This paper presents a learning-based, physics-aware soil moisture (SM) retrieval algorithm for NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission. The goal of the proposed novel method is to advance CYGNSS-based SM estimations, exploiting the spatio-temporal resolution of the GNSS reflectometry (GNSS-R) signals to its highest potential within a machine learning framework. The methodology employs a fully connected Artificial Neural Network (ANN) regression model to perform SM predictions through learning the nonlinear relations of SM and other land geophysical parameters to the CYGNSS observables. In situ SM measurements from several International SM Network (ISMN) sites are used as reference labels; CYGNSS incidence angles, derived reflectivity and trailing edge slope (TES) values, as well as ancillary data, are exploited as input features for training and validation of the ANN model. In particular, the utilized ancillary data consist of normalized difference vegetation index (NDVI), vegetation water content (VWC), terrain elevation, terrain slope, and h-parameter (surface roughness). Land cover classification and inland water body masks are also used for the intermediate derivations and quality control purposes. The proposed algorithm assumes uniform SM over a 0.0833 ${}^{\circ }\times$ 0.0833 ${}^{\circ }$ (approximately 9 km × 9 km around the equator) lat/lon grid for any CYGNSS observation that falls within this window. The proposed technique is capable of generating sub-daily and high-resolution SM predictions as it does not rely on time-series or spatial averaging of the CYGNSS observations. Once trained on the data from ISMN sites, the model is independent from other SM sources for retrieval. The estimation results obtained over unseen test data are promising: SM predictions with an unbiased root mean squared error of 0.0544 cm ${}^3$ /cm ${}^3$ and Pearson correlation coefficient of 0.9009 are reported for 2017 and 2018.

SCoBi-Veg stands for Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains. It simulates polarimetric reflectometry of vegetation canopy over a flat ground using a Monte Carlo scheme. The model is aimed at assessing the value of navigation and communication satellite Signals of Opportunity in a range of frequencies from P- to S-bands for remote sensing of a number of geophysical land parameters such as soil moisture and biomass. A fully polarimetric expression for bistatic scattering from a vegetation canopy is first formulated for a general case and is then specialized to the practical case of ground-based/low-altitude platforms with passive receivers overlooking vegetation using the signals transmitted from large distances. Using analytical wave theory in conjunction with distorted Born approximation, the transmit and receive antenna effects (i.e., polarization crosstalk/mismatch, orientation, and altitude) are explicitly accounted for. The forward model developed here enables the understanding of the effect of different geophysical parameters and system configurations on the coherent and incoherent components of the reflected signatures. It can thus help developing robust inverse algorithm for extraction of soil moisture and biomass. The model is applied to P-band signals of geostationary communication satellites to describe polarimetric reflections from tree canopies as observed from down-looking platforms at various altitudes. The relative contributions of diffuse and specular scattering on total reflected power and reflectivity are quantified for various observing scenarios.

Global navigation satellite system reflectometry (GNSS-R) has the potential to offer a cost-effective solution for global land observations. In this study, we aim to understand GNSS-R sensitivity to changing land geophysical parameters. For this objective, we performed simulations of a ground-based receiver using a recently developed coherent bistatic vegetation scattering model (SCoBi-Veg) to detect GNSS-R signatures under varying soil moisture (SM), vegetation water content (VWC), and surface roughness during a full corn growing season. We modeled different corn growth stages by using in situ measurement data. We analyzed the simulated reflectivity and received power values based on the aforementioned variable input parameters. This study demonstrates that specular reflections dominate the diffusely scattered contribution in case of moderate roughness, regardless of the corn field row structure or the polarization. Significant correlations between VWC and cross-polarized reflectivity values are also shown. Furthermore, the study quantifies the effects of SM and surface roughness on GNSS-R deliverables.

Recently, unmanned aerial vehicles (UAVs) have become one of the most popular and promising means for both military and civilian posts and academic research areas. Localization of the UAVs and persistent tracking of a UAV have vital importance to provide a UAV with navigation information and help to cope with getting lost permanently. Indeed, Inertial Navigation System (INS) and Global Positioning System (GPS) seem to be adequate for navigation of UAVs. However, an alternative augmented navigation system for UAVs should be taken into consideration since INS has accumulated errors and GPS always has the possibility of jamming and satellite signal loss. Terrain Referenced Navigation (TRN) could be a good alternative as a decision support system for these main systems. This study aims to detect the location of a lost or GPS-disabled UAV throughout a planned flight by using only the terrain data. In addition, assumptions and limitations are minimized for the sake of simplifying the process to apply this methodology on a real UAV in the future, e.g. flight through all directions with physically possible turn rates is allowed. In order to provide data of the terrain, Digital Elevation Model (DEM) of the flight region with 30m resolution is exploited. The proposed method is based on searching and matching the collected elevation values of the terrain below UAV within the DEM and makes use of simulation techniques to test the accuracy and performance. The whole algorithm utilizes a sequence of elevation values with a predefined length (i.e. profile). Mainly, all possible profiles are generated before the flight and stored in a huge search space. We identify, sort and classify these elevation profiles in order to perform search operations in a small subset of the huge set. During the flight, a sequence of terrain elevations, which is computed with the help of radar and barometric altimeter measurements, is searched within a small neighborhood of corresponding profile set.