Search Results (1,828)

Wind is a fundamental driver of wildfire behavior, yet wind–fire relationships remain poorly characterized in the mountainous regions of South Asia, where ground-based observations are scarce. This study examines the wildfire in the Suleiman Range of western Pakistan for May 2022, integrating Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection, Landsat-derived burn severity, ECMWF Reanalysis v5 (ERA5) meteorological data, and Shuttle Radar Topography Mission (SRTM) topography data. Twenty-nine wildfire-classified detections (Fire Radiative Power, FRP range 6.0–52.1 megawatts (MW)) were analyzed across the Sherani, Musakhel, and Dera Ismail Khan (D.I. Khan) districts between 18 and 29 May 2022. The ERA5 wind speed at the fire points was moderately positively correlated with the FRP, although strong collinearity with temperature prevented the separation of the effects of wind and temperature. The wind direction was highly consistent throughout the event. Spread events were defined as consecutive detection pairs; among pairs separated by more than 2 km, four were aligned with the ERA5 downwind direction. These findings are consistent with synoptic winds broadly contributing to eastward fire progression, whereas local-scale spread was likely modulated by the terrain-channeled winds that ERA5 cannot resolve at its ~27 km grid scale. Elevation was strongly negatively correlated with the FRP. The burn severity analysis indicated that approximately 86 km² of burn occurred, predominantly at low-to-moderate severity. This integrated workflow offers a transferable template for characterizing wildfire behavior in data-sparse mountainous regions. Full article

The Northeast China Cold Vortex (NCCV), a typical synoptic-scale system in Northeast China, North China, and the Jianghuai region, frequently triggers thunderstorms, strong winds, and heavy precipitation, making it significant for meteorological monitoring and operational forecasting. However, the cloud microphysical properties of NCCV-associated cloud systems remain poorly characterized, as long-term cloud microphysical observations are limited. This study utilizes cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze cloud-type frequencies and four key cloud microphysical properties under NCCV conditions: liquid effective radius (Re_liq), ice effective radius (Re_ice), liquid water path (LWP), and ice water path (IWP). Nearly identical cloud-type compositions are found for the two groups, NCCV and non-NCCV samples with similar cloud fractions on the regional scale, which are dominated by stratocumulus (Sc), altostratus (As), cumulus (Cu), and stratus (St), with Sc accounting for above 40% of total cloud occurrence. Yet microphysical properties differ markedly between these two groups. LWP shows the most contrast and it is evidently larger in NCCV than in non-NCCV cloud systems. As for the spatial structure of cloud microphysics in the NCCV domain, it is found that Sc, As, St, and nimbostratus (Ns) constitute the primary background, and Sc remains the dominant cloud type in almost all spatial sectors. LWP and IWP tend to have stronger spatial heterogeneity than Re_liq and Re_ice. LWP gets notably larger in the northern to northwestern sectors, whereas IWP shows much higher variations in both radial and azimuthal dimensions. These results reveal the statistical microphysical characteristics of cloud systems associated with NCCV from the perspective of satellite observations, providing a reference for a deeper understanding of their unique cloud and precipitation physical processes. Full article

The radiometric stability of satellite thermal infrared (TIR) channels is an indispensable prerequisite for the accurate retrieval of land surface temperature (LST) and the generation of reliable climate data records. This study evaluates the on-orbit radiometric calibration stability of the Fengyun-3D (FY-3D)/MEdium Resolution Spectral Imager-II (MERSI-II) TIR channels (channels 24 and 25) over four years (2021–2024) via a rigorous cross-calibration framework against Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS). By imposing stringent spectral, spatial, temporal, and angular constraints to ensure the high fidelity of collocated pixel pairs, the cross-calibration results demonstrate that FY-3D/MERSI-II exhibits exceptional radiometric stability. Absolute brightness temperature biases are typically less than 0.1 K, with root mean square errors (RMSEs) limited to 1.20 K over a range of diurnal and seasonal conditions, demonstrating no noticeable systematic degradation. Furthermore, the downstream impact of this calibration on LST retrieval was quantified using the adapted National Oceanic and Atmospheric Administration Joint Polar Satellite System Enterprise algorithm. Validated against independent ground-based longwave radiation measurements collected from the Heihe Watershed Allied Telemetry Experimental Research network (HiWATER) and the Surface Radiation Budget Network (SURFRAD), the retrieved LST yielded overall biases of 0 K and −0.37 K, respectively, with RMSEs below 2.5 K. Cross-calibration demonstrates a limited and context-dependent impact on daytime LST, while the nighttime LST accuracy can be marginally improved using seasonal calibration coefficients derived from combined day/night matchups. Mechanistically, the integration of a soil directional emissivity model into the retrieval algorithm effectively mitigates viewing-zenith-angle (VZA)-induced uncertainties, systematically reducing biases by 0.12–0.20 K and RMSEs by 0.04–0.06 K. These findings confirm that the on-orbit radiometric calibration of FY-3D/MERSI-II meets scientific quality requirements and provide practical guidance for optimizing LST retrieval. Full article

Wildfire regimes in mountain forest landscapes are shaped by complex interactions among topography, climate, vegetation, and human activity. However, predicting and interpreting fire occurrence in topographically heterogeneous regions remains challenging because fire–environment relationships vary strongly across elevation gradients and temporal scales. This study developed a hybrid machine-learning framework integrating an Information Value Model (IVM), Random Forest (RF), and Convolutional Neural Network (CNN) to decode elevation-mediated wildfire regimes in western Sichuan, China, a mountainous forest region characterized by strong vertical environmental gradients and high ecological conservation value. Multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) burned-area records, topographic variables, monthly meteorological data, vegetation indices, land-cover information, and human-accessibility proxies, were integrated at a 500 m spatial resolution. Environmentally comparable non-fire samples were generated from unburned vegetated pixels, and model training, RF-based feature selection, hyperparameter tuning using Particle Swarm Optimization (PSO), and performance evaluation were conducted within a nested spatial block cross-validation framework. The model produced continuous wildfire occurrence probabilities and showed strong discriminatory performance under the adopted validation protocol, with AUC values exceeding 0.95 across temporal datasets and low probability-error metrics. RF importance and correlation analyses identified mean temperature, elevation, and precipitation as the dominant predictors of wildfire probability. Spatial analyses revealed pronounced elevation-mediated differentiation in wildfire regimes: low-elevation valleys showed higher fire probability and stronger associations with human-accessibility proxies, whereas high-elevation plateau areas exhibited lower and more scattered fire patterns associated with climatic constraints. Seasonal and monthly analyses further showed that winter and spring fires dominated the regional fire regime, with risk intensifying during the pre-monsoon dry period. By combining probabilistic fire-risk mapping, spatial-context learning, and elevation-gradient interpretation, this study provides a transferable framework for understanding wildfire regimes in complex mountain forest landscapes. The findings support adaptive forest fire management, targeted monitoring, and risk zoning in mountainous regions where forest ecosystems, human activities, and conservation values intersect. Full article

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making. Full article

A methodology for deriving high-resolution (5-km) surface shortwave radiative (SWR) fluxes over the Arctic was applied to observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the spring and summer melt season (March–September) of 2007, when the Arctic experienced a historically significant and well-documented decline in sea ice extent. The derived SWR fluxes were used to estimate solar heat input into the Arctic Ocean during the melt season, a task that had not previously been undertaken at such high spatial resolution. According to the National Snow and Ice Data Center (NSIDC), Arctic sea ice extent reached a record minimum of 4.13 million km² on 16 September 2007, approximately 38% below the 1979–2000 climatological mean and 24% below the previous record minimum in 2005. This extreme reduction in sea ice resulted in several weeks of ice-free opening along portions of the ‘Northwest Passage’. Availability of high spatial resolution SWR fluxes in the Arctic is particularly important for improving estimates of solar heat input into the Arctic Ocean, especially within the highly heterogeneous marginal ice zone. To facilitate comparison with sea ice concentration products from NSIDC, the MODIS-derived 5-km SWR fluxes were aggregated to 0.25° equal-area grid cells (approximately 25 km resolution). Our results show that the abrupt increase in the open water fraction produced anomalies in solar heating to the upper ocean exceeding 300%, hereby enhancing the ice–albedo feedback mechanism and promoting further sea ice melt. The estimated monthly cumulative solar heat input to the ocean for a nominal 1° grid cell was 164.9 MJ m⁻² in May. In contrast, the corresponding four 0.25° sub-grid cells, resolved using the high-resolution MODIS data, exhibited cumulative heat inputs of 58.0, 93.0, 189.3, and 296.4 MJ m⁻², respectively. Although the average heat input for the 1° grid cell (165 MJ m⁻² was similar to the average value obtained from the four 0.25° grid cells (159 MJ m⁻² the substantial sub-grid variability is important because the oceanic and sea-ice responses to solar heating are highly nonlinear. Consequently, unresolved spatial variability can significantly affect the magnitude of derived quantities and associated feedback processes. These findings demonstrate the importance of high-spatial-resolution radiative flux information for accurately quantifying ocean heating and ice–ocean interactions in the Arctic. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Land surface temperature (LST) is a core physical parameter that characterizes land surface processes and surface-atmosphere energy exchange. As the demand for high-accuracy LST products intensifies across diverse research domains—including climate science, hydrology, and ecosystem modeling—the systematic quantification of pixel-level retrieval uncertainties has become essential for generating long-term, consistent Climate Data Records (CDRs). However, existing studies predominantly emphasize algorithmic development or localized validation, with limited attention to systematic cross-site and long-term uncertainty assessments. This gap impedes a comprehensive understanding of the compositional structure and spatiotemporal variability of LST retrieval uncertainties under heterogeneous surface and atmospheric conditions. In this study, based on the improved generalized split-window (GSW) algorithm and error propagation theory, the total uncertainty (U_total) and its four primary components—algorithm uncertainty (U_a), land surface emissivity uncertainty (U_e), noise equivalent delta temperature uncertainty (U_n), and atmospheric water vapor uncertainty (U_w)—at the pixel level over long time series and across multiple sites are quantified. Our analysis spans a 21-year period (2003–2023) and encompasses multiple geographically distributed sites, utilizing high-quality Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data—specifically MYD11_L2 and MOD11_L2 products—collocated at the locations of 15 globally distributed ground-based reference sites. These sites are used to represent diverse climatic regimes and land-cover conditions, rather than to provide point-scale “true” LST values for residual-based validation. Results show that the interquartile range (IQR) of U_total is consistently concentrated between 1.0 and 1.2 K, demonstrating long-term stability. Systematic differences in U_total are identified across sensor platforms and diurnal cycles: U_total for Aqua/MYD data (1.13–1.25 K) is marginally higher than that for Terra/MOD data (1.05–1.17 K); similarly, daytime U_total (1.08–1.23 K) is generally slightly elevated relative to nighttime U_total (1.05–1.18 K). The contributions of individual uncertainty components to U_total exhibit substantial variation, with mean relative contributions of 81.97%, 11.32%, 4.46%, and 2.25% for U_e, U_a, U_n, and U_w, respectively. The dominant drivers of U_total differ markedly across climatic regions: in arid regions, U_total is predominantly governed by U_e, termed “emissivity-dominated,” accounting for over 85% of the total; conversely, humid tropical regions exhibit a “surface-atmosphere co-influenced” regime, characterized by a reduced contribution from U_e and correspondingly enhanced contributions from U_a and U_w. Furthermore, U_total decreases with increasing total column water vapor (TCWV) (Pearson correlation coefficient r = −0.498; linear slope k = −0.0425 K/(g/cm²)), and increases with increasing viewing zenith angle (VZA) (r = 0.208; k = 0.0022 K/degree). While U_a, U_n, and U_w all increase with TCWV, U_e decreases. Full article

Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire products are widely used for global fire monitoring, but single-sensor records are limited by differences in observation geometry, spatial resolution, detection sensitivity, and swath coverage. To combine the long-term continuity of Aqua MODIS with the higher sensitivity of Suomi NPP VIIRS, this study developed a correction-before-fusion framework for MYD14 and VNP14IMG and generated a daily fused fire radiative power (FRP) dataset at the native MODIS footprint scale. MYD14 and VNP14IMG observations from 2012 to 2024 were processed using duplicate-detection correction, footprint-scale near-synchronous matching, area-based VIIRS cloud correction, and anomalous-sample screening. Cloud-corrected VIIRS FRP was then used as the reference to develop an empirical viewing zenith angle (VZA)-dependent correction model for MODIS FRP. Finally, VZA-corrected MODIS FRP and cloud-corrected VIIRS FRP were integrated using a quality-prioritized fusion strategy. The correction model achieved high fitting accuracy ($R^2\ge 98.18\%$) and reduced MODIS underestimation under large-VZA conditions. Compared with the original MODIS product, the fused product increased detected fire pixels by approximately 3.82-fold, improved spatial continuity, and reduced temporal data gaps. Landsat-based validation showed improved low-intensity fire detection while maintaining low commission error. This framework provides a harmonized long-term FRP dataset for fire monitoring, emission estimation, and fire-climate studies. Full article

Net primary productivity (NPP) is an important indicator of ecosystem carbon accumulation capacity and vegetation productivity potential, and its accurate estimation is of great significance for agricultural management and regional carbon cycle research. To address the problem that the temporal continuity of single-source optical remote sensing data is easily affected by cloud cover, this study used Sentinel-2 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as data sources and constructed an NDVI time series with high spatial and temporal resolution for the study area based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) method. On this basis, the Simple Ratio (SR) index was incorporated to supplement canopy information, and the key parameters of the Carnegie–Ames–Stanford Approach (CASA) model were differentially optimized for different crop types, thereby enabling remote sensing-based estimation of crop NPP. The results showed that the fused NDVI effectively compensated for observation gaps caused by cloud interference, and its temporal variation was generally consistent with the crop growth process. In addition, the Fraction of Photosynthetically Active Radiation (FPAR) improved with the fused NDVI, which effectively characterized phenological differences among crops. Compared with the unoptimized model, the improved model significantly improved NPP estimation accuracy for both maize and rice. Specifically, for maize, the coefficient of determination ($R^2$) increased from 0.75 to 0.88, and the mean absolute percentage error (MAPE) decreased from 67.00% to 34.68%. For rice, the MAPE decreased from 78.51% to 23.43%, while the mean absolute error (MAE) decreased from 345.1 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$ to 95.6 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$. These results indicate that constructing a highly continuous vegetation index time series through spatiotemporal fusion, together with optimizing the CASA model by incorporating the SR index and crop-specific parameterization, can effectively improve the stability and accuracy of NPP estimation for agricultural crops. Full article

Vegetation spring phenology in drylands is sensitive to climatic and anthropogenic pressures, yet the nonlinear responses of the start of the growing season (SOS) across different vegetation types remain inadequately quantified. Here, we extracted the start of the growing season from 2001 to 2020 Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series for stable vegetation areas on the Mongolian Plateau (MP) and applied Extreme Gradient Boosting (XGBoost) models with Shapley Additive Explanations (SHAP) analysis to six environmental drivers—precipitation, temperature, windspeed, livestock density, population density, and elevation—across forests, shrublands, and grasslands. The SOS displayed pronounced spatial heterogeneity, with earlier onset in northern forests and shrublands and delayed onset in southern arid grasslands. Forests and shrublands exhibited significant advancing trends of 6.8 and 6.4 days per decade, respectively, while grasslands showed no significant trend. Temperature dominated the SOS variability across all vegetation types, yet the relative importance of other drivers varied; windspeed notably influenced forests, whereas precipitation and elevation were critical for grasslands and shrublands. SHAP analysis revealed strong nonlinearities and threshold effects, including a U-shaped temperature response and a 350 mm precipitation threshold in grasslands, beyond which the SOS responses markedly shifted. These results highlight the vegetation-specific and nonlinear nature of phenological regulation in drylands, suggesting that phenology prediction and ecosystem monitoring should explicitly incorporate vegetation type and threshold-based climatic responses. Full article

Glacial lake dynamics in high-mountain regions serve as a sensitive proxy for cryospheric responses to climate warming. This study utilizes multi-temporal Sentinel-2 imagery and digital elevation model (DEM) data to quantify glacial lake evolution in the Donglin Tsangpo Watershed, a strategically important section of the China–Nepal Economic Corridor, from 2016 to 2024. The results show a significant expansion in both the number (from 43 to 56) and total area (from 3.97 km² to 4.94 km², +24.43%) of glacial lakes, primarily driven by the rapid emergence of very small lakes (0.02–0.05 km²) and a clear upward shift in elevation distribution, with new lakes forming above 5300 m and extending to elevations exceeding 5500 m. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) reveals that this expansion coincided with pronounced positive thermal anomalies, particularly the 2020 extreme warm event (daytime +3.88 °C, nighttime +1.61 °C). Mechanistic analysis using the ERA5-Land reanalysis dataset further demonstrates that persistent positive downward longwave radiation (LW) anomalies (peaking at +10.71 W/m² in 2021) effectively compensated for reduced shortwave input, inhibiting nocturnal refreezing and extending the effective ablation period. Furthermore, a rising liquid-to-solid precipitation ratio and extreme melt-day anomalies (up to +39.36 days) provided intensified hydrothermal inputs, driving the pronounced expansion of glacier-contact lakes despite non-linear interannual responses. This study also estimates individual lake volumes, identifying a transition toward rapid lake development that elevates potential downstream hazard exposure. These findings provide a high-resolution dataset and a robust physical framework for transboundary environmental monitoring and risk assessment in this climate-sensitive region. Full article

Accurate crop classification in complex and heterogeneous agricultural landscapes is often challenged by mixed-pixel effects and spatial autocorrelation. This study proposes a prior-guided crop classification framework that integrates accessible Moderate Resolution Imaging Spectroradiometer (MODIS) optical and Sentinel-1 synthetic aperture radar (SAR) time-series data with explicit phenological and structural priors. By embedding physically meaningful constraints into temporal feature learning, the model shifts from purely data-driven learning toward biophysically interpretable discrimination between crop types and background classes. Performance was rigorously evaluated using spatial cross-validation (SCV) to ensure geographic independence. Results demonstrate that the prior-guided CNN achieves an overall accuracy (OA) of 98.66% and a Kappa of 0.9832, outperforming unguided deep learning and conventional machine learning models. Notably, the framework exhibits high spatial robustness, with a minimal performance gap between random and spatial validation (ΔOA = 0.0049). In addition to improving classification accuracy, integrating phenological features with SAR-based prior information enhances the stability of non-crop categories in fragmented scenarios, while leveraging readily available medium-resolution data to support large-scale applications. These findings demonstrate that embedding physically meaningful prior knowledge into multi-source time-series learning improves classification accuracy while enhancing spatial generalizability and interpretability. More broadly, the proposed framework offers a transferable paradigm for integrating domain knowledge with deep learning, providing a practical and scalable solution for crop mapping in heterogeneous agricultural landscapes using widely accessible medium-resolution data. Full article

Aerosol optical depth (AOD) is a key indicator for quantifying aerosol radiative effects and evaluating air quality. However, atmospheric chemical transport models often exhibit systematic AOD biases, and model capability for column-integrated optical properties is not always consistent with that for near-surface particulate matter concentrations. Here, we evaluate AOD simulated by the Community Multiscale Air Quality (CMAQ) model over five major urban clusters in China, including the Beijing-Tianjin-Hebei (BTH) region, Fenwei Plain (FWP), Sichuan Basin (SCB), Yangtze River Delta (YRD), and Pearl River Delta (PRD), using satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based retrievals from the Aerosol Robotic Network (AERONET), and vertical extinction profiles from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). CMAQ reproduces the major spatial patterns and exhibits relatively small biases in near-surface PM_2.5. However, it persistently underestimates AOD relative to MODIS, with the largest negative bias occurring in April (i.e., a typical spring month). This contrast indicates a pronounced inconsistency between column-integrated aerosol amount and surface mass density. Relative to AERONET, CMAQ shows a negative bias (NMB = −38%), whereas MODIS shows a positive bias (NMB = 56%), suggesting that both model and retrieval uncertainties contribute to the CMAQ–MODIS disagreements. CALIPSO-constrained vertical analysis further suggests that insufficient extinction above the planetary boundary layer (PBL) is an important contributor to the negative AOD bias, although the relative roles of boundary-layer and upper-layer contributions vary across regions, underscoring the importance of accurately representing aerosol vertical transport and optical processes. These results indicate that evaluations based solely on surface observations may fail to fully capture the overall structure of AOD errors, particularly given the clear differences between near-surface mass concentrations and column optical properties, which vary across regions. This also highlights the importance of improving the representation of aerosol vertical transport and optical processes in chemical transport models. Full article

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. Full article