Highlights: What are the main findings? The trained ANN algorithm simultaneously retrieves soil moisture and vegetation optical depth from CYGNSS observations, showing strong agreement with reference satellite products (SMAP SM: R = 0.83, RMSE = 0.063 m ³/m ³; SMOS VOD: R = 0.89, RMSE = 0.088). ANN-derived VOD shows strong correlation with independent vegetation indicators—biomass (R~0.77), canopy height (R~0.95), Leaf Area Index (R = 0.96), and vegetation water content (R~0.90)—confirming reliable sensitivity to vegetation structure. What are the implications of the main findings? The combination of GNSS-R data with environmental variables enables reliable dual retrieval of soil moisture and vegetation optical depth, serving as a cost-effective, higher-resolution alternative/complement to SMAP and SMOS. Joint retrieval of SM and VOD enables improved characterization of land–atmosphere interactions, supporting hydrological, ecological, and climate applications. Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m ³/m ³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks.

Although Light Detection and Ranging (LiDAR) technology is currently one of the most efficient methods for acquiring high-density point cloud, there are still challenges in terms of data reliability. In particular, the accuracy assessment of LiDAR data, especially in the height component, is one of the main issues in this context. This study introduces a rapid and cost-effective platform to improve the accuracy and precision of LiDAR data by integrating high-density GNSS-Ranging measurements with LiDAR data. The platform offers the capability to rapidly collect a significant number of network real time kinematic (NRTK) points with centimetric precision. A continuous correction surface is proposed to integrate the platform and LiDAR data, resulting in improved accuracy for all ground-class LiDAR data. Evaluation using GNSS benchmarks and NRTK checkpoints showed a significant reduction in LiDAR height errors after applying the correction surface. The root mean squares error (RMSE) decreased from 18.5 cm to 8.2 cm when compared to GNSS benchmarks and from 17.4 cm to 5.3 cm for approximately 1000 NRTK control points. The results indicate that collecting a large number of high-density GNSS ground targets and applying a correction surface to LiDAR height data significantly enhance the accuracy and precision of the LiDAR extracted products.

Until now, RTK (real-time kinematic) and NRTK (Network-based RTK) have been the most popular cm-level accurate positioning approaches based on Global Navigation Satellite System (GNSS) signals in real-time. The tropospheric delay is a major source of RTK errors, especially for medium and long baselines. This source of error is difficult to quantify due to its reliance on highly variable atmospheric humidity. In this paper, we use the NRTK approach to estimate double-differenced zenith tropospheric delays alongside ambiguities and positions based on a complete set of multi-GNSS data in a sample 6-station network in Europe. The ZTD files published by IGS were used to validate the estimated ZTDs. The results confirmed a good agreement, with an average Root Mean Squares Error (RMSE) of about 12 mm. Although multiplying the unknowns makes the mathematical model less reliable in correctly fixing integer ambiguities, adding a priori interpolated ZTD as quasi-observations can improve positioning accuracy and Integer Ambiguity Resolution (IAR) performance. In this work, weighted least-squares (WLS) were performed using the interpolation of ZTD values of near reference stations of the IGS network. When using a well-known Kriging interpolation, the weights depend on the semivariogram, and a higher network density is required to obtain the correct covariance function. Hence, we used a simple interpolation strategy, which minimized the impact of altitude variability within the network. Compared to standard RTK where ZTD is assumed to be unknown, this technique improves the positioning accuracy by about 50%. It also increased the success rate for IAR by nearly 1.