PM2.5-O3 compound pollution is less understood in high-latitude, cold-climate regions. Using multi-site observations (2015-2020) combined with WRF-CMAQ diagnostics, we examined temporal features, formation processes, and source contributions in Northeast China. The seasonal maximum shifted to late spring/early summer: April-May were the modal months during 2016-2020, whereas 2015 showed a broader March-October season. While the frequency of compound days declined, event-time PM2.5 increased by 3.7 mu g m-3 yr-1 and MDA8 O3 marginally decreased, indicating a spring, PM-leaning regime. For a representative episode (13-15 April 2020), process analysis showed daytime O3 dominated by vertical mixing and regional inflow, whereas PM2.5 was governed by local emissions and aerosol processes. Scenario experiments quantified regional transport contributions to O3 of 51-80% and local-emission contributions to PM2.5 of 49-85%. Trajectories and concentration-weighted analyses identified the North China Plain and the Russian Far East as major upwind source regions, with a Bohai-Shandong recirculation zone along the prevailing SW -> NE pathway. These contrasts imply that O3 mitigation requires cross-regional coordination, while PM2.5 reduction should prioritize local controls, especially during late-spring episodes in cold-climate settings.

Biomass burning is a major contributor to atmospheric pollution and short-term climate forcing, yet its spatiotemporal variability and associated meteorological constraints in China are not fully characterized. Using Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data from 2012 to 2024, combined with MODIS land-cover information and ERA5 meteorological reanalysis, this study investigated temporal trends, spatial heterogeneity, and meteorological controls of biomass burning across four vegetation types-cropland, forest, grassland, and savanna. Results revealed distinct temporal trends among vegetation classes. Cropland fires declined sharply after 2018, while grassland fires showed a steady long-term decrease. In contrast, forest and savanna fires exhibited greater interannual variability and partial recovery after 2020. Spatially, the North China Plain and Northeast China dominate agricultural residue burning, whereas forest and savanna fires were concentrated in south regions such as Yunnan and Guangxi. Kernel density analyses revealed that biomass burning occurrences are strongly constrained within narrow meteorological windows, e.g., under light precipitation (<3-4 mm/day), low-to-moderate winds (<4 m/s), and vegetation-specific ranges of temperature-humidity and moisture deficit. This study clarified the spatiotemporal variability of biomass burning across China and identified the distinct atmospheric conditions under which different fire types most frequently occur, offering a useful basis for improving regionally tailored fire-risk monitoring and control measures.

Industrial point sources, particularly steelworks, are significant contributors to regional air pollution, yet their dynamic impact on urban air quality remains insufficiently characterized. This study investigates the contributions of real-time NO2 emissions from steel industry point sources in an energy-intensive region of northern China using the CMAQ chemical transport model, coupled with high-resolution meteorological inputs and CEMS-based emissions. Multiple simulations were conducted to isolate the influence of individual sources, and the results were validated using ground-based NO2 observations and satellite data from TROPOMI. The model effectively captured the spatiotemporal patterns of NO2 but showed consistent underestimation in densely populated areas. Source apportionment revealed that four major point sources (P01, P02, P06 and P12) accounted for the highest contributions to surface NO2 concentrations, demonstrating the disproportionate impact of a few large emitters. Temporal analysis further highlighted shifts in sectoral contributions driven by meteorology and emission activity patterns. The findings underscore the need for high-resolution, time-resolved modeling frameworks and improved stack parameterizations to enhance air quality management and inform targeted mitigation strategies in industrial regions.

Ammonia (NH3) emissions from fertilized cropland are influenced by conservation tillage practices, yet the underlying mechanisms remain insufficiently understood in the black soil region of northeastern China. In this study, field observations were conducted in a maize cropland to compare NH3 volatilization under conventional tillage, no-tillage, and straw incorporating treatments following application of urea and slow-release fertilizer. Results showed that compared with conventional tillage, no-tillage-straw mulching (T1) and ridge tillage-straw mulching (T3) treatments significantly reduced soil temperature while increasing soil moisture and decreasing the estimated soil resistance to NH3 transport. These changes were accompanied by higher NH3 emission factors (EFs) in the T1 and T3 treatments, although differences in EFs among tillage treatments were not statistically significant. Compared with urea, slow-release fertilizer delayed the occurrence of peak NH3 volatilization and reduced cumulative NH3 emissions by approximately 54%. Notably, measurements under slow-release fertilizer application revealed that elevated NH3 volatilization persisted for more than 40 days after fertilization, indicating that conventional monitoring periods may underestimate cumulative NH3 losses in conservation tillage systems using slow-release fertilizers. Overall, conservation tillage substantially altered soil environmental conditions associated with NH3 volatilization, while fertilizer types strongly influenced the temporal dynamics and magnitude of NH3 emissions. These findings provide useful insights for improving NH3 emission monitoring, process understanding, and inventory estimation in conservation tillage systems.

Mineral dust is a major atmospheric aerosol influencing Earth's energy balance through aerosol-radiation (ARI) and aerosol-cloud interactions (ACI). While homogeneous dust effects have been studied, the impact of mineralogical composition on regional meteorology and air quality remains underexplored, limiting accurate forecasting of dust storm impacts, especially in dust belt regions. In this study, we used a two-way coupled WRF-CHIMERE model with three mineralogical dust atlases (Nickovic et al., 2012 (N2012); Journet et al., 2014 (J2014); and a new dataset, Li et al., 2024 (L2024), from the Earth Surface Mineral Dust Source Investigation (EMIT)) to evaluate ARI effects during the March 2021 dust storm in North China. Results showed significant spatial variations in radiative forcing due to mineralogical differences. Bulk dust (without considering mineralogy) caused an average shortwave radiative forcing of -5.72 Wm-2, while mineral-specific forcings increased this by up to +0.10 Wm-2. Integrating EMIT data reduced PM10 biases by over 15 % in high-concentration regions and improved ozone predictions, with localized changes of -2.46 to +3.52 & micro;gm-3. Hematite's strong absorption and quartz's reflective properties were key in altering radiative and air quality outcomes. Compared to scenarios of bulk dust, the consideration of ARI effects of mineralogical compositions can increase PM10 concentration by up to 1189.48 & micro;gm-3 in dust source regions. Future research perspectives on the utilization of high-resolution EMIT data in two-way coupled meteorology and air quality models for investigating the ACI effects of mineralogical dust on cloud microphysics are proposed.

Abstract. Snow with high albedo enhances atmospheric photochemical reactions, influencing key oxidative processes. Nitryl chloride (ClNO2), as a strong oxidizing species, is generated by the heterogeneous reaction between dinitrogen pentoxide (N2O5) and chloride adsorbed on aerosol and the ground surfaces. After sunrise, the photolysis of ClNO2 rapidly releases highly reactive chlorine radicals (Cl·), which contributes to the formation of secondary pollutants. However, the pollution mechanisms in high-latitude, snow-covered regions associated with increased chlorine emissions remain unclear. In this study, we employed the WRF-CAMx model (Weather Research and Forecasting Model-Comprehensive Air Quality Model with extensions) with a modified chemical mechanism (CB6r2h_lts, Carbon Bond 6 revision 2 with heterogeneous chemistry for low-temperature and snow-covered conditions) that incorporated heterogeneous N2O5 reactions and ClNO2 photolysis on ground surfaces to assess their impact on regional atmosphere under snow-covered conditions in Northeast China. Our findings reveal that under snow-covered conditions, the YU20 aerosol scheme (from study by YU et al., 2020) outperforms the BT09 scheme (from study by Bertram et al., 2009) in simulating N2O5 and ClNO2 concentrations within the CAMx model. Incorporating anthropogenic chlorine emissions and ground surface chemistry significantly improved model performance for ClNO2, reducing the mean bias (MB) from -105.78 pptv to 2.66 pptv and increasing the index of agreement (IOA) from 0.39 to 0.86. These processes resulted in a maximum hourly increase of 3.65 µg/m³ in PM2.5 (relative contribution: 15.34 %) and 3.41 ppbv in MDA8 O3 (5.68 %). Notably, ground surface chemical processes were identified as the dominant source of nocturnal ClNO2, contributing approximately 28.36 % to nighttime accumulation across Northeast China. These findings not only highlight the pivotal role of chlorine chemistry in atmospheric processes under snow-covered conditions, but also provide crucial support for the refinement of the mechanisms governing the flux exchange of chemical substances between the atmosphere and the cryosphere.

The environmental impacts of reactive nitrogen (Nr) emitted from fertilized cropland present significant challenges for balancing food security, air pollution and climate change mitigation. As a leading agricultural producer, China requires high-resolution Nr emissions modeling within a comprehensive processed-based framework to address these issues effectively. In this study, we applied a process-based agroecological model (FEST-C*) to estimate daily Nr emissions at 0.25° in China during 2020 and analyzed the driving factors by using Structural Equation Modeling, Random Forest, and Dominance Analysis. The hotspots of annual Nr emissions were in North China, Southeast China, and Southwest China, collectively responsible for over 80 % of the total emissions. Approximately 81 % of the total Nr emissions were from wheat, maize, and rice fields. Timing and amount of basal and topdressing fertilization under different crop rotation systems determined the monthly and seasonal variations of Nr emissions. The impacts of various factors on Nr emissions varied with NH3 being mainly driven by fertilizer consumption and other Nr species (N2O, NO, and HONO) also affected by soil temperature and water content. The spatial distributions of monthly Nr emissions calculated by FEST-C* were more realistic than currently available emission inventories compared to satellite or field observations. These findings will enable policymakers to develop effective control measures that alleviate cropland Nr emissions while sustaining crop production in China.

Excited-state intramolecular proton transfer (ESIPT) can be accompanied by electron transfer within a molecular skeleton, leading to a proton-coupled electron transfer (PCET) process. The kinetics of ESIPT are influenced by the concurrent electron transfer, which in turn affects the overall photophysical properties of the system. In this study, we elucidate the photoinduced PCET mechanism in an ESIPT system based on 7-(indol-2-yl)-triazolopyrimidine (In-TAP) derivatives, which feature an N-H···N-type intramolecular hydrogen bond between adjacent heteroaromatic rings. Chemical modifications introduce short-range and long-range charge-transfer characteristics, facilitating an ultrafast ESIPT reaction on a time scale of ∼150 fs, prior to the system reaching its equilibrium polarization of the solvent field. Strongly solvatochromic T* emission is attributed to the solvation-associated ESIET process following the ultrafast ESIPT reaction. This work proposes a novel and relatively rare N-H···N-type molecular framework that approximately follows the case-B PCET mechanism (PT occurs prior to ET), extending insights from the well-established systems such as 2-((2-(2-hydroxyphenyl)benzo-[d]oxazol-6-yl)methylene)-malononitrile (diCN-HBO), 2-((2-(2-hydroxyphenyl)benzo[d]oxazol-6-yl)-methylene)-cyanoacetic acid (HBODC), and related analogs.

Snow cover has the characteristic of high reflectivity, and the snow particles inside it are prone to form a quasi-liquid layer (QLL) on their surface at temperatures below 0 °C, both of which promote the photolysis and hydrolysis reactions of nitrous acid (HONO). In this study, the CAMx model was updated by incorporating 11 heterogeneous chemical reactions of HONO, including HONO depletion reactions, and was applied to conduct numerical simulations for March 2024 in Northeast China. The results showed that the average HONO flux during the snowmelt period (9.06 × 1014 molecules m-2 s-1) exceeded that of the snow accumulation period (7.74 × 1014 molecules m-2 s-1), while the flux during the snow-free period was significantly lower (3.12 × 1014 molecules m-2 s-1), indicating that HONO flux peaks during the snowmelt period. Moreover, this study summarized that HONO flux from mid-latitude snow cover was found to be two orders of magnitude higher than that in polar regions, which indicates that snow cover in mid-latitude regions might be an important potential source of atmospheric HONO. Enhancement factor functions were established based on ammonia (NH3) concentrations, relative humidity (RH), and unknown HONO sources, which reveals substantial contributions to unknown HONO during the snow-covered (8.25 %) and snowmelt (19.78 %) periods. Compared to HO2 and RO2, the enhancement factor contributes the most to OH, with a maximum contribution of 24.76 %, while its maximum contributions to HO2 and RO2 are 12.14 % and 9.73 %, respectively. These findings elucidate HONO formation mechanisms over seasonal snow and quantify cryosphere-atmosphere flux exchange.

Reactive chlorine species can significantly influence the formation of secondary air pollutants. Due to limited observational data, their contribution to haze formation in cold environments remains poorly constrained. In this study, we conducted field measurements of reactive chlorine species in snowy Northeast China, a region frequently affected by wintertime haze events. The average nitryl chloride (ClNO2) and molecular chlorine (Cl2) were 110 ± 193 and 13 ± 13 ppt during the day, and 186 ± 216 and 12 ± 17 ppt at night, respectively. In addition, we performed vertical profile experiments to determine the air-snow surface exchange fluxes of ClNO2 and Cl2. They generally exhibited net deposition to the snowpack at night but frequently showed net emissions from the snowpack during the day, suggesting photochemical production on the snowpack. On average, photolysis of Cl2 produced three times more chlorine radicals than ClNO2 on a daily basis. The combined chlorine radical production rate from Cl2 and ClNO2 reached approximately one-third of the hydroxyl radical production rate from nitrous acid, highlighting their substantial role in wintertime atmospheric oxidation. Our results contribute to a better understanding of atmospheric oxidation in the snowy, polluted regions worldwide.

Biogenic volatile organic compounds (BVOCs) are crucial players in atmospheric chemistry, significantly impacting the formation of tropospheric ozone (O3). While China has made substantial strides in reducing anthropogenic VOC (AVOCs) emissions, O3 levels persist, highlighting the complex interplay between biogenic and anthropogenic sources. A critical knowledge gap exists in understanding how BVOC emissions influence ozone formation regimes (OFRs) and how this knowledge can inform effective air quality policies. This study employs the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 3.2 and the Community Multiscale Air Quality Modeling System (CMAQ) version 5.3.3 models, combined with process analysis (PA) and the Integrated Source Apportionment Method (ISAM), to evaluate the impact of BVOC emissions on OFRs in China. The models simulate BVOC emissions and their effects on OFRs across various regions during July 2019. The findings highlight that BVOCs play a pivotal role in shifting OFRs, with significant implications for ozone mitigation strategies in China. The study suggests that effective ozone control measures must consider the dual impact of BVOCs and AVOCs, with tailored strategies for different regions and times of day. The study also proposes potential challenges in mitigating BVOC emissions and outlines future research directions for interdisciplinary collaboration to address the complexities of ozone pollution management. This research advances the understanding of BVOCs' roles in ozone formation dynamics and provides a foundation for developing more effective air quality management policies in China, especially as global greening and climate change continue to influence BVOC emissions.

The emissions of reactive nitrogen (Nr) from cropland links the pedosphere and atmosphere, playing a crucial role in the Earth’s nitrogen cycle while significantly impacting regional climate change, air quality, and human health. Among various Nr species, nitrogen oxide (NO) and nitrous acid (HONO) have garnered increasing attention as critical precursors to surface ozone (O3) formation due to their participation in photochemical reactions. While most studies focus on Nr emissions from soils, the specific contributions of cropland Nr emissions considering planting activities to regional O3 pollution remain insufficiently investigated. This study applied the enhanced process-based agroecological model (FEST-C*) coupled with the air quality (CMAQ) model to quantify cropland Nr emissions and assess their contributions to regional O3 formation across China in June 2020. The simulated results indicated that the fertilizer-induced total Nr emission was estimated at 1.26 Tg in China, with NO emissions accounting for 0.66 Tg and HONO emissions for 0.60 Tg. North China was identified as a hotspot for cropland Nr emissions, contributing 43% of the national total. The peak emissions of cropland NO and HONO occurred in June, with emissions of 169 and 192 Gg, respectively. Cropland Nr emissions contributed approximately 8% to the national monthly mean MDA8 O3 concentration, with localized enhancements exceeding 9% in agricultural hotspots in summer. North China experienced the largest MDA8 O3 increase, reaching 11.71 μg m−3, primarily due to intensive fertilizer application and favorable climatic conditions. Conversely, reductions in nighttime hourly O3 concentrations were observed in southern North China and northern Southeast China due to the rapid titration of O3 via NO. In this study, the contributions of cropland Nr emissions to MDA8 O3 concentrations across different regions of China have been further constrained. Incorporating cropland Nr emissions into the CMAQ model improved simulation accuracy and reduced mean biases in MDA8 O3 predictions. This study offers a detailed quantification of the contribution of cropland Nr emissions in regional ozone formation across China and highlights the critical need to address cropland NO and HONO emissions in air quality management strategies.

Biogenic volatile organic compounds (BVOCs) are key precursors to ozone (O3) and secondary organic aerosol (SOA) formation, influencing both air quality and climate changes. BVOC emissions are highly responsive to environmental stressors such as drought, temperature, and ozone. While significant progress has been made in modeling BVOC emissions, existing studies in China lack a detailed exploration of how different abiotic stressors-particularly in combination-affect emissions and their subsequent impacts on O3 and SOA formation. In this study, we employed the MEGAN 3.2 model to quantify the effects of different stressors (drought, temperature, ozone, COQ, wind, and LAI) on BVOC emissions across China during 2019. Seven scenario simulations were conducted, each isolating individual stressors as well as a combined scenario. Our results show that drought and ozone significantly alter emissions, reducing isoprene and monoterpene output while increasing SOA formation under certain conditions. The largest impacts were observed in Central and Eastern China, where combined stressors led to reductions in BVOC emissions by up to 25 % during summer months. This study provides new insights into how different abiotic stressors interact to influence BVOC emissions and air quality in China. The findings highlight the need for integrated stressor assessments in emission models to better predict O3 and SOA concentrations under future climate scenarios. These results contribute to advancing air quality management strategies, particularly in regions facing increasing environmental stress due to climate change.

This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows: 1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality: YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"

Fire is a key ecological factor in marshes, significantly influencing the nitrogen (N) cycle. The impacts of different fire regimes on marshes have garnered increasing attention. This study aims to reveal the effects of fire regimes on N distribution in marshes. We conducted field experiments with fixed–point prescribed burning in typical Sanjiang Plain freshwater marshes, exploring the influences of various fire regimes on the distribution of N in marshes. We found that in the spring–burned plots, the soil ammonium (NH4+–N) content increased by 318% with thrice–burned approaches compared to once–burned, and by 186% with thrice–burned compared to twice–burned. In the autumn–burned plots, NH4+–N content increased by 168% and 190%, respectively. Similarly, the soil nitrate (NO3––N) content three years subsequent to burning increased by 29.1% compared to one year since burning, and by 5.96% compared to two years since burning in the spring–burned plots (73.8% and 32.9% increases, respectively, in the autumn–burned plots). The plant stem–N content of the autumn burns increased by 30.9%, 119%, and 89.1% compared to the spring burns after one, two, and three years since burning, respectively. Our results indicate that high fire–frequency promotes marsh N cycling within the span of three years. The marsh soil conversion of NH4+–N to NO3––N was enhanced with increased time since burning. High fire–frequency promotes plant growth, exacerbating competition between plant populations, with this effect being more significant in autumn–burned plots than in spring–burned plots.

In recent years, China has faced severe air pollution of fine particulate matter (with an aerodynamic diameter of smaller than 2.5 µm, PM2.5) and ozone (O3). It is importance to investigate the driving factors to air pollution. Numerous studies have emerged on the impact of meteorology on PM2.5 and O3, but most of them have concentrated on the highly developed and heavily populated areas of eastern China. There is insufficient research on PM2.5 and O3 pollution in the three northeastern provinces of China. To further understand the effect of meteorology on PM2.5 and O3 in the three northeastern of China, back-propagation neural network (BPNN) and random forest (RF) was used in this study. Firstly, meteorological factors (temperature, relative humidity, precipitation, wind speed, pressure and planetary boundary layer (PBL) height, cloud cover) were put into BPNN and RF models to investigate the influence of weather conditions on PM2.5 and O3 during 2015–2021. Results showed that RF performed better than BPNN. Among meteorological factors, precipitation and PBL significantly affect PM2.5; temperature and pressure (PRS) significantly affect O3. Then, when pollutants factors, such as PM2.5, O3, carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and formaldehyde (HCHO) were also inputted into BPNN and RF, it was illustrated that prediction accuracy of them had been improved. This study also revealed that the contribution of meteorology to PM2.5 and O3 were 16.73

As the cereal-producing region of China’s black soil, there are many agricultural activities, mainly including cultivation, straw processing, and harvesting, in Northeast China. In the process of carrying out these agricultural activities, they inevitably lead to large carbon emissions, among which straw burning and wind erosion are two processes that directly lead to carbon emissions from farmland. In this study, we estimated the carbon emissions of these two processes based on two algorithms: the improved Fire Radiative Power and Community Multiscale Air Quality (FENGSHA) algorithms. The results showed that the carbon emissions from straw burning in Northeast China can reach up to 126,651 Gg in 2017, and those from wind erosion of agricultural land can reach up to 80.45 Gg a year. When compared with the carbon emissions in 2017, the implementation of the Action Plan for Straw Disposal in Northeast China resulted in around a 40% decrease in the carbon emissions from straw burning in 2022. However, the carbon emissions from agricultural land wind erosion increased by about 10%. The seasonal characteristics of both straw burning and farmland wind erosion were obvious, with both being concentrated in the spring. In addition, based on the potential impacts of straw burning on wind erosion, we proposed that a Y-shaped integrated monitoring network should be constructed to monitor both straw burning and wind erosion in Northeast China. Thus, the study of carbon emissions from straw burning and wind erosion in Northeast China is of great importance for energy conservation and emission reduction, and the implementation of a straw burning ban policy, straw recycling and reuse, and a black soil protection policy is recommended.

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both cru-cial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area map-ping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating mul-tiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38％to 86.90％and 73.47％to 97.14％for the 52TCQ and 51TYM cases,respectively.In comparison,the ac-curacy of the burned area map was improved to 87.14％and 98.33％for the 52TCQ and 51TYM cases,respectively by multiple vari-able logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Given the increasingly severe global fires, the accurate detection of small and fragmented cropland fires has been a significant challenge. The use of medium-resolution satellite data can enhance detection accuracy; however, key challenges in this approach include accurately capturing the annual and interannual variations of burning characteristics and identifying outliers within the time series of these changes. In this study, we focus on a typical crop-straw burning area in Henan Province, located on the North China Plain. We develop an automated burned-area detection algorithm based on near-infrared and short-wave infrared data from Landsat 5 imagery. Our method integrates time-series outlier analysis using filtering and automatic iterative algorithms to determine the optimal threshold for detecting burned areas. Our results demonstrate the effectiveness of using preceding time-series and seasonal time-series analysis to differentiate fire-related changes from seasonal and non-seasonal influences on vegetation. Optimal threshold validation results reveal that the automatic threshold method is efficient and feasible with an overall accuracy exceeding 93%. The resulting burned-area map achieves a total accuracy of 93.25%, far surpassing the 76.5% detection accuracy of the MCD64A1 fire product, thereby highlighting the efficacy of our algorithm. In conclusion, our algorithm is suitable for detecting burned areas in large-scale farmland settings and provides valuable information for the development of future detection algorithms.