Kimberley Rain Miner

Synthetic Aperture Radar (SAR) remote sensing is uniquely suited for characterizing wildland fuels, as microwave backscatter is inherently sensitive to both vegetation structure and moisture content. In this study, we demonstrate that time-series SAR observations directly correlates with weather-driven dead fuel moisture content (DFMC), establishing a physical link between active microwave response and dead fuel moisture dynamics. To isolate this moisture signal from structural scattering, we employed a quadratic regression relating lidar-derived tree canopy cover (TCC) to SAR backscatter (γ^0). This approach successfully removed static canopy effects, yielding a canopy-detrended backscatter residual (Δγ^0) driven primarily by temporal moisture variations. Applying linear regression to this residual produced a good fit for coarse 1000-hr DFMC using L-band SAR (UAVSAR: R^2 = 0.54, RMSE = 2.46%; NISAR: R^2 = 0.58, RMSE = 1.74%), and moderate fit for fine 10-hr DFMC using C-band SAR (Sentinel-1: R^2 = 0.42, RMSE = 4.68%). Evaluating this framework across frequencies revealed a distinct sensitivity governed by physical penetration depths: in moderate-to-highly vegetated areas, C-band backscatter effectively captured the rapid fluctuations of fine surface fuels (1-hr and 10-hr timelags), whereas L-band exhibited superior sensitivity to the gradual moisture dynamics of coarse fuels (100-hr and 1000-hr timelags). This research highlights the potential of continuous L-band NISAR and C-band Sentinel-1 time series to generate operational, gridded fuel moisture maps, offering a scalable alternative to labor-intensive field sampling across broad landscapes.

Carbon emissions from disturbances in the Arctic-Boreal Zone (ABZ) are not adequately included in global carbon budgets due to insufficient measurements and model representation. Due to this data gap, we conducted an expert assessment to examine the potential trajectories of multiple disturbance types in the ABZ and their impacts on net carbon fluxes over the next one to three centuries. Across five disturbance categories – anthropogenic (e.g., oil and gas infrastructure), biotic, fire, abrupt permafrost thaw, and weather – experts agreed that the extent, frequency, range, and severity of disturbances will increase for at least the next century. Taken together, our assessment suggests that ABZ disturbances could result in 154–417 TgC yr ^− 1 (median expert estimates) greater net carbon emissions by 2100 relative to 2000–2020 for three climate change mitigation scenarios – global mean temperature increase held below (1) 1.5°C by 2100, (2) 2°C by 2100, and (3) Representative Concentration Pathway (RCP) 8.5 (~ 2.6–4.8°C by 2100 with rising temperatures beyond that). Critically, the experts agreed these impacts will persist long after global net zero anthropogenic emissions are reached, causing continued carbon emissions that are currently unaccounted for. Key research priorities for reducing uncertainty in these estimates were 1) improved spatio-temporal monitoring of ABZ disturbances to quantify their extent and distribution, 2) increased measurements of ecosystem carbon fluxes during and following disturbance events, and 3) incorporation of disturbance processes into models.

The January 2025 Los Angeles County wildfires burned more than 50,000 acres, destroyed more than 16,000 structures, and caused 31 deaths. This Commentary examines how NASA's Disasters Program coordinated with response organizations to deliver 22 satellite and airborne products during the fires, even as the Eaton Fire forced the Jet Propulsion Laboratory (JPL) to close and displaced many employees. No single sensor met all operational needs. Airborne Visible Infrared Imaging Spectrometer 3 (AVIRIS‐3) observations supported methane screening and detailed mapping of burn severity and relative char and ash abundance. Sentinel‐1 synthetic aperture radar provided damage information through smoke and clouds, while thermal, optical, and nighttime‐lights sensors contributed active‐fire, power‐outage, surface‐temperature, and burn‐scar observations. The combined products supported situational awareness, damage assessment, air quality monitoring, infrastructure evaluation, and debris‐flow planning. Their operational value, however, depended on whether observations were delivered within the relevant decision window and at a spatial scale suited to agency needs. The response exposed persistent limitations associated with data latency, uneven spatial resolution, manual processing, and the difficulty of quantifying how individual products influenced decisions. Priorities for future events include automated and cloud‐based processing, higher‐resolution commercial and airborne observations, cross‐center continuity planning, and sustained training and exercises with response agencies. The recently launched NASA‐ISRO Synthetic Aperture Radar (NISAR) mission and the future Explorer for Artemis Geology Lunar and Earth (EAGLE) missions can expand these capabilities when paired with the processing systems and partnerships needed to convert observations into actionable products.

Mapping invasive species and native vegetation is crucial for effective land management and conservation, especially in pervasively invaded landscapes. It is often difficult to differentiate between species using traditional remote sensing methods. However, advances in imaging spectroscopy and machine learning techniques offer a solution by leveraging the often‐distinct spectral signatures of different plant species. This study explores the potential of high‐resolution, multi‐temporal hyperspectral data from the Airborne Visible‐Infrared Imaging Spectrometer ‐ Next Generation (AVIRIS‐NG) to map vegetation on two California preserves: The Nature Conservancy's Jack and Laura Dangermond Preserve and the University of California Natural Reserve System (UCNRS) Sedgwick Reserve. We employed machine learning algorithms to address two distinct case studies: mapping invasive iceplant ( Carpobrotus edulis ) at Dangermond Preserve and identifying native perennial grass patches ( Stipa spp.) at Sedgwick Reserve. By analyzing AVIRIS‐NG data from February to May 2022, we found that May imagery provided the highest spectral separability for both species, likely due to phenological differences. Using a support vector machine classifier, we achieved high accuracy in mapping iceplant (87% overall accuracy). We achieved an accuracy of 59.4% for mapping native grasses in annual grass regions. Our results show that integrating multi‐temporal hyperspectral remote sensing with machine learning can provide land managers with the high‐resolution data needed to prioritize resource allocation, monitor management efficacy, and enable informed decision‐making in a changing landscape. This collaborative, technology‐driven approach provides a valuable model for biodiversity conservation.

Fuel moisture content is a key indicator of fire ignition risk and required to drive fire behavior models, however it often relies on in situ measurements which are sparse in time and space. Regional-scale and temporally dynamic measurements are required, and remote sensing provides an opportunity for more comprehensive mapping. In this paper, we demonstrate the use of fuel moisture metrics derived from imaging spectroscopy to inform pre- and post-fire conditions. Imaging spectroscopy can quantify canopy water content (CWC) using a physics-based retrieval, and distinguish landscape characteristics such as photosynthetic and non‐photosynthetic vegetation, and soils. Here, we demonstrate a use case of imaging spectroscopy-derived CWC and fractional cover for a field site on Monroe Mountain in the Fishlake National Forest, Utah. This location was heavily mapped by airborne AVIRIS-3 during a 2023 NASA/FASMEE field campaign. This region also underwent a prescribed burn in October 2023, permitting us to examine how we can use imaging spectroscopy to image fuel conditions pre- and post-fire. The relationship of CWC with in situ dead fuel moisture measurements is stronger for the DFMC surface fuels when filtering for points that contain 50% or more fraction of non-photosynthetic vegetation (R² = 0.50 to 0.54), except for 10-hour fuels (small branches and larger twigs) which had a decrease in agreement. CWC was also found to both decline following the prescribed burn, consistent with the spatial patterns of burn severity, indicating the use of these variables for assessing pre- and post-fire vegetation conditions.

Forecasting wildland fire location and behavior increases in importance as fires grow in frequency and severity worldwide. Observing and understanding pre-fire fuel dynamics through airborne and satellite imagery can support planning and management. The diversity of tools available for management include in-situ, airborne, and satellite remote sensing products, but they are better when used together. Here, we identify Imaging Spectroscopy and Synthetic Aperture Radar products including fuel moisture and load, canopy cover and water content, and photosynthetic or non-photosynthetic vegetation that can be utilized en tandem to understand pre-fire fuels, if they are pre-processed and validated with the combined use in mind. Making these data available to operational users can be leveraged through implementation in novel modeling frameworks, and ongoing assessment and validation.

Wildfires in wildland‐urban interfaces (WUIs) are a growing concern due to their devastating impact on human communities and ecosystems. Low‐latency impact assessment is critical for wildfire response, yet immediate access to fire‐affected communities can be limited. Here, we demonstrate that unmixing char/ash fractional cover using imaging spectroscopy data can support rapid structural damage assessment. Using acquisitions over the 2025 Eaton Fire burn scar collected by the Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS‐3), we demonstrate that a generic spectral endmember library is sufficient for binary structural damage classification between undamaged and destroyed buildings with an accuracy of 86.3%. Incorporating locally collected Eaton Fire endmembers into the library improves the accuracy by 1.3%. This demonstrates the feasibility of assessing structural damage from airborne imaging spectroscopy to rapidly inform post‐fire response and recovery efforts in WUI communities.

While NASA’s Application Readiness Level (ARL) framework effectively tracks the technical maturity of Earth observation systems, it does not capture their broader societal impact or sustained adoption by decision-makers. This creates a critical gap in understanding how Earth science investments translate into real-world benefits across scales from local emergency response to international policy. Drawing on impact assessment frameworks from health research, we propose a nine-level Impact Readiness Level (IRL) scale that evaluates societal adoption, sustained use, and institutional integration of Earth science products. The IRL framework uses quantitative metrics including user engagement, policy integration, and operational deployment to assess progression from initial research (IRL 1) to institutionalized, scaled impact (IRL 9). This framework would enable users to better understand, communicate, and optimize the societal return on Earth science investments, while identifying opportunities for broader adoption and sustained impact.

Permafrost degradation in the Arctic is accelerating and expected to enhance greenhouse gas (GHG) emissions. The Arctic Methane and Permafrost Challenge (AMPAC) was initialized by NASA and ESA as a transatlantic networking action striving to promote scientific research and improve observational capabilities. Earth Observation technology must be harnessed, expanded and synergies exploited to accurately quantify and better understand the state of the permafrost and coincident methane emissions. AMPAC aims at improving the observation capacity over polar regions by evaluating dedicated campaign activities, by analyzing satellite data, and by identifying satellite retrieval improvements. AMPAC provides suggestions for the enhanced exploitation of the increasing Earth observation (EO) capacities of land surface, cryosphere, biosphere and atmosphere missions to enhance the scientific understanding of changes in Arctic permafrost regions and methane emissions and to bridge the gap between top-down (T-D) and bottom-up (B-U) estimates of methane fluxes in the changing Arctic.In particular, monitoring of methane concentrations as well as landcover properties related to wetland and freeze/thaw dynamics is needed. Upcoming synthetic aperture radar missions and constellations of multiple multispectral sensors are expected to advance relevant monitoring capabilities significantly. This will allow better representation of seasonality and advance methane source attribution in general. In addition, continuity of current missions providing greenhouse gas observations, including methane, is crucial. Active optical instruments (lidar) developed for concentration retrieval are expected to substantially enhance detection capabilities across the Arctic.We provide an overview of relevant current and approved atmosphere and land-focused satellite missions of NASA, NOAA, ESA and DLR/CNES with emphasis on advancements and remaining gaps in the context of AMPAC.

Lead (Pb) has been used in human civilization for centuries, but the quantity and source of Pb pollution released into the environment varies spatially and temporally. Ice cores and snowpits are an excellent record of past Pb use.