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Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels.

During the 2019-20 bushfire season, 30 million hectares of bush burned, over 3000 homes were destroyed and 33 lives were lost. Over 80 per cent of Australia's population was affected by bushfire smoke and some locations were blanketed with smoke for weeks even though fires were not local.

Purpose This paper reviews the most recent literature related to the use of remote sensing (RS) data in wildland fire management. Recent Findings Studies dealing with pre-fire assessment, active fire detection, and fire effect monitoring are reviewed in this paper. The analysis follows the different fire management categories: fire prevention, detection, and post-fire assessment. Extracting the main trends from each of these temporal sections, recent RS literature shows growing support of the combined use of different sensors, particularly optical and radar data and lidar and optical passive images. Dedicated fire sensors have been developed in the last years, but still, most fire products are derived from sensors that were designed for other purposes. Therefore, the needs of fire managers are not always met, both in terms of spatial and temporal scales, favouring global over local scales because of the spatial resolution of existing sensors. Lidar use on fuel types and post-fire regeneration is more local, and mostly not operational, but future satellite lidar systems may help to obtain operational products. Regional and global scales are also combined in the last years, emphasizing the needs of using upscaling and merging methods to reduce uncertainties of global products. Validation is indicated as a critical phase of any new RS-based product. It should be based on the independent reference information acquired from statistically derived samples. Summary The main challenges of using RS for fire management rely on the need to improve the integration of sensors and methods to meet user requirements, uncertainty characterization of products, and greater efforts on statistical validation approaches.

Live Fuel Moisture Content (LFMC) is a key determinant of vegetation flammability and fire behaviour, yet LFMC products have traditionally relied on coarse-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), limiting their utility for fine-scale fire management. This study introduces the first continental-scale operational LFMC product for Australia derived from Sentinel-2 imagery at 20 m resolution. We developed a Random Forest regression model trained on approximately 680,000 paired Sentinel-2 reflectance and MODIS-LFMC samples (2015–2022) to emulate outputs from the Australian Flammability Monitoring System (AFMS), a MODIS-based pre-operational LFMC product. Model evaluation against AFMS showed strong agreement for grasslands (R2 = 0.83, RMSE = 32.45%) and moderate performance for forests (R2 = 0.43, RMSE = 20.84%) and shrublands (R2 = 0.21, RMSE = 10.28%). Validation using 2279 in situ LFMC measurements from Globe-LFMC 2.0 indicated improved accuracy at homogeneous sites (NDVI CV ≤ 20th percentile: R2 = 0.42, RMSE = 31.39%). Additionally, when validating with a dedicated field campaign specifically designed for Sentinel-2 LFMC assessment, the model achieved its highest accuracy (R2 = 0.53, RMSE = 32.14%), highlighting the importance of tailored ground protocols for satellite product validation. Predicted LFMC also reproduced observed seasonal dynamics at sites with frequent field monitoring. Despite variability across vegetation types, the Sentinel-2 LFMC product effectively captured spatial patterns and seasonal dynamics, providing a step change in monitoring vegetation moisture at landscape scales. This high-resolution dataset offers actionable intelligence for prescribed burning, fuel treatment planning, and fire behaviour modelling in fire-prone environments.

Live fuel moisture content (LFMC) is a key determinant of landscape ignition potential, but quantitative estimates of its effects on wildfire are lacking. We present a causal inference framework to isolate the effect of LFMC from other drivers like fuel type, fuel amount, and meteorology. We show that in California when LFMC is below a critical flammability threshold, the likelihood of fires is 1.8 times as high statewide (2.25% vs. 1.27%) and 2.5 times as high in shrubs, compared to when LFMC is greater than the threshold. This risk ratio is >2 times when LFMC is 10% less than the threshold. Between 2016 and 2021, the risk ratio was highest in 2020 (2.3 times), potentially contributing to the record‐breaking wildfire activity in 2020. Our estimates can inform several wildfire prediction and management applications, including wildfire suppression, prescribed burn planning, and public safety power shutoff implementation. Plain Language Summary Wildfires are complex phenomena and can occur under a range of conditions, making it difficult to determine how different drivers affect wildfire occurrence. For example, dense contiguous vegetation, dry litter, dry vegetation, high winds, and warm air all contribute to high fire likelihood. Under such co‐occurring conditions, how can we determine each driver's effect on fire likelihood? Here, we present a framework to attribute an individual driver's effect on fire occurrence, while controlling for other correlated factors that might provide a false signal. We consider lightning‐based ignitions, assuming they are randomly distributed, and compare the fire outcomes for different vegetation dryness levels, all else held constant. We test our framework in California and find that when vegetation is dryer than the critical flammability threshold, wildfire likelihood increases by almost a factor of two. These results help clarify the uncertainty in the effect of vegetation dryness on fire occurrence, and may also help improve wildfire preparedness, management, and response strategies. Key Points We provide a causal inference framework to understand the contribution of natural drivers to wildfire occurrence When live fuels are extremely flammable, lightning strikes are about twice as likely to cause wildfires The increase in fire likelihood is highest in shrubs and lowest in grass

Forests, once the largest terrestrial carbon sink, are increasingly becoming significant sources of carbon emissions worldwide due to large wildfires and the accumulation of fire fuels in warming environments that deplete soil and vegetation moisture. Despite growing needs such as Nature-Based Solutions, there is a lack of operationalized near-real-time satellite observations of forest fuel conditions to assess whether forests are acting as carbon sinks or emitters. Most existing satellite products focus on chlorophyll content or vegetation cover rather than directly measuring hydrological or thermal variations that influence carbon flux. From Soil Moisture and Ocean Salinity (SMOS) L-band microwave brightness temperature, we retrieved forest (or canopy) temperatures over the 2023 Canadian and 2021 Sakha Republic wildfires that generated some of the world’s largest carbon emissions. We propose forest canopy temperature as a predictor of natural carbon emissions from mega-wildfires, in comparison with Soil Moisture Active Passive vegetation water content and European Centre for Medium-Range Weather Forecasts ERA5 land surface temperature products, which fail to capture the tipping points of thermal development in cold forests. The heat-amplifying feedback between forests and pre-fire sensible heat further accelerates fuel dryness through evapotranspiration driven by snow-melt water and forest warming, leading to the large-scale spread of wildfires. Under such dry conditions, forestation policies may inadvertently increase fuel availability and wildfire risk, potentially leading to an increase in net carbon emissions rather than achieving the intended benefits of carbon sequestration.

Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.

The summer season of 2019–2020 has been named Australia’s Black Summer because of the large forest fires that burnt for months in southeast Australia, affecting millions of Australia’s citizens and hundreds of millions of animals and capturing global media attention. This extensive fire season has been attributed to the global climate crisis, a long drought season and extreme fire weather conditions. Our aim in this study was to examine the factors that have led some of the wildfires to burn over larger areas for a longer duration and to cause more damage to vegetation. To this end, we studied all large forest and non-forest fires (>100 km2) that burnt in Australia between September 2019 and mid-February 2020 (Australia’s Black Summer fires), focusing on the forest fires in southeast Australia. We used a segmentation algorithm to define individual polygons of large fires based on the burn date from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) active fires product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product (MCD64A1). For each of the wildfires, we calculated the following 10 response variables, which served as proxies for the fires’ extent in space and time, spread and intensity: fire area, fire duration (days), the average spread of fire (area/days), fire radiative power (FRP; as detected by NASA’s MODIS Collection 6 active fires product (MCD14ML)), two burn severity products, and changes in vegetation as a result of the fire (as calculated using the vegetation health index (VHI) derived from AVHRR and VIIRS as well as live fuel moisture content (LFMC), photosynthetic vegetation (PV) and combined photosynthetic and non-photosynthetic vegetation (PV+NPV) derived from MODIS). We also computed more than 30 climatic, vegetation and anthropogenic variables based on remotely sensed derived variables, climatic time series and land cover datasets, which served as the explanatory variables. Altogether, 391 large fires were identified for Australia’s Black Summer. These included 205 forest fires with an average area of 584 km2 and 186 non-forest fires with an average area of 445 km2; 63 of the forest fires took place in southeast (SE) Australia (the area between Fraser Island, Queensland, and Kangaroo Island, South Australia), with an average area of 1097 km2. Australia’s Black Summer forest fires burnt for more days compared with non-forest fires. Overall, the stepwise regression models were most successful at explaining the response variables for the forest fires in SE Australia (n = 63; median-adjusted R2 of 64.3%), followed by all forest fires (n = 205; median-adjusted R2 of 55.8%) and all non-forest fires (n = 186; median-adjusted R2 of 48.2%). The two response variables that were best explained by the explanatory variables used as proxies for fires’ extent, spread and intensity across all models for the Black Summer forest and non-forest fires were the change in PV due to fire (median-adjusted R2 of 69.1%) and the change in VHI due to fire (median-adjusted R2 of 66.3%). Amongst the variables we examined, vegetation and fuel-related variables (such as previous frequency of fires and the conditions of the vegetation before the fire) were found to be more prevalent in the multivariate models for explaining the response variables in comparison with climatic and anthropogenic variables. This result suggests that better management of wildland–urban interfaces and natural vegetation using cultural and prescribed burning as well as planning landscapes with less flammable and more fire-tolerant ground cover plants may reduce fire risk to communities living near forests, but this is challenging given the sheer size and diversity of ecosystems in Australia.

Live fuel moisture content (LFMC) is a critical variable influencing wildfire behavior, ignition potential, and suppression difficulty, yet it remains challenging to monitor consistently across landscapes due to sparse field observations, rapid temporal changes, and vegetation heterogeneity. This study presents a comprehensive review of satellite-based approaches for estimating LFMC, with emphasis on methods applicable to New Zealand, where wildfire risk is increasing due to climate change. We assess the suitability of different remote sensing data sources, including multispectral, thermal, and microwave sensors, and evaluate their integration for characterizing both LFMC and fuel types. Particular attention is given to the trade-offs between data resolution, revisit frequency, and spectral sensitivity. As knowledge of fuel type and structure is critical for understanding wildfire behavior and LFMC, the review also outlines key limitations in existing land cover products for fuel classification and highlights opportunities for improving fuel mapping using remotely sensed data. This review lays the groundwork for the development of an operational LFMC prediction system in New Zealand, with broader relevance to fire-prone regions globally. Such a system would support real-time wildfire risk assessment and enhance decision-making in fire management and emergency response.

Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity; R 2  = 0.71). This has led to rapid increases in human exposure to wildfire risk, both because the population living in areas with high plant-water sensitivity grew 50% faster during 1990–2010 than in other wildland–urban interfaces and because VPD has risen most rapidly in these vulnerable areas. As plant-water sensitivity is strongly linked to wildfire vulnerability, accounting for ecophysiological controls should improve wildfire forecasts. If recent trends in VPD and demographic shifts continue, human wildfire risk will probably continue to increase. The authors show that an ecosystem’s sensitivity to drought, measured as the amount of change in vegetation moisture content for a given change in background moisture, predicts the fire hazard in that location.