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.

Soil moisture is an essential climate variable that directly influences many hydrological, agricultural, and water-cycle processes. Many satellites have been launched and are still being launched to map soil moisture accurately at a global scale. Motivated by the coarse resolution of existing satellite products, many statistical, physics-based, and machine learning-based methods have been proposed to downscale soil moisture to much finer spatial scales. In this paper, we propose a novel deep learning approach that is constrained by the Tau-omega radiative transfer model to enhance the resolution of surface soil moisture. We demonstrate the proposed framework by downscaling Soil Moisture Active Passive (SMAP) L-band brightness temperature (TB) with C-band synthetic aperture radar (SAR) backscattering coefficient (σ0) imagery from Sentinel-1A/B and subsequently retrieving high spatial resolution (1km) soil moisture.

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.

Numerous satellite sources measure global soil moisture, but the spatial resolution of these products is usually coarse making it challenging to use them for various science applications. In this work, we develop a conditional generative adversarial network-based model to downscale satellite measured brightness temperature. Augmenting a physics-based Tau-omega model to the framework, high resolution soil moisture is retrieved. For training and validation, upscaled in-situ soil moisture measurements are utilized. Experimental results suggests that the proposed framework captures the soil moisture variations from in-situ sensors and helps in substantially improving the resolution of passive microwave satellite soil moisture maps.

A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.

In this paper we develop a Convolutional Long Short-term memory (ConvLSTM) model, a time series deep learning neural network, to predict soil moisture, with an add-on module of passive microwave (radiometer) soil moisture retrieval using the Tau omega model. We incorporate antecedent observations, landscape properties, and forcing factors such as precipitation, landcover, clay fraction, and brightness temperature in the prediction scheme. A regularization Monte Carlo Dropout layer is added to the network to remove stochasticity and avoid overfitting during the training phase. This dropout layer also provides a Bayesian approximation to quantify uncertainty during forecasting. The model is validated at four Soil Moisture Active Passive (SMAP) Cal/Val locations using performance metrics such as Root Mean Square Error (RMSE) and Bias to evaluate effectiveness of the proposed method. This model is developed as a component of the Science Simulator within the Distributed Spacecraft with Heuristic Intelligence to Enable Logistical Decisions (D-SHIELD) project.