33 citations as recorded by crossref.

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3. A Weakly Supervised Multi-Scale Cross-Modal Information Fusion Method for Wildfire Detection D. Wen et al. https://doi.org/10.3390/computers15050311
4. Substantial discrepancies across global fire emissions inventories: a systematic intercomparison J. Yang et al. https://doi.org/10.1016/j.jes.2026.105563
5. Review of agricultural biomass burning and its impact on air quality in the continental United States of America S. Pinakana et al. https://doi.org/10.1016/j.envadv.2024.100546
6. Tillage and biomass detection for estimating winter-time cropland management practices with satellite remote sensing M. Yli-Heikkilä et al. https://doi.org/10.1080/22797254.2025.2525967
7. Landscape fires and decreasing carbon sequestration capacity: Quantifying greenhouse gas emissions due to the Russo–Ukrainian war R. Vasylyshyn et al. https://doi.org/10.1016/j.ecolind.2025.114453
8. Influence of country development levels and agricultural burning policies on global cropland biomass burning observed from satellites S. An et al. https://doi.org/10.1088/1748-9326/ae1b1d
9. Estimating cropland fire emissions with remote sensing: A literature review and data strategy C. Dahman et al. https://doi.org/10.1051/e3sconf/202567603002
10. Accelerating learning and impact in conservation by tailoring learning and adaptive management approaches to the stage of strategy development S. Reddy et al. https://doi.org/10.1111/csp2.70117
11. Accelerated reduction in China's cropland fires against the background of policy enhancement C. Lian et al. https://doi.org/10.1016/j.eiar.2024.107512
12. Landscape fire emissions from the 5th version of the Global Fire Emissions Database (GFED5) G. van der Werf et al. https://doi.org/10.1038/s41597-025-06127-w
13. A survival guide for assessing global fire risks to natural and human systems C. Steinmann et al. https://doi.org/10.1016/j.ijdrr.2026.106140
14. Integrated leaching-steam explosion pretreatment to upgrade low-grade biomass into high-quality black pellets B. Samal et al. https://doi.org/10.1016/j.renene.2026.125749
15. BuRNN (v1.0): a data-driven fire model S. Lampe et al. https://doi.org/10.5194/gmd-19-955-2026
16. The NASA VIIRS burned area product, global validation, and intercomparison with the NASA MODIS burned area product L. Giglio et al. https://doi.org/10.1016/j.rse.2025.115006
17. The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific D. Lee et al. https://doi.org/10.3390/rs17122092
18. Increase in carbon monoxide (CO) and aerosol optical depth (AOD) observed by satellites in the Northern Hemisphere over the summers of 2008–2023, linked to an increase in wildfires A. Ehret et al. https://doi.org/10.5194/acp-25-6365-2025
19. HSRRSFPD and MEGRDNet: empowering high-resolution farmland parcel mapping in complex terrains Y. Yan et al. https://doi.org/10.1016/j.jag.2026.105346
20. The Global Forest Fire Emissions Prediction System version 1.0 K. Anderson et al. https://doi.org/10.5194/gmd-17-7713-2024
21. Model fires, not ignitions: Capturing the human dimension of global fire regimes M. Kasoar et al. https://doi.org/10.1016/j.crsus.2024.100128
22. Enhanced CH4 emissions from global wildfires likely due to undetected small fires J. Zhao et al. https://doi.org/10.1038/s41467-025-56218-w
23. Two decades of fire activity over the PEEX domain: a look from space, with contribution from models and ground-based measurements L. Sogacheva et al. https://doi.org/10.1080/20964471.2024.2316730
24. Impacts of El Niño–Southern Oscillation on global fire PM2.5 during 2000–2023 Y. Hu et al. https://doi.org/10.1016/j.aosl.2025.100597
25. A global behavioural model of human fire use and management: WHAM! v1.0 O. Perkins et al. https://doi.org/10.5194/gmd-17-3993-2024
26. GloCAB cropland field boundary dataset J. Hall et al. https://doi.org/10.1016/j.dib.2024.110739
27. Evaluation of global fire simulations in CMIP6 Earth system models F. Li et al. https://doi.org/10.5194/gmd-17-8751-2024
28. Small-scale livelihood and cultural fire: Global spatiotemporal characteristics, and gaps in data C. Smith et al. https://doi.org/10.1371/journal.pone.0339561
29. Artificial intelligence with earth observations provides continuous streamflow data across varying wildfire recurrence and recovery scenarios S. Uddin et al. https://doi.org/10.1016/j.envsoft.2026.106989
30. Deep learning for wildfire risk prediction: Integrating remote sensing and environmental data Z. Xu et al. https://doi.org/10.1016/j.isprsjprs.2025.06.002
31. Temporal and Spatial Dynamics of Summer Crop Residue Burning Practices in North China: Exploring the Influence of Climate Change and Anthropogenic Factors S. Yin et al. https://doi.org/10.3390/rs16244763
32. A Comprehensive Survey of Satellite-Based Wildfire Indicators and Spatiotemporal Modeling Approaches: Past, Present, and Future S. Nurdiati et al. https://doi.org/10.3390/earth7040121
33. Landsat-based fire maps reveal higher fire emissions from larger area of low-severity burnings than coarse resolution data in Southeast Asia M. Xiahou & Z. Shen https://doi.org/10.1016/j.jag.2025.104815