Deadwood is a critical component of forest ecosystems, storing nutrients for plants and serving as a carbon store and emission source. Climate change influences forest ecosystem dynamics with the potential for deadwood to emit carbon more rapidly due to accelerated decay and increased wildfires and increased inputs via mass forest mortality and disturbance events. To objectively inform our understanding of wildfires and associated carbon emissions, this study estimates the carbon content of dead fine woody debris (FWD) using multimodal data, such as Landsat-8 multispectral imagery, Sentinel-1 (C-band) and PALSAR (L-band) synthetic aperture radar (SAR) imagery, and terrain features to estimate the FWD of less than 0.25 in (1 h), 0.25–1 in (10 h), and 1–3 in (100 h). This data fusion provides spectral information to assess vegetation health that correlates with deadwood, as well as penetrability from SAR, resulting in structural information and biomass sensitivity. An ensemble machine learning (ML) model was trained using measurements from the Forest Inventory and Analysis (FIA) Database. A feature importance analysis was also performed to investigate the importance of input features to the model’s performance. A super learner regression (SLR) model composed of 9 base learners, including an ElasticNet model as meta-learner, was proposed and achieved the$R^{2}$values of 0.75, 0.72, and 0.62 to estimate 1-, 10-, and 100-h FWD, respectively. The validated model was then used to estimate deadwood carbon in the 2021 Dixie Fire region of California, demonstrating the effectiveness of our approach, emphasizing the value of multimodal data for real-time FWD carbon stock estimation.

High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions are unable to directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV)–mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval uncertainty. This study addresses both gaps by evaluating SM retrievals from a drone-based Portable L-band Radiometer (PoLRa) across shrubland, bare soil, and forest strips in Central Illinois, U.S., using a 10-day field campaign in 2024. Controlled UAV flights at altitudes of 10 m, 20 m, and 30 m were performed to generate brightness temperatures (TB) at spatial resolutions of 7 m, 14 m, and 21 m. SM retrievals were carried out using multiple tau-omega-based algorithms, including the single channel algorithm (SCA), dual channel algorithm (DCA), and multi-temporal dual-channel algorithm (MT-DCA). A Bayesian inference framework was then applied to provide probabilistic uncertainty characterization for both SM and vegetation optical depth (VOD). Results show that the gridded TBdistributions consistently capture dry-wet gradients associated with vegetation density variations, and spatial correlations between polarized observations are largely maintained across scales. Validation againstin situmeasurements indicates that PoLRa-derived SM retrievals from the SCA-V and MT-DCA algorithms achieve unbiased root-mean-square errors (ubRMSE) generally below 0.04 m3/m3across different land covers. Bayesian posterior analyses confirm that reference SM values largely fall within the derived uncertainty intervals, with mean uncertainty ranges around ± 0.02 m3/m3and ± 0.11 m3/m3for SCA and DCA-related retrievals. These findings underscore the potential of UAV-mounted PoLRa for high-resolution SM retrieval across varied landscapes and emphasize the need for standardized calibration and uncertainty quantification frameworks to support broader scientific and operational adoption.

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed to predict the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers’ permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors.

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which are typically unknown in agricultural and forest fields. In this paper, we leverage the mobility of an unmanned aerial system (UAS) to collect spatially-diverse radar measurements, enabling the joint estimation of soil moisture, above-ground biomass moisture, and biomass height, all without assuming any calibration steps. We leverage the changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of reflected radar signals as the UAS flies above a reflector buried under the soil. We demonstrate the effectiveness of our algorithm by simulating its performance under realistic measurement noises as well as conducting lab experiments with different types of above-ground biomass. Our simulation results conclude that our algorithm is capable of estimating volumetric soil moisture to less than 1% median absolute error (MAE), vegetation height to 11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental results demonstrate the effectiveness of the proposed method in practical scenarios for varying biomass moistures and heights.

Accurate fuel condition assessment is crucial for predicting fire behavior, enhancing operational decision support, and improving overall fire management. Our approach utilizes diverse data sources, such as Landsat-8 optical imagery, Sentinel-1 (C-band) SAR imagery, PALSAR (L-band) SAR imagery, and terrain features, to estimate time-lag fuel loadings (1 hour, 10 hours, and 100 hours). Optical data mainly captures the characteristics of leaf and forest canopy, while SAR data is more sensitive to forest vertical structures due to its strong penetrability. An ensemble model was trained on the Forest Inventory and Analysis (FIA) plots and spectral indices. Followed by, feature importance analysis and the inclusion of polynomial features were undertaken. The ensemble strategy, involving neural networks, decision trees, gradient boosting, and ensemble methods, achieved R2values of 0.72, 0.70, and 0.60 for 1-hour, 10-hour, and 100-hour fuel loads. Extensive experimentation in the 2021 Dixie Fire incident validates the effectiveness of our approach, emphasizing the value of leveraging multimodal data and ensemble machine learning models for real-time fuel load estimation.

Coordination across multiple disciplines is necessary for current and future generations of modeling systems and the decision-support tools used for understanding and managing wildland fires. This coordination manifests in active-fire campaigns involving practitioners, modelers, and data-collection and management groups to produce datasets for training and evaluating models and underlying theories upon which they are built. New approaches and technology often are involved in these efforts, and in many cases their development is specified or driven by requirements identified by data-collection or model-evaluation activities. The Fire and Smoke Model Evaluation Experiment (FASMEE) is an effort to 1) conduct large-scale, active-fire data collection in order to produce a library of wildland fire model inputs 2) to advance wildland fire and smoke models, decision tools, and underlying science, and 3) to encourage disciplinary cross-training, diversity of backgrounds, and the development of new partnerships and technology in wildland fire science.

The use of coarse scale and temporally static effective roughness and single scattering albedo parameters in the Soil Moisture Active Passive (SMAP) operational algorithms falls short in achieving high spatial resolution in soil moisture (SM) retrieval. To address this issue, this study leverages in-situ SM measurements from 2016 to 2018 at the SMAP core validation sites to derive monthly parameters. These parameters were then applied to estimate SM for subsequent periods. Our findings suggest that SM retrievals from these monthly-adjusted parameters exhibit a marginal improvement in mean absolute error relative to the standard SMAP retrievals. This scheme will be applied to develop high spatiotemporal resolution parameters for passive microwave SM retrieval from instruments aboard uncrewed aerial systems (UAS). The proposed method also allows for these parameters to be tailored to targeted agricultural and forested areas for applications ranging from precision agriculture to intelligent wildfire management. This adaptation promises to refine our ability to retrieve SM with greater accuracy and resolution.

Wildfire fuel mapping plays a vital role in understanding and mitigating the risks associated with wildfires. This study conducts a comprehensive analysis of existing literature to investigate the prevailing trends in wildfire fuel mapping, including an analysis of the satellite sensors commonly used for fuel mapping, the predominant types of fuels mapped, the resolution of fuel maps to assess accuracy and detail, and the publication trends over the years to understand the growth and interest in the field. By leveraging AI techniques including machine learning, we review solutions to overcome challenges such as data scarcity, modalities, mapping understory vegetation, model explainability, and lack of uncertainty-aware models. This manuscript aims to serve as a reference for researchers and practitioners seeking to advance the field of wildfire fuel mapping through data-driven and AI-powered approaches.

Accurate forest biomass estimation is crucial for effective wildfire risk assessment and management as well as in agricultural applications. However, existing remote sensing methods cannot estimate under-canopy biomass. This paper investigates the feasibility of a novel approach that leverages ultra-wideband radars mounted on uncrewed aerial systems in conjunction with reference ground reflectors to estimate the under-canopy biomass profile. Through extensive electromagnetic wave propagation simulations encompassing diverse material configurations, we investigate the sensing capabilities of our proposed technique. We leverage deep learning to effectively extract biomass information from radar signals, enabling the prediction of material properties and geometries. The developed deep learning system was successfully trained with an R2metric of 98% in testing and produced dielectric permittivity distributions that visually match the target configurations. The results underscore the potential of our approach in advancing wildfire management strategies, bolstering carbon sequestration efforts, preserving forests, and aiding agricultural applications by facilitating accurate biomass characterization and estimations of crop yield and soil moisture.

We demonstrate Platypus, a sub-millimeter micro-displacement sensing system presented in [3]. Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery parts can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Platypus enables sub-millimeter level sensing accuracy by using mmWave backscatter tags and their reflection as phase carriers to shift the phase changes due to tiny displacements to clean frequency bins for precise tracking. It then reconstructs the tag phase changes with sub-millimeter level accuracy even from extended ranges (over 100m) or in non-line-of-sight (NLoS) situations where the tag is blocked by other objects. Here, we demonstrate Platypus’s performance by attaching a Platypus tag to a stepper motor-driven motion-stage and demonstrating the micro-displacement detection in real time, and the system robustness against multipath and occlusions.

Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery displace can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Traditionally, such displacements on large structures are measured using visual sensing platforms or advanced surveying equipment. However, they either fall short in varying weather and lighting conditions or require installation and maintenance of high-power sensing platforms that are expensive to deploy at scale, especially if continuous measurements are desired.