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Accurately estimating fine ambient particulate matter (PM ) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM . However, there is little research on full-coverage PM estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing⁻Tianjin⁻Hebei (BTH). The LME model was used to calibrate the PM concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM . The results showed a strong agreement with ground measurements, with an overall coefficient ( ²) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m³ in cross-validation (CV). The seasonal ² values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

Fires are a major contributor to atmospheric budgets of greenhouse gases and aerosols, affect soils and vegetation properties, and are a key driver of land use change. Since the 1990s, global burned area (BA) estimates based on satellite observations have provided critical insights into patterns and trends of fire occurrence. However, these global BA products are based on coarse spatial-resolution sensors, which are unsuitable for detecting small fires that burn only a fraction of a satellite pixel. We estimated the relevance of those small fires by comparing a BA product generated from Sentinel-2 MSI (Multispectral Instrument) images (20-m spatial resolution) with a widely used global BA product based on Moderate Resolution Imaging Spectroradiometer (MODIS) images (500 m) focusing on sub-Saharan Africa. For the year 2016, we detected 80% more BA with Sentinel-2 images thanwith the MODIS product. This difference was predominately related to small fires: we observed that 2.02 Mkm² (out of a total of 4.89 Mkm²) was burned by fires smaller than 100 ha, whereas the MODIS product only detected 0.13 million km² BA in that fire-size class. This increase in BA subsequently resulted in increased estimates of fire emissions; we computed 31 to 101% more fire carbon emissions than current estimates based on MODIS products. We conclude that small fires are a critical driver of BA in sub-Saharan Africa and that including those small fires in emission estimates raises the contribution of biomass burning to global burdens of (greenhouse) gases and aerosols.

The carbon balance of tropical ecosystems remains uncertain, with top-down atmospheric studies suggesting an overall sink and bottom-up ecological approaches indicating a modest net source. Here we use 12 years (2003 to 2014) of MODIS pantropical satellite data to quantify net annual changes in the aboveground carbon density of tropical woody live vegetation, providing direct, measurement-based evidence that the world’s tropical forests are a net carbon source of 425.2 ± 92.0 teragrams of carbon per year (Tg C year−1). This net release of carbon consists of losses of 861.7 ± 80.2 Tg C year−1 and gains of 436.5 ± 31.0 Tg C year−1. Gains result from forest growth; losses result from deforestation and from reductions in carbon density within standing forests (degradation or disturbance), with the latter accounting for 68.9% of overall losses.

Droughts and heatwaves in the Mediterranean can induce plant activity decline and severe wildfires leading to considerable economic, social and environmental damages. This study aims at statistically quantifying the isolated and combined impacts of these extreme events based on a combination of regional land surface-atmosphere modeling and satellite observations of surface properties (MODIS). A simulation by the RegIPSL coupled regional model (ORCHIDEE-WRF) over the 1979–2016 period in the Western Mediterranean is used to identify heatwaves and droughts. After an evaluation of the model performance against surface observations of temperature and precipitation, a spatio-temporal analysis is conducted using specific indicators of extreme events: Percentile Limit Anomalies (PLA) and the Standardized Precipitation Evapotranspiration Index (SPEI). The impact on vegetation and wildfires is assessed using the MODIS observations of Leaf Area Index (LAI), burned area (BA) and fire radiative power (FRP), clustered by simulated extreme weather events. Due to water stress, droughts lead to significant biomass decrease (− 10 % LAI on average and reaching − 23 % in some areas). The isolated effect of heatwaves is smaller ( ∼  − 3 % LAI) so that the combined effect is dominated by the impact of droughts. Heatwaves and droughts significantly exacerbate wildfire regimes. Through synergistic effects, simultaneous droughts and heatwaves increase BA and FRP by 2.1 and 2.9 times, respectively, compared to normal conditions. By reducing biomass, droughts slightly decrease fuel availability. However, our results show that the inter-annual variation in fire activity is mainly driven by weather conditions rather than fuel load.

Runoff‐generated debris flows are a potentially destructive and deadly response to wildfire until sufficient vegetation and soil‐hydraulic recovery have reduced susceptibility to the hazard. Elevated debris‐flow susceptibility may persist for several years, but the controls on the timespan of the susceptible period are poorly understood. To evaluate the connection between vegetation recovery and debris‐flow occurrence, we calculated recovery for 25 fires in the western United States using satellite‐derived leaf area index (LAI) and compared recovery estimates to the timing of 536 debris flows from the same fires. We found that the majority (>98%) of flows occurred when LAI was less than 2/3 of typical prefire values. Our results show that total vegetation recovery is not necessary to inhibit runoff‐generated flows in a wide variety of regions in the western United States. Satellite‐derived vegetation data show promise for estimating the timespan of debris‐flow susceptibility. Plain Language Summary Debris flows caused by excessive surface‐water runoff during intense rainfall can be a deadly and destructive hazard in mountainous areas after wildfire. In some cases, debris flows have only occurred in the burned area in the weeks to months after the fire, while, in other cases, debris flows occurred over several years. Though the recovery of vegetation is important for stabilizing sediment and reducing debris‐flow likelihood, uncertainty remains about how much recovery is needed to inhibit debris flows and about how much time is needed to reach this level of recovery. Knowing for how long debris flows are likely to be a hazard is important for managing risks to residents and infrastructure. To investigate this issue, we assembled a data set of 536 debris flows from the western United States and used satellite‐derived vegetation data to calculate the recovery condition of the burned area when each debris flow occurred. We found that the vast majority of the debris flows initiated when the burned area had not yet reached two‐thirds of its prefire vegetation condition. Burned areas that were slower to recover tended to experience debris flows over more protracted timescales. Key Points Majority (>98%) of western United States postfire debris flows occurred when leaf area index was less than 2/3 of typical prefire values Total recovery of vegetation not necessary to inhibit debris flows Remotely sensed postfire vegetation state useful to evaluate elevated debris‐flow susceptibility with time

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability.

Background Two of Ghana’s ecological zones—Guinea-savanna zone (GSZ) and Forest-savanna mosaic zone (FSZ)—are practically homologous in terms of structure and floristic composition, with some differences. The various sub-ecosystems that make up these areas are being depleted and losing their natural values due to various threats. There is little understanding about the fire trends in these areas due to a lack of data and poor accessibility to existing fire statistics. This study aimed to contribute to the understanding of the trends of area burned and active fire in the Guinea-savanna and Forest-savanna mosaic zones in order to inform policy-makers about sustainable management options. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire (MDC14ML) and burned-area (MCD64A1) products to characterize the fire regime in terms of seasonality, intensity, density, burned area, frequency, and trends during the study period of 2001 to 2021. Results This study indicated that fire activity started in October and peaked in December (GSZ) and January (FSZ). The mean proportion burned was approximately 39.95% (burned area of 2659.31 km 2 ; FSZ) and 60.05% (burned area of 3996.63 km 2 : GSZ), while the frequency was approximately 42.87% (1759.95 of active fires; FSZ) and 57.13% (2345.26 of active fires: GSZ). In 2018, GSZ recorded the largest burned area (19 811.2 km 2 , which represents an average of 825.5 km 2 of the total area burned from 2001 to 2021) with 4719 active points detected. FSZ recorded its greatest burned area in 2015 (8727.4 km 2 ; which represents an average of 363.6 km 2 of the total area burned from 2001 to 2021) with 5587 active points recorded. In addition, it was found that specific times of the day (1000 h to 1420 h) recorded the majority of burned areas. In overview, between 2001 and 2021, burned areas increased by an average of 1.4 km 2 (FSZ) and 4.6 km 2 (GSZ), and the number of active fires increased by an average of 4.7 (FSZ) and 4.4 (GSZ) active fires per km 2 . Conclusions In conclusion, burned areas and active fires are increasing in both ecological zones. This study demonstrated the relevance of remote sensing to describe spatial and temporal patterns of fire occurrence in Ghana and highlighted the need for fire control and fuel management by the policies and institutions (e.g., Ghana National Fire and Rescue Service) in these important and vulnerable zones (GSZ and FSZ). This is especially true in the Forest-savanna mosaic zone, which is increasingly affected by the disasters of wildfires and records more active fires than GSZ, indicating that this zone is becoming more and more vulnerable. Therefore, rigorous continuous monitoring is essential, and collaboration between organizations fighting for the conservation of natural resources in the field is strongly recommended.

An increasing number of end-users looking for ground data about fire activity in regions where accurate official datasets are not available adopt a free-of-charge global burned area (BA) and active fire (AF) products for applications at the local scale. One of the pressing requirements from the user community is an improved ability to detect small fires (less than 50 ha), whose impact on terrestrial environments is empirically known but poorly quantified, and is often excluded from global earth system models. The newest generation of BA algorithms combines the capabilities of both the BA and AF detection approaches, resulting in a general improvement of detection compared to their predecessors. Accuracy assessments of these products have been done in several ecosystems; but more complex ones, such as regions that are characterized by frequent small fires and steep terrain has never been assessed. This study contributes to the understanding of the performance of global BA and AF products with a first assessment of four selected datasets: MODIS-based MCD45A1; MCD64A1; MCD14ML; and, ESA’s Fire_CCI in a mountainous region of northwest Yunnan; P.R. China. Due to the medium to coarse resolution of the tested products and the reduced sizes of fires (often smaller than 50 ha) we used a polygon intersection assessment method where the number and locations of fire events extracted from each dataset were compared against a reference dataset that was compiled using Landsat scenes. The results for the two sample years (2006 and 2009) show that the older, non-hybrid products MCD45A1 and, MCD14ML were the best performers with Sørensen index (F1 score) reaching 0.42 and 0.26 in 2006, and 0.24 and 0.24 in 2009, respectively, while producer’s accuracies (PA) were 30% and 43% in 2006, and 16% and 47% in 2009, respectively. All of the four tested products obtained higher probabilities of detection when smaller fires were excluded from the assessment, with PAs for fires bigger than 50 ha being equal to 53% and 61% in 2006, 41% and 66% in 2009 for MCD45A1 and MCD14ML, respectively. Due to the technical limitations of the satellites’ sensors, a relatively low performance of the four products was expected. Surprisingly, the new hybrid algorithms produced worse results than the former two. Fires smaller than 50 ha were poorly detected by the products except for the only AF product. These findings are significant for the future design of improved algorithms aiming for increased detection of small fires in a greater diversity of ecosystems.

Satellite radiometers like MODIS use a bi‐spectral retrieval algorithm to simultaneously retrieve cloud optical thickness and cloud effective radius re $\\left({r}_{\\mathrm{e}}\\right)$. However, retrievals fail for liquid clouds when the re ${r}_{\\mathrm{e}}$ observation exceeds the maximum threshold of 30 μ ${\\upmu }$m in MODIS's solution space, leading to a sampling bias. Here, we quantify this bias by reconstructing pixels with failed retrievals using two methods: a conservative approach assigning a fixed minimum re ${r}_{\\mathrm{e}}$ threshold to failed pixels, and a representative approach modeling failed re ${r}_{\\mathrm{e}}$ using CloudSat radar measurements. We show that MODIS overestimates cloud droplet number concentration by 8%–9% and underestimates liquid water path by 8%–11% globally. We demonstrate that this bias can introduce erroneous correlations between cloud properties that may be misinterpreted as causal processes. Accordingly, we show that accounting for this bias increases the cloud water adjustments by 24%–36%, highlighting the crucial need to expand the solution space in MODIS and similar sensors.