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 SM 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 (SNR) 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 (NSNR) to account for the deviations in double-scale coefficients, which depend on the variations in vegetation and surface roughness between 36 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.

To fully leverage the potential of GNSS-R technology for high spatiotemporal resolution SM and F/T retrieval, this study first proposed an observation reconstruction method to address the mutual constraints of spatiotemporal resolution on GNSS-R gridded observations. The reconstructed CYGNSS observations were then utilized for SM and F/T retrievals over the Qinghai-Tibet Plateau region. The RMSE and R of the SM retrieval are 0.063 cm3/cm3and 0.52, respectively, which are comparable to the accuracy of the SM retrieval performed before reconstruction. Similarly, the accuracy of the F/T retrieval was 84.1%. This is also comparable to the accuracy of the F/T retrieval performed before reconstruction. Independent evaluation of the local in situ sites also showed consistent performance with the SM and F/T results from the original CYGNSS observations. Notably, the temporal resolution of the CYGNSS reconstructed observations was improved by 315% over the original CYGNSS 9 km observations.

This study introduces a method that combines Soil Moisture Active Passive (SMAP) radiometer and Cyclone Global Navigation Satellite System (CYGNSS) reflectometer to derive a 9 km quasi-global microwave land surface emissivity product. This algorithm aims to utilize CYGNSS reflectivity to capture spatial heterogeneity at 9 km scale, so as to create an emissivity product with a spatial resolution of 9 km and a temporal resolution of 2 days in the area of interest. The global results demonstrate the satisfactory performance of the SMAP/CYGNSS 9 km emissivity, with a median repeat period of 2.5 days for most mid-latitude regions and 4.7 days for all regions. Compared to the SMAP emissivity that has a native 36 km spatial resolution and a revisit time of 2-3 days, the daily SMAP/CYGNSS 9 km emissivity covers approximately 40% of the United States, increasing to 75% for a 3-day average emissivity.

The emergence of seamless mobile navigation systems integrating various Internet of Things (IoT) devices has sparked interest in context awareness enhancement technology. In the concept of advanced adaptive integrated navigation technology, the context comprises two key elements: environment characteristics and carrier behaviors, which are not entirely independent, especially in certain scenarios. Leveraging the abundant sensors in smartphones, a model combining context connectivity and behavior association is developed to detect environment scenes accurately with low energy consumption across outdoor, semi-outdoor, and indoor spaces. The model comprises three main parts: sensor-based SVM (Support Vector Machine), behavior-aided HMM (Hidden Markov Model), and classifier combination. The parameters of a behavior-aided HMM are adjusted by behavioral probabilities and a specified EMA method. Four classifier combination techniques, including SA, EWA, EBWA, and stacking, are used to integrate the environment detecting strengths of multiple smartphone sensors. The proposed model is evaluated on a dataset collected from a complex building at Wuhan University and achieves a best environment detection accuracy of 94.22% with stacking ensemble technique. The multisensor model outperforms the other three classifier combination techniques, improving detection accuracy by 6.93% compared to a GNSS-supported model. The proposed model has certain advantages over high recognition accuracy, low model consumption compared to the main existing environment detection models.

The demand for high-precision navigation and positioning is growing rapidly with the advancements in autonomous driving and intelligent robotics. Nowadays, many studies have been conducted on the fusion of the global navigation satellite system (GNSS), inertial navigation systems (INS), and light detection and ranging (LiDAR) because of their complementary characteristics. Notably, in multi-source fusion, it is crucial to precisely model the uncertainty (covariance) of each sensor. While GNSS and IMU covariance modeling are mature, LiDAR covariance modeling remains unsophisticated, resulting in a suboptimal fusion of GNSS/INS/LiDAR. In this work, the LiDAR covariance modeling method is proposed, including the original point cloud covariance, the localmap covariance, and the observation covariance modeling. A point cloud selecting approach based on the eigenvectors and centroids is also introduced to maintain valid surface and edge feature points, thereby reducing repeated observations. These above methods are applied to the tightly coupled RTK/INS/LiDAR_PPL (G+I+PPL) system based on the Multi-State Constraint Kalman Filter (MSCKF) to further improve positioning accuracy and covariance consistency. The experimental results show that the proposed method has better covariance consistency in comparison to LIOSAM and FASTLIO. Meanwhile, G+I+PPL with fused point-to-plane/line (PPL) observations outperforms compared to RTK/INS (G+I) and RTK/INS/LiDAR_CP (G+I+CP) using closest point (CP) observations, in the right, front, and up (RFU) directions, demonstrating a positioning improvement in performance by (50.7%, 58.6%, 54.3%) and (46.2%, 55.0%, 58.8%), respectively.

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 cm3/cm3, respectively, while the R and RMSE of the 3 km grid are 0.68 and 0.041 cm3/cm3, 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.

Previous studies have reported that signal penetration will introduce an underestimation of snow depth, referred to as the snow depth difference. So far, however, there have been few detailed investigations into the relationship between snow depth difference and signal-to-noise ratio (SNR) metrics. In this study, we briefly describe the snow depth difference and provide a physical explanation of the systematic negative error. The baseline- and short-term variations of snow depth difference and SNR metrics were identified, and their relationships during various snow periods were investigated. The results indicated that the systematic negative errors and SNR metrics during the stable and melting periods are dominated by the layered structures and liquid water content of snowfall, respectively. Meanwhile, compared with the baseline terms, the short-term variations of snow depth difference and SNR metrics were more sensitive to fresh, low-density snowfall over the old snow surface. Additionally, an improved method is proposed to compensate for systematic differences using 2- and 5-parameter multiple linear regression (MLR) models with SNR metrics as independent variables. The results showed that the compensation values conformed with the measured values with correlation coefficients exceeding 0.85. In terms of accuracy, once the MLR models were applied, the root mean squared errors (RMSEs) decreased from 22.05 cm to 3.89 cm and 3.40 cm, respectively. Moreover, the corrected estimates agree well with the meteorological records, with regression slope deviations of less than 2% and correlation coefficients of over 0.97, suggesting no systematic errors between the estimates and reference.