Remote sensing using GNSS-reflectometry (GNSS-R) delay-Doppler maps (delay-Doppler maps) over land is an emerging field. Understanding the sensitivity of delay-Doppler maps to geophysical variables of interest, such as soil moisture and vegetation, is needed to understand the performance limitation of their retrievals. This work presents an analytical sensitivity analysis of GNSS-R delay-Doppler maps to land surface variables using the IGOT model and the Cyclone GNSS (CYGNSS) data over three areas with various topographical terrains. The IGOT model is an electromagnetic DDM model for land applications that is valid for topographical terrains with bare-to-intermediate vegetation cover. It divides the surface roughness into three scales. The large scale is governed by a digital elevation model (a DEM), whereas the other two are stochastic. The vegetation effect is modeled as an attenuation layer. Sensitivity to soil moisture, multiple scales of roughness, and vegetation are among the studied land surface variables. Furthermore, numerical results are obtained using realistic scenarios derived from CYGNSS observations. This work finds that the DDM peak reflectivity sensitivity to the large-scale surface roughness parameter and incidence angles varies depending on the geometry of the transmitter, receiver, and specular point. Moreover, the DDM peak reflectivity is inversely proportional to vegetation (due to attenuation effects) and small-scale surface roughness, which is consistent with the literature. Additionally, CYGNSS data over the study are analyzed to study the effect of incidence and azimuth angles. The analysis confirms that the behavior of peak reflectivity varies depending on both angles.

Accurate assessment of areas burned in wildfires is vital for various monitoring, management, and spread modeling applications. Wildfires, especially in forested regions, pose immense challenges for precise mapping due to the inherent dynamics of fuel types and terrain complexities. While remote sensing, particularly satellite imagery, offers an approach to studying burned areas, reliance on such satellite sources introduces challenges in characterizing burned areas amidst dense vegetation and environmental variations. This paper presents a mapping of forested burned areas utilizing global navigation satellite system–reflectometry (GNSS-R) from Cyclone Global Navigation Satellite System (CYGNSS) with ancillary observations from Soil Moisture Active Passive (SMAP) mission and Shuttle Radar Topography Mission (SRTM) using machine learning approaches. We validate the results with existing burned area products and provide maps of representative California fires within CYGNSS coverage. Assimilation of GNSS-R data into the model provides near real-time and high temporal resolution, enabling rapid response and mitigation efforts to fire events.

Global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) are used in various remote sensing applications over land, including retrieving surface soil moisture. Analyzing GNSS-R DDM sensitivity to such geophysical variables aids in understanding retrieval limitations and performance. This work presents a sensitivity analysis of DDMs to vegetation and soil moisture. The analysis uses the improved geometric optics with topography (IGOT) model, which computes DDMs over topographical lands with bare to intermediate vegetation. This IGOT model was validated using Cyclone GNSS (CYGNSS) DDMs over various validation sites. Analytical sensitivity results of the IGOT model are presented in this paper. Specifically, DDM is sensitive to the amplitude of the Fresnel reflection coefficient of the surface, which incorporates soil moisture. Furthermore, DDM is sensitive to the secant of the incidence angle multiplied by the surface normalized bistatic radar cross section (NBRCS), which includes an exponential term of the vegetation parameter.

We develop and verify a space-based, distributed, adaptive intelligent, responsive New Observing System (NOS) to improve wildfire response decisions by monitoring and forecasting fuel flammability and wildfire spread and providing on-demand fire danger and burnt area maps. We use Global Navigation Satellite System Reflectometry (GNSS-R) as the NOS, informed by improvements to existing frameworks - D-SHIELD (Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions) and WRFx (Weather Research and Forecasting Fire Spread Model). Five new products are developed/enhanced using GNSS-R data from CYGNSS (7-sat NASA mission) and Spire Global (commercial fleet) and assimilated into WRFx, which improves existing USGS fire danger and LANDFIRE fuel layers products. These products are expected to inform observation planning and fire management via an observation value framework and a fire forecast reporter that we develop. Adaptive intelligence to dynamically task the observing (satellites) and planning (ground stations) assets, and synchronization between them, is achieved using novel Monte Carlo Tree Search (MCTS) based planner.

While microwave imaging satellites, such as the NASA Soil Moisture Active Passive (SMAP), can provide reliable estimates of surface soil moisture at km resolution, the temporal frequency of observations is on the order of days. To increase the temporal frequency of observations, a new class of approaches considers global navigation satellite system (GNSS)-reflectometry (GNSS-R) signals. In this work, we consider observations from the NASA Cyclone GNSS (CYGNSS) constellation, as well as auxiliary observations, and seek to provide instantaneous soil moisture estimates. To achieve accurate retrievals, a novel machine learning approach for probabilistic regression is considered, namely the NGBoost. In addition to achieving an accuracy comparable to previous approaches employing state-of-the-art machine learning methods, the considered framework also provides prediction intervals to quantify prediction uncertainty. Using observations from the Yanco SMAP core validation site in southeast Australia over a period of three years, we quantify the performance in terms of both retrieval accuracy and associated uncertainty. Furthermore, using noisy observations, we experimentally demonstrate the impact of input noise on the prediction uncertainty.

In the last decade, multiple global navigation satellite system (GNSS)-reflectometry (GNSS-R) space missions have been launched, including the UK-DMC satellite, the TechDemoSat-1 (TDS-1) satellite, the Cyclone GNSS (CYGNSS) constellation, the FMPL-2 instrument, the Spire GNSS-R CubeSats, and the BuFeng-1 A/B twin satellites. In parallel, multiple electromagnetic models have been developed to calculate the scattered GNSS-R signals over land [1] - [5] .

Monitoring soil moisture enables detailed understandings of its role in the water cycle and how it is affected by climate change. Multiple airborne and spaceborne missions have been dedicated to estimating soil moisture, including Soil Moisture Active Passive (SMAP) [1] , Soil Moisture and Ocean Salinity (SMOS) [2] , and Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) [3] . Some other remote sensing missions have added soil moisture retrievals along with their primary goal. For instance, the primary objective of the Cyclone Global Navigation Satellite System (CYGNSS) mission [4] is wind speed retrieval over the ocean, but it is now also used for soil moisture retrieval, among other applications [5] , [6] . All these remote sensing systems and future systems need in situ soil moisture measurements to validate their products.

Lidar elevation maps are utilized in a bistatic electromagnetic scattering model of the Cyclone Global Navigation Satellite System over San Luis Valley in Colorado, USA. The results are compared with results generated using the Shuttle Radar Topography Mission (SRTM) 1 arcsecond global digital elevation model (DEM). The high resolution lidar maps are also used to generate maps of surface roughness at length scales finer than the horizontal resolution of SRTM. Model results using these maps illustrate the importance of the spatial variation of surface roughness on reflectometry signals. Results also highlight the shortcomings of using SRTM in scattering models and show improvements when a lidar DEM is used.

The majority of the soil moisture estimation algorithms using radars in the literature are for retrievals using a single instrument or not optimized for retrievals using multiple radars. A method for retrieving soil moisture using polarimetric radars at multiple frequencies is presented. The method uses a forward model and a hybrid local and global optimizer to retrieve soil moisture. Monte Carlo simulations of soil moisture retrieval using a maximum of four radars with different frequencies and incidence angles have been performed to assess the performance of the algorithm for various vegetation types and realistic instrument noise models. The simulation results show a mean unbiased root mean square error (ubRMSE) of less than 0.01 m3m-3, and a mean bias less than 0.005 m3m-3. The mean ubRMSE and bias values were quite small under the assumption that vegetation properties and surface roughness are known.

A comparison of three different electromagnetic scattering models for land surface delay-Doppler maps (DDMs) obtained from global navigation satellite system reflectometry (GNSS-R) along a Cyclone Global Navigation Satellite System (CYGNSS) track in the San Luis Valley, Colorado, USA, is presented. The three models are the analytical Kirchhoff solutions (AKS), the Soil And VEgetation Reflection Simulator (SAVERS), and the improved geometrical optics with topography (IGOT). Common inputs to the three models were defined by using field samples of soil moisture and texture, soil surface roughness measurements, and a digital elevation model (DEM). The resulting peak reflectivity profiles of the models and the CYGNSS data all had a range of 10 dB along the selected track, mainly due to the influence of topography. The reflectivities obtained from all three models agreed with one another to within 2.4 dB along the full length of the track. The models also showed general agreement with the corresponding CYGNSS data, although the modeled profiles were higher than CYGNSS Science Data Record Version 3.1 by an average of 5 dB and also smoother. Additional characterization of fine-scale surface roughness is identified as an area for future work to improve model fidelity. An intercomparison of DDM structure for three selected acquisitions is also provided.

A comparison of three different electromagnetic scattering models for delay-Doppler maps (DDMs) of global navigation satellite system reflectometry (GNSS-R) from land is performed along a Cyclone Global Navigation Satellite System (CYGNSS) track over a validation site in the San Luis Valley, Colorado, USA. The peak reflectivity profiles of all three models and of the corresponding CYGNSS data are found to be in general agreement and are strongly influenced by topography. An intercomparison of DDM structure for one acquisition is also included. Efforts to refine the model results using a high resolution lidar survey are ongoing.

This work presents recent field activities in support of the NASA CYGNSS missions' land applications. Land reflected GNSS signals are known to be sensitive to surface topography, vegetation cover, and soil moisture. To better understand CYGNSS sensitivity to surface conditions, especially freeze-thaw states, two SoilSCAPE wireless in situ network sites were deployed in the San Luis Valley (SLV), CO, in late Oct. 2019. These sites capture similar weather and climatic conditions but have contrasting topography and vegetation cover. Initial analysis of CYGNSS Signal-to-Noise (SNR) observations over SLV indicates the need to fully account for land-scape topography. To this end, a forward wave scattering model that incorporates a Digital Elevation Model (DEM)is currently being developed to help explain the effects of local topography on SNR observations.