{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T14:47:25Z","timestamp":1772635645592,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFC3000300"],"award-info":[{"award-number":["2021YFC3000300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wildfires are predicted to occur more frequently and intensely as a result of global warming, posing a greater threat to human society, terrestrial ecosystems, and the atmosphere. Most existing methods for monitoring wildfire occurrences are based either on static topographical information or weather-based indices. This work explored the advantages of a new machine learning-based \u2018soil properties\u2019 attribute in monitoring wildfire occurrence in Pakistan. Specifically, we used satellite observations during 2001\u20132020 to investigate the correlation at different temporal and spatial scales between wildfire properties (fire count, FC) and soil properties and classes (SoilGrids1km) derived from combination with local covariates using machine learning. The correlations were compared to that obtained with the static topographic index elevation to determine whether soil properties, such as soil bulk density, taxonomy, and texture, provide new independent information about wildfires. Finally, soil properties and the topographical indices were combined to establish multivariate linear regression models to estimate FC. Results show that: (1) the temporal variations of FC are negatively correlated with soil properties using the monthly observations at 1\u00b0 grid and regional scales; and overall opposite annual cycles and interannual variations between and soil properties are observed in Pakistan; (2) compared to the other static variables such as elevation, soil properties shows stronger correlation with the temperate wildfire count in Northern Pakistan but weaker correlation with the wildfire properties in Southern Pakistan; and it is found that combining both types of indices enhances the explained variance for fire attributes in the two regions; (3) In comparison to linear regression models based solely on elevation, multivariate linear regression models based on soil properties offer superior estimates of FC.<\/jats:p>","DOI":"10.3390\/rs14215503","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T08:15:12Z","timestamp":1667376912000},"page":"5503","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Evaluation of Wildfire Occurrences in Pakistan with Global Gridded Soil Properties Derived from Remotely Sensed Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Warda","family":"Rafaqat","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China USTC, Hefei 230026, China"}]},{"given":"Mansoor","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering & Information Science, University of Science and Technology of China USTC, Hefei 230027, China"}]},{"given":"Rida","family":"Kanwal","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China USTC, Hefei 230026, China"}]},{"given":"Song","family":"Weiguo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fire Science, University of Science and Technology of China USTC, Hefei 230026, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1016\/j.scitotenv.2016.11.025","article-title":"A review of biomass burning: Emissions and impacts on air quality, health and climate in China","volume":"579","author":"Chen","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.tree.2005.04.025","article-title":"Fire as a global \u2018herbivore\u2019: The ecology and evolution of flammable ecosystems","volume":"20","author":"Bond","year":"2005","journal-title":"Trends Ecol. Evol."},{"key":"ref_3","first-page":"485","article-title":"Projections of future forest age class structure under the influence of fire and harvesting: Implications for forest management in the boreal forest of eastern Canada","volume":"90","author":"Bergeron","year":"2017","journal-title":"For. Int. J. For. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1111\/jvs.12466","article-title":"Fire is a stronger driver of forest composition than logging in the boreal forest of eastern Canada","volume":"28","author":"Boucher","year":"2017","journal-title":"J. Veg. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.5194\/bg-15-1173-2018","article-title":"Fire intensity impacts on post-fire temperate coniferous forest net primary productivity","volume":"15","author":"Sparks","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_6","first-page":"e2158","article-title":"Forest fires pollution impact on the solar UV irradiance at the ground","volume":"18","author":"Tzanis","year":"2009","journal-title":"Fresenius Environ. Bull"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1029\/2000GB001382","article-title":"Emission of trace gases and aerosols from biomass burning","volume":"15","author":"Andreae","year":"2001","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1126\/science.1132075","article-title":"The impact of boreal forest fire on climate warming","volume":"314","author":"Randerson","year":"2006","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41893-020-00646-7","article-title":"Economic footprint of California wildfires in 2018","volume":"4","author":"Wang","year":"2021","journal-title":"Nat. Sustain."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1071\/WF09092","article-title":"Will climate change drive 21st century burn rates in Canadian boreal forest outside of its natural variability: Collating global climate model experiments with sedimentary charcoal data","volume":"19","author":"Bergeron","year":"2010","journal-title":"Int. J. Wildland Fire"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1071\/WF08187","article-title":"Implications of changing climate for global wildland fire","volume":"18","author":"Flannigan","year":"2009","journal-title":"Int. J. Wildland Fire"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecolmodel.2008.11.017","article-title":"Development of a framework for fire risk assessment using remote sensing and geographic information system technologies","volume":"221","author":"Chuvieco","year":"2010","journal-title":"Ecol. Model."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/j.1365-2664.2006.01184.x","article-title":"The effect of fire season, fire frequency, rainfall and management on fire intensity in savanna vegetation in South Africa","volume":"43","author":"Govender","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.apgeog.2015.11.014","article-title":"Wildfire ignition in the forests of southeast China: Identifying drivers and spatial distribution to predict wildfire likelihood","volume":"66","author":"Guo","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"118381","DOI":"10.1016\/j.foreco.2020.118381","article-title":"Fire from policy, human interventions, or biophysical factors? Temporal\u2013spatial patterns of forest fire in southwestern China","volume":"474","author":"Xiong","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1080\/01431160210144651","article-title":"Review of users\u2019 needs in operational fire danger estimation: The Oklahoma example","volume":"24","author":"Carlson","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1071\/WF03046","article-title":"Slope and wind effects on fire propagation","volume":"13","author":"Viegas","year":"2004","journal-title":"Int. J. Wildland Fire"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.rse.2013.05.029","article-title":"A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products","volume":"136","author":"Yebra","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1071\/WF12077","article-title":"The influence of fuel moisture content on the combustion of Eucalyptus foliage","volume":"22","author":"Possell","year":"2012","journal-title":"Int. J. Wildland Fire"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"222","DOI":"10.4314\/sajg.v7i3.2","article-title":"Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas","volume":"7","author":"Molaudzi","year":"2018","journal-title":"South Afr. J. Geomat."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hamadeh, N., Daya, B., Hilal, A., and Chauvet, P. (May, January 29). An analytical review on the most widely used meteorological models in forest fire prediction. Proceedings of the 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), Beirut, Lebanon.","DOI":"10.1109\/TAEECE.2015.7113633"},{"key":"ref_22","unstructured":"Van Wagner, C.E. (1987). Development and Structure of the Canadian Forest Fire Weather Index System, Canadian Forest Service Publications. Canadian Forestry Service, Headquarters, Ottawa. Forestry Technical Report 35."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sulova, A., and Jokar Arsanjani, J. (2020). Exploratory analysis of driving force of wildfires in Australia: An application of machine learning within Google Earth engine. Remote Sens., 13.","DOI":"10.3390\/rs13010010"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.isprsjprs.2014.03.011","article-title":"Operational perspective of remote sensing-based forest fire danger forecasting systems","volume":"104","author":"Chowdhury","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","unstructured":"Li, H. (2011). Digital Soil Mapping. Handbook of Soil Science, CRC."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Boettinger, J.L., Howell, D.W., Moore, A.C., Hartemink, A.E., and Kienast-Brown, S. (2010). Digital Soil Mapping: Bridging Research, Environmental Application, and Operation, Springer Science & Business Media.","DOI":"10.1007\/978-90-481-8863-5"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1255\/jnirs.716","article-title":"Infrared spectroscopy\u2014Enabling an evidence-based diagnostic surveillance approach to agricultural and environmental management in developing countries","volume":"15","author":"Shepherd","year":"2007","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1111\/gcb.12632","article-title":"Soil Spectroscopy: An Opportunity to Be Seized","volume":"21","author":"Nocita","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.asr.2004.03.012","article-title":"Terra and Aqua MODIS products available from NASA GES DAAC","volume":"34","author":"Savtchenko","year":"2004","journal-title":"Adv. Space Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B., Kempen, B., Leenaars, J.G., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., Mendes de Jesus, J., and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.jaridenv.2005.03.013","article-title":"The influence of soil texture and vegetation on soil moisture under rainout shelters in a semi-desert grassland","volume":"63","author":"English","year":"2005","journal-title":"J. Arid Environ."},{"key":"ref_32","first-page":"226","article-title":"The influence of soil on vegetation structure and plant diversity in different tropical savannic and forest habitats","volume":"11","author":"Rodrigues","year":"2018","journal-title":"J. Plant Ecol."},{"key":"ref_33","unstructured":"Hengl, T., and Nauman, T. (2018). Predicted USDA soil great groups at 250 m (probabilities). Zenodo, Available online: https:\/\/zenodo.org\/record\/3528062#.Y2CfcHZByUk."},{"key":"ref_34","unstructured":"Hengl, T. (2018). Soil bulk density (fine earth) 10 \u00d7 kg\/m-cubic at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Zenodo, Available online: https:\/\/zenodo.org\/record\/1492157#.Y2CfzHZByUk."},{"key":"ref_35","unstructured":"Hengl, T. (2018). Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m. Zenodo, Available online: https:\/\/zenodo.org\/record\/2525817#.Y2Cf9HZByUk."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ontel, I., Ir Ontel, I., Irimescu, A., Boldeanu, G., Mihailescu, D., Angearu, C.V., Nertan, A., Craciunescu, V., and Negreanu, S. (2021). Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania. Sensors, 21.","DOI":"10.3390\/s21248371"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.isprsjprs.2021.10.019","article-title":"Assessing forest fire properties in Northeastern Asia and Southern China with satellite microwave Emissivity Difference Vegetation Index (EDVI)","volume":"183","author":"Li","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rafaqat, W., Iqbal, M., Kanwal, R., and Song, W. (2022). Study of Driving Factors Using Machine Learning to Determine the Effect of Topography, Climate, and Fuel on Wildfire in Pakistan. Remote Sens., 14.","DOI":"10.3390\/rs14081918"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1007\/s00704-019-02899-5","article-title":"Seasonal near-surface air temperature dependence on elevation and geographical coordinates for Pakistan","volume":"138","author":"Kattel","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"151","DOI":"10.5094\/APR.2011.020","article-title":"Long\u2013range transport of soil dust and smoke pollution in the South Asian region","volume":"2","author":"Begum","year":"2011","journal-title":"Atmos. Pollut. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1071\/WF10131","article-title":"Fire frequency analysis in Portugal (1975\u20132005), using Landsat-based burnt area maps","volume":"21","author":"Oliveira","year":"2011","journal-title":"Int. J. Wildland Fire"},{"key":"ref_42","first-page":"53","article-title":"Large forest fires in mainland Portugal, brief characterization. M\u00e9diterran\u00e9e","volume":"121","year":"2013","journal-title":"Rev. G\u00e9ographique Des Pays M\u00e9diterran\u00e9ens\/J. Mediterr. Geogr."},{"key":"ref_43","unstructured":"Smith, G.D. (1934). Experimental Studies on the Development of Heavy Claypans in Soils, University of Missouri, College of Agriculture, Agricultural Experiment Station."},{"key":"ref_44","unstructured":"(2022, September 27). MODIS\/Aqua+Terra Thermal Anomalies\/Fire locations 1 km FIRMS V006 NRT (Vector Data), Available online: https:\/\/catalog.data.gov\/dataset\/modis-aqua-terra-thermal-anomalies-fire-locations-1km-firms-v006-nrt-vector-data."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The Shuttle Radar Topography Mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1071\/WF06007","article-title":"Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA","volume":"16","author":"Maingi","year":"2007","journal-title":"Int. J. Wildland Fire"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.foreco.2013.03.050","article-title":"Conifer regeneration following stand-replacing wildfire varies along an elevation gradient in a ponderosa pine forest, Oregon, USA","volume":"302","author":"Dodson","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Starbuck, C.A., Considine, E.S., and Chambers, C.L. (2020). Water and elevation are more important than burn severity in predicting bat activity at multiple scales in a post-wildfire landscape. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231170"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5194\/nhess-10-485-2010","article-title":"Assessment and validation of wildfire susceptibility and hazard in Portugal","volume":"10","author":"Verde","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e2009717118","DOI":"10.1073\/pnas.2009717118","article-title":"Warming enabled upslope advance in western US forest fires","volume":"118","author":"Alizadeh","year":"2021","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.landurbplan.2010.11.017","article-title":"Land use and topography influences on wildfire occurrence in northern Portugal","volume":"100","author":"Carmo","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.foreco.2017.04.006","article-title":"Interacting effects of fire severity, time since fire and topography on vegetation structure after wildfire","volume":"396","author":"Bassett","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.foreco.2017.04.033","article-title":"Interacting effects of topography, vegetation, human activities and wildland-urban interfaces on wildfire ignition risk","volume":"397","author":"Touza","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s10980-015-0257-6","article-title":"The relative impacts of vegetation, topography and spatial arrangement on building loss to wildfires in case studies of California and Colorado","volume":"31","author":"Alexandre","year":"2016","journal-title":"Landsc. Ecol."},{"key":"ref_55","unstructured":"Louis Giglio, C.J. (2022, September 27). MODIS\/Aqua Thermal Anomalies\/Fire 5-Min L2 Swath 1 km V006. NASA EOSDIS Land Processes DAAC, 2015-08-26T00:00:00.000Z. 006. Available online: https:\/\/data.amerigeoss.org\/es\/dataset\/modis-aqua-thermal-anomalies-fire-5-min-l2-swath-1km-v006."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/00103624.2020.1854294","article-title":"Soil salinity research in 21st century in Pakistan: Its impact on availability of plant nutrients, growth and yield of crops","volume":"52","author":"Syed","year":"2021","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1071\/WF02053","article-title":"Detection of non-linearities in the dependence of burn area on fuel age and climatic variables","volume":"12","author":"Schoenberg","year":"2003","journal-title":"Int. J. Wildland Fire"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"77","DOI":"10.4996\/fireecology.0801077","article-title":"Modelling fire ignition probability from satellite estimates of live fuel moisture content","volume":"8","author":"Jurdao","year":"2012","journal-title":"Fire Ecol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1890\/13.WB.016","article-title":"What\u2019s in a name? The importance of soil taxonomy for ecology and biogeochemistry","volume":"11","author":"Schimel","year":"2013","journal-title":"Front. Ecol. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106039","DOI":"10.1016\/j.ecoleng.2020.106039","article-title":"The effect of soil and plant material transplants on vegetation and soil biota during forest restoration in a limestone quarry: A case study","volume":"158","author":"Kukla","year":"2020","journal-title":"Ecol. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/ldr.3654","article-title":"Usage of MODIS NDVI to evaluate the effect of soil and water conservation measures on vegetation in Burkina Faso","volume":"32","author":"Nyamekye","year":"2021","journal-title":"Land Degrad. Dev."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1111\/1365-2664.13597","article-title":"Trade-off between vegetation type, soil erosion control and surface water in global semi-arid regions: A meta-analysis","volume":"57","author":"Wu","year":"2020","journal-title":"J. Appl. Ecol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.1002\/2016GL068614","article-title":"Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia","volume":"43","author":"Nolan","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1126\/science.1128834","article-title":"Warming and earlier spring increase western US forest wildfire activity","volume":"313","author":"Westerling","year":"2006","journal-title":"Science"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.rse.2007.05.004","article-title":"Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon","volume":"112","author":"Schroeder","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1071\/WF15090","article-title":"Measuring radiant emissions from entire prescribed fires with ground, airborne and satellite sensors\u2013RxCADRE 2012","volume":"25","author":"Dickinson","year":"2015","journal-title":"Int. J. Wildland Fire"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Fu, Y., Li, R., Wang, X., Bergeron, Y., Valeria, O., Chavard\u00e8s, R.D., Wang, Y., and Hu, J. (2020). Fire detection and fire radiative power in forests and low-biomass lands in Northeast Asia: MODIS versus VIIRS Fire Products. Remote Sens., 12.","DOI":"10.3390\/rs12182870"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5503\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:08:42Z","timestamp":1760144922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5503"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":68,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215503"],"URL":"https:\/\/doi.org\/10.3390\/rs14215503","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,1]]}}}