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Objectives: Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Methods: Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS signal-to-noise ratio observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN). Results: Comparing the inversion effects of above three models, BPNN model has the best inversion effect, followed by RF model, and LSSVM model is the worst. The results show that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, and BPNN models are 0.830, 0.953, and 0.980, respectively, and the corresponding root mean square errors are 0.045, 0.035 and 0.032 cm³ /cm³ Conclusions: Compared with the single feature inversion method, both the accuracy and correlation coefficient of soil moisture inversion increase significantly. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter to measure the material and energy exchange between soil and atmosphere. It is a key environmental factor in the fields of hydrology, meteorology, agriculture and so on. Remote sensing inversion and model simulation are two basic means to obtain global soil moisture data. Remote sensing inversion can obtain soil moisture data with relatively high resolution, but there are often time-space gaps while land surface model can simulate spatiotemporal continuous data, but the spatial resolution is often coarse. Both two data cannot meet the fine observation of soil moisture. Therefore, it is very important to develop an effective method to obtain high-resolution, spatiotemporal seamless soil moisture data. Methods: Focusing on their complementary advantages, we propose a global seamless soil moisture data generation method integrating microwave remote sensing and model simulation. Specifically, for the vacant area of 9 km soil moisture data of soil moisture active passive satellite, GLDAS Noah 0.25° model assimilation data is introduced to establish the spatiotemporal fusion model between them. Through this model, Noah data is downscaled to 9 km to fill the vacant area. As for some areas that have not been filled, Noah data is used for fitting, interpolation and filling, and the residual correction is further carried out based on Poisson equation method, and then the spatiotemporal seamless 9 km daily soil moisture data is generated. Results: We evaluated the experimental results by simulation experiment evaluation, site data evaluation and triple collocation evaluation. Experiments show that the global seamless soil moisture data produced in this paper performs well in all evaluation methods. Conclusions: This method can effectively combine the spatial resolution advantage of remote sensing observation with the spatiotemporal continuity advantage of model simulation, provide spatiotemporal seamless global soil moisture data, and better meet the needs of global scale water cycle monitoring and water resources management. [ABSTRACT FROM AUTHOR]

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system-reflectometry (GNSS-R ) is a new method for measuring soil moisture with rich signal sources, which utilizes GNSS reflected signals for detecting the physical parameters of the reflector with cheap cost and high spatial resolution. Methods: Considering the fact that in the traditional GNSS-R soil moisture retrieval, the data of land cover types is ignored or simpli fied, this paper makes detailed labels about the land cover types based on the definition of international geo sphere biosphere programme (IGBP) considering the similarities and differences between different land cover types, and uses back propagation (BP) neural network to establish soil moisture inversion models. The main work is to replace the IGBP number labels with refined feature labels. After the extraction, matching and quality control of cyclone global navigation satellite system (CYGNSS) and soil moisture active passive (SMAP) data, the dataset is randomly divided into training set and testing set. Then BP algorithm is used for training neural networks to establish soil moisture retrieval model. Results: The correlation coefficient between retrieved soil moisture and reference value is 0.85, and the root mean square error (RMSE) is 0.060 cm3 /cm3 . Meanwhile, the soil moisture obtained by CYGNSS and SMAP have good spatial and tem poral consistency, but the inversion performance varies on the different land cover types. On the barren or sparsely vegetated land type, the RMSE is smallest and on the evergreen ncedlcleaf forest land type, the RMSE is largest. Meanwhile on the shrublands, grasslands and croplands land types, the inversion effect is better than permanent wetlands, urban/built-up and cropland/natural vegetation mosaic land types. Com‐ pared with the existing IGBP numbering and labeling method, the accuracy has been improved by 7.7%. The precision improvement of deciduous broadleaf forest, closed shrublands and woody savannas types is significant. Conclusions: The proposed method of refined land type digitization can effectively retrieve soil moisture without relying on other auxiliary data such as vegetation opacity and surface roughness, and it is feasible and effective. [ABSTRACT FROM AUTHOR]

Objectives: Global navigation satellite system-interferometric reflectometry can be taken advantage of identifying soil moisture of land surface. Methods: Aiming at the problem of multi-GNSS combined soil moisture inversion, we use the phase extracted from the signal-to-noise ratio observation data of global positioning system (GPS), BeiDou satellite navigation system (BDS), GLONASS and Galileo system, and solve soil moisture inversion within consideration of satellite track clustering by using the empirical model. The estimation of multi-GNSS combination is obtained by weighted average method. Results: The inversion accuracy of BDS and Galileo is equivalent and superior to GPS and GLONASS. The root mean square error of the method in this paper is 0.041 4 cm3 /cm3, which is about 16.3% and 5.2% lower than that of single navigation satellite system and the optimal frequency band respectively. Conclusions: The results indicate that the multi-GNSS reflection signal estimation method based on track clustering can effectively monitor changes in soil moisture. [ABSTRACT FROM AUTHOR]

This paper presents two dual antenna GNSS-R(global navigation satellite system reflectometry) soil moisture retrieval models with soil roughness compensation-an analytic model and an artificial neural network (ANN) model. Then a simulator for GNSS-R soil moisture retrieval is built in consideration of GPS L1 C/A code modulation. After that the impact of soil roughness is elaborated. The simulation results show that the roughness compensation is necessary for the analytic model when the RMSH(root mean square high) is larger than 0.010 m. The roughness compensation works well for small roughness, but there are some limitations for large roughness. Under the situation where RMSH is greater than 0.025 m, the accuracy of ANN model is 36.83%-72.36% higher than the analytic model without roughness compensation, and the accuracy of ANN model is 42.86%-54.40% higher than the analytic model with roughness compensation. The ANN model achieves similar accuracy regardless of roughness compensation, and the accuracy of ANN model without compensation is still 35.83%-53.48% higher than the analytic model with compensation. [ABSTRACT FROM AUTHOR]