178 citations as recorded by crossref.

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2. Reduced global fire activity due to human demography slows global warming by enhanced land carbon uptake C. Wu et al. 10.1073/pnas.2101186119
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5. Estimation of Forest Fire Burned Area by Distinguishing Non-Photosynthetic and Photosynthetic Vegetation Using Triangular Space Method X. Wang et al. 10.3390/rs15123115
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7. Spatiotemporal pattern of ENSO-induced modulation on landscape fires over Pacific Rim from 2001 to 2020 F. Li et al. 10.1016/j.accre.2024.11.001
8. Fires in the South American Chaco, from dry forests to wetlands: response to climate depends on land cover R. San Martín et al. 10.1186/s42408-023-00212-4
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11. A patch-based algorithm for global and daily burned area mapping M. Campagnolo et al. 10.1016/j.rse.2019.111288
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13. Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model J. Liu et al. 10.3390/rs13245131
14. Performance Analysis of Deep Convolutional Autoencoders with Different Patch Sizes for Change Detection from Burnt Areas P. de Bem et al. 10.3390/rs12162576
15. Assessing the Accuracy of MODIS MCD64A1 C6 and FireCCI51 Burned Area Products in Mediterranean Ecosystems T. Katagis & I. Gitas 10.3390/rs14030602
16. Accounting for forest fire risks: global insights for climate change mitigation L. Chu et al. 10.1007/s11027-023-10087-0
17. Accuracy estimation of two global burned area products at national scale T. Katagis & I. Gitas 10.1088/1755-1315/932/1/012001
18. Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis J. Lizundia-Loiola et al. 10.3390/rs12010151
19. Burned area detection and mapping using time series Sentinel-2 multispectral images P. Liu et al. 10.1016/j.rse.2023.113753
20. Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia H. Achour et al. 10.1080/10106049.2021.1980118
21. A remote sensing-based approach to estimating the fire spread rate parameter for individual burn patch extraction M. Humber et al. 10.1080/01431161.2022.2027544
22. Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery A. Pacheco et al. 10.3390/rs13071345
23. Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction R. Ghali & M. Akhloufi 10.3390/fire6050192
24. Interannual variability and climatic sensitivity of global wildfire activity R. Tang et al. 10.1016/j.accre.2021.07.001
25. Central African biomass carbon losses and gains during 2010–2019 Z. Zhao et al. 10.1016/j.oneear.2024.01.021
26. Soil Burn Severity Assessment Using Sentinel-2 and Radiometric Measurements R. Llorens et al. 10.3390/fire7120487
27. Optimizing afforestation and reforestation strategies to enhance ecosystem services in critically degraded regions . Fahrudin et al. 10.1016/j.tfp.2024.100700
28. Modeling the Spatial Distribution of Wildfire Risk in Chile Under Current and Future Climate Scenarios J. Gajardo et al. 10.3390/fire8030113
29. Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa M. Tanase et al. 10.3390/rs12020334
30. Validating MCD64A1 and FireCCI51 burned area mapping in the Qinghai-Tibetan Plateau Y. Huang et al. 10.1080/10106049.2023.2285345
31. Modeling and prediction of fire occurrences along an elevational gradient in Western Himalayas S. Bar et al. 10.1016/j.apgeog.2022.102867
32. Observational evidence of wildfire-promoting soil moisture anomalies S. O et al. 10.1038/s41598-020-67530-4
33. Validation of Earth Observation Time-Series: A Review for Large-Area and Temporally Dense Land Surface Products S. Mayr et al. 10.3390/rs11222616
34. High-resolution atmospheric mercury emission from open biomass burning in China: Integration of localized emission factors and multi-source finer resolution remote sensing data Z. Xu et al. 10.1016/j.envint.2023.108102
35. Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires F. Filipponi 10.3390/rs11060622
36. A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine E. Roteta et al. 10.3390/rs13214298
37. IntelliSense silk fibroin ionotronic batteries for wildfire detection and alarm Q. Liu et al. 10.1016/j.nanoen.2022.107630
38. Assessment of Burned Areas during the Pantanal Fire Crisis in 2020 Using Sentinel-2 Images Y. Shimabukuro et al. 10.3390/fire6070277
39. Heating effect on chromium speciation and mobility in Cr-rich soils: A snapshot from New Caledonia G. Thery et al. 10.1016/j.scitotenv.2024.171037
40. Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards M. Kouachi et al. 10.3390/rs16132500
41. Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data J. Lizundia-Loiola et al. 10.3390/rs13214295
42. Mapping tropical forest degradation with deep learning and Planet NICFI data R. Dalagnol et al. 10.1016/j.rse.2023.113798
43. Evaluating the Abilities of Satellite-Derived Burned Area Products to Detect Forest Burning in China X. Wang et al. 10.3390/rs15133260
44. Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models L. Teckentrup et al. 10.5194/bg-16-3883-2019
45. Global Detection of Long-Term (1982–2017) Burned Area with AVHRR-LTDR Data G. Otón et al. 10.3390/rs11182079
46. Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades A. Chen et al. 10.1016/j.geosus.2020.03.002
47. A novel deep Siamese framework for burned area mapping Leveraging mixture of experts S. Seydi et al. 10.1016/j.engappai.2024.108280
48. Assessing satellite-derived fire patches with functional diversity trait methods M. Moreno et al. 10.1016/j.rse.2020.111897
49. Seasonal spatial-temporal trends of vegetation recovery in burned areas across Africa O. Maillard et al. 10.1371/journal.pone.0316472
50. High Resolution (30 m) Burned Area Product Improves the Ability for Carbon Emission Estimation in Africa B. Qi et al. 10.1029/2024EF005051
51. The Landscape Fire Scars Database: mapping historical burned area and fire severity in Chile A. Miranda et al. 10.5194/essd-14-3599-2022
52. Refining historical burned area data from satellite observations V. Fernández-García & C. Kull 10.1016/j.jag.2023.103350
53. Future climate change impact on wildfire danger over the Mediterranean: the case of Greece A. Rovithakis et al. 10.1088/1748-9326/ac5f94
54. Fire decline in dry tropical ecosystems enhances decadal land carbon sink Y. Yin et al. 10.1038/s41467-020-15852-2
55. Using An Attention-Based LSTM Encoder–Decoder Network for Near Real-Time Disturbance Detection Y. Yuan et al. 10.1109/JSTARS.2020.2988324
56. Suitability of band angle indices for burned area mapping in the Maule Region (Chile) P. Oliva et al. 10.3389/ffgc.2022.1052299
57. Analysis of Trends in the FireCCI Global Long Term Burned Area Product (1982–2018) G. Otón et al. 10.3390/fire4040074
58. Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review E. Kurbanov et al. 10.3390/rs14194714
59. Lightning-induced fire regime in Portugal based on satellite-derived and in situ data L. Menezes et al. 10.1016/j.agrformet.2024.110108
60. Influence of atmospheric teleconnections on interannual variability of Arctic-boreal fires Z. Zhao et al. 10.1016/j.scitotenv.2022.156550
61. Description and evaluation of the JULES-ES set-up for ISIMIP2b C. Mathison et al. 10.5194/gmd-16-4249-2023
62. Fire, flammability and functional traits at the forefront of global change ecology R. Mitchell & A. Martin 10.1111/1365-2435.14432
63. Recent global and regional trends in burned area and their compensating environmental controls M. Forkel et al. 10.1088/2515-7620/ab25d2
64. Evaluating accuracy of four MODIS-derived burned area products for tropical peatland and non-peatland fires Y. Vetrita et al. 10.1088/1748-9326/abd3d1
65. A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates C. Whaley et al. 10.5194/gmd-17-7141-2024
66. Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine E. Roteta et al. 10.3390/rs13040816
67. Estimating the Carbon Emissions of Remotely Sensed Energy-Intensive Industries Using VIIRS Thermal Anomaly-Derived Industrial Heat Sources and Auxiliary Data X. Kong et al. 10.3390/rs14122901
68. Assessment and characterization of sources of error impacting the accuracy of global burned area products M. Franquesa et al. 10.1016/j.rse.2022.113214
69. Satellite Remote Sensing Contributions to Wildland Fire Science and Management E. Chuvieco et al. 10.1007/s40725-020-00116-5
70. Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin L. Galizia et al. 10.5194/nhess-21-73-2021
71. Anticipating Future Risks of Climate-Driven Wildfires in Boreal Forests S. Corning et al. 10.3390/fire7040144
72. Extreme Events Contributing to Tipping Elements and Tipping Points A. Romanou et al. 10.1007/s10712-024-09863-7
73. Enhancing burned area monitoring with VIIRS dataset: A case study in Sub-Saharan Africa B. Ouattara et al. 10.1016/j.srs.2024.100165
74. Understanding wildfire occurrence and size in Jalisco, Mexico: A spatio-temporal analysis C. Toledo-Jaime et al. 10.1016/j.foreco.2024.122349
75. Global ecosystems and fire: Multi‐model assessment of fire‐induced tree‐cover and carbon storage reduction G. Lasslop et al. 10.1111/gcb.15160
76. Analysis of Spectral Separability for Detecting Burned Areas Using Landsat-8 OLI/TIRS Images under Different Biomes in Brazil and Portugal A. Pacheco et al. 10.3390/f14040663
77. Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2001–2021 A. Romanov et al. 10.1016/j.scitotenv.2022.157322
78. Validation of MCD64A1 and FireCCI51 cropland burned area mapping in Ukraine J. Hall et al. 10.1016/j.jag.2021.102443
79. The smokescreen of Russian protected areas R. Cazzolla Gatti et al. 10.1016/j.scitotenv.2021.147372
80. Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations V. Arora et al. 10.5194/bg-20-1313-2023
81. Progress and Limitations in the Satellite-Based Estimate of Burnt Areas G. Laneve et al. 10.3390/rs16010042
82. The Temporal-Based Forest Disturbance Monitoring Analysis: A Case Study of Nature Reserves of Hainan Island of China From 1987 to 2020 H. Xiao et al. 10.3389/fenvs.2022.891752
83. Development of a consistent global long-term burned area product (1982–2018) based on AVHRR-LTDR data G. Otón et al. 10.1016/j.jag.2021.102473
84. Assessing wildfire activity and forest loss in protected areas of the Amazon basin E. Da Ponte et al. 10.1016/j.apgeog.2023.102970
85. Google Earth Engine ile Türkiye'de Yanmış Alanların MODIS ve FireCCI51 Küresel Yanmış Alan Uydu Gözlem Verileriyle Karşılaştırmalı Değerlendirilmesi H. TONBUL 10.48123/rsgis.1410382
86. “Forest fire emissions: A contribution to global climate change” S. Singh 10.3389/ffgc.2022.925480
87. A Global Bottom‐Up Approach to Estimate Fuel Consumed by Fires Using Above Ground Biomass Observations F. Di Giuseppe et al. 10.1029/2021GL095452
88. Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation models M. Forkel et al. 10.5194/bg-16-57-2019
89. The Landsat Burned Area algorithm and products for the conterminous United States T. Hawbaker et al. 10.1016/j.rse.2020.111801
90. DLSR-FireCNet: A deep learning framework for burned area mapping based on decision level super-resolution S. Seydi & M. Sadegh 10.1016/j.rsase.2025.101513
91. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019) J. Balch et al. 10.3390/rs12213498
92. Annual and Seasonal Patterns of Burned Area Products in Arctic-Boreal North America and Russia for 2001–2020 A. Clelland et al. 10.3390/rs16173306
93. Sources of Uncertainty in Regional and Global Terrestrial CO2 Exchange Estimates A. Bastos et al. 10.1029/2019GB006393
94. Mechanisms for Data Acquisition to Train Artificial Intelligence Models for Detecting Increased Susceptibility to Fire Situations by Using Internet of Things Devices and Satellite Systems . Dawid Jurczyński & . Paweł Buchwald 10.12845/sft.63.1.2024.3
95. A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products J. Moreno-Ruiz et al. 10.3390/f10050363
96. Detection and impacts of tiling artifacts in MODIS burned area classification T. Liu & M. Crowley 10.1088/2633-1357/abd8e2
97. Microscopic charcoals in ocean sediments off Africa track past fire intensity from the continent A. Haliuc et al. 10.1038/s43247-023-00800-x
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99. Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations M. Forkel et al. 10.1038/s41598-019-55187-7
100. Sharing Multiple Perspectives on Burning: Towards a Participatory and Intercultural Fire Management Policy in Venezuela, Brazil, and Guyana B. Bilbao et al. 10.3390/fire2030039
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104. A Synergetic Approach to Burned Area Mapping Using Maximum Entropy Modeling Trained with Hyperspectral Data and VIIRS Hotspots A. Fernández-Manso & C. Quintano 10.3390/rs12050858
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