (R) between the retrieval snow depth and the PBO snow depth of 0.99, and the accuracy is better than 3 cm. Therefore, using multi-GNSS and multi-frequency data to retrieve the snow depth has a good accuracy and feasibility.

Snow is one of the most critical sources of freshwater, which influences the global water cycle and climate change. However, it is difficult to monitor global snow variations with high spatial–temporal resolution using traditional techniques due to their costly and labor-intensive nature. Nowadays, the Global Positioning System Interferometric Reflectometry (GPS-IR) technique can measure the average snow depth around a GPS antenna using its signal-to-noise ratio (SNR) data. Previous studies focused on the use of GPS data at sites located in flat areas or on very gentle slopes. In this contribution, we propose a strategy called the Tilted Surface Strategy (TSS), which uses the SNR data reflected only from the flat quadrants to estimate the snow depth instead of the conventional strategy, which employs all the SNR data reflected from the whole area around a GPS antenna. Three geodetic GPS sites from the Plate Boundary Observatory (PBO) project were chosen in this experimental study, of which GPS sites p683 and p101 were located on slopes with their gradients up to 18% and the site p025 was located on a flat area. Comparing the snow depths derived with the GPS-IR TSS method with the snow depth results provided with the GPS-PBO, i.e., GPS-IR with the conventional strategy, the Snowpack Telemetry (SNOTEL) network measurements and gridded Snow Data Assimilation System (SNODAS) estimates, it was found that the snow depths derived with the four methods had a good agreement, but the snow depth time series with the GPS-IR TSS method were closer to the SNOTEL measurements and the SNODAS estimates than those with GPS-PBO method. Similar observations were also obtained from the cumulative snowfall time series. Results generally indicated that for those GPS sites located on slopes, the TSS strategy works better.

Floods are severe natural disasters that are harmful and frequently occur across the world. From May to July 2022, the strongest, broadest, and longest rainfall event in recent years occurred in Guangdong Province, China. The flooding caused by continuous precipitation and a typhoon resulted in severe losses to local people and property. During flood events, there is an urgent need for timely and detailed flood inundation mapping for areas that have been severely affected. However, current satellite missions cannot provide sufficient information at a high enough spatio-temporal resolution for flooding applications. In contrast, spaceborne Global Navigation Satellite System reflectometry technology can be used to observe the Earth’s surface at a high spatio-temporal resolution without being affected by clouds or surface vegetation, providing a feasible scheme for flood disaster research. In this study, Cyclone Global Navigation Satellite System (CYGNSS) L1 science data were processed to obtain the change in the delay-Doppler map and surface reflectivity (SR) during the flood event. Then, a flood inundation map of the extreme precipitation was drawn using the threshold method based on the CYGNSS SR. Additionally, the flooded areas that were calculated based on the soil moisture from the Soil Moisture Active Passive (SMAP) data were used as a reference. Furthermore, the daily Dry Wet Abrupt Alternation Index (DWAAI) was used to identify the occurrence of the flood events. The results showed good agreement between the flood inundation that was derived from the CYGNSS SR and SMAP soil moisture. Moreover, compared with the SMAP results, the CYGNSS SR can provide the daily flood inundation with higher accuracy due to its high spatio-temporal resolution. Furthermore, the DWAAI can identify the transformation from droughts to floods in a relatively short period. Consequently, the distributions of and variations in flood inundation under extreme weather conditions can be identified on a daily scale with good accuracy using the CYGNSS data.

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique provides a new remote sensing method that shows great potential for soil moisture detection and vegetation growth, as well as for climate research, water cycle management, and ecological environment monitoring. Considering that the land surface is always covered by vegetation, it is essential to take into account the impacts of vegetation growth when detecting soil moisture (SM). In this paper, based on the GNSS-IR technique, the SM was retrieved from multi-GNSS and multi-frequency data using a machine learning model, accounting for the impact of the vegetation moisture content (VMC). Both the signal-to-noise ratio (SNR) data that was used to retrieve SM and the multipath data that was used to eliminate the vegetation influence were collected from a standard geodetic GNSS station located in Nanjing, China. The normalized microwave reflectance index (NMRI) calculated by multipath data was mapped to a normalized difference vegetation index (NDVI), which was derived from Sentinel-2 data on the Google Earth Engine platform to estimate and eliminate the influence of VMC. Based on the characteristic parameters of amplitude and phase extracted from detrended SNR signals and NDVI derived from multipath data, three machine learning methods, including random forest (RF), multiple linear regression (MLR), and multivariate adaptive regression spline (MARS), were employed for data fusion. The results show that the vegetation effect can be well eliminated using the NMRI method. Comparing MLR and MARS, RF is more suitable for GNSS-IR SM inversion. Furthermore, the SM reversed from amplitude and phase fusion is better than only those from either amplitude fusion or phase fusion. The results prove the feasibility of the proposed method based on a multipath approach to characterize the vegetation effect, as well as the RF model to fuse multi-GNSS and multi-frequency data to retrieve SM with vegetation error-correcting.

Soil moisture is an important geophysical parameter for studying terrestrial water and energy cycles. It has been proven that Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) can be applied to monitor soil moisture. Unlike the Global Positioning System (GPS) that has only medium earth orbit (MEO) satellites, the Beidou Navigation Satellite System (BDS) also has geosynchronous earth orbit (GEO) satellites and inclined geosynchronous satellite orbit (IGSO) satellites. Benefiting from the distribution of three different orbits, the BDS has better coverage in Asia than other satellite systems. Previous retrieval methods that have been confirmed on GPS cannot be directly applied to BDS MEO satellites due to different satellite orbits. The contribution of this study is a proposed multi-satellite soil moisture retrieval method for BDS MEO and IGSO satellites based on signal-to-noise ratio (SNR) observations. The method weakened the influence of environmental differences in different directions by considering satellite repeat period. A 30-day observation experiment was conducted in Fengqiu County, China and was used for verification. The satellite data collected were divided according to the satellite repeat period, and ensured the response data moved in the same direction. The experimental results showed that the BDS IGSO and MEO soil moisture estimation results had good correlations with the in situ soil moisture fluctuations. The BDS MEO B1I estimation results had the best performance; the estimation accuracy in terms of correlation coefficient was 0.9824, root mean square error (RMSE) was 0.0056 cm³cm⁻³, and mean absolute error (MAE) was 0.0040 cm³cm⁻³. The estimations of the BDS MEO B1I, MEO B2I, and IGSO B2I performed better than the GPS L1 and L2 estimations. For the BDS IGSO satellites, the B1I signal was more suitable for soil moisture retrieval than the B2I signal; the correlation coefficient was increased by 19.84%, RMSE was decreased by 42.64%, and MAE was decreased by 43.93%. In addition, the BDS MEO satellites could effectively capture sudden rainfall events.

Tropical deforestation driven by agricultural expansion to meet growing food demand poses a rising threat to the global carbon cycle. While previous studies quantified agriculture‐related carbon losses, the role of cropland expansion remains poorly resolved, partly because widely used agricultural driver data sets are too coarse to detect fine‐scale forest‐to‐cropland conversion, especially in topographically complex regions. Here we integrate multiple high‐resolution satellite observations to show that tropical forest‐to‐cropland conversion reached 1.08 Mha yr ⁻¹ during 2004–2019, accelerating by 0.35 ± 0.07 Mha yr ⁻¹ per four‐year period ( p < 0.05). These conversions shifted toward steeper terrains, with mean slope rising by 0.16 ± 0.05° per period. Annual forest carbon loss nearly tripled from 46 to 130 TgC yr ⁻¹ , increasing at 30 ± 6 TgC yr ⁻¹ per period ( p < 0.05). Our findings reveal a systematic shift of agricultural frontiers toward marginal, carbon‐rich tropical forests, underscoring the need for more targeted land‐use governance to support global climate mitigation goals.