{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:01:48Z","timestamp":1776441708735,"version":"3.51.2"},"reference-count":71,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T00:00:00Z","timestamp":1678492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42130113"],"award-info":[{"award-number":["42130113"]}]},{"name":"National Natural Science Foundation of China","award":["201806"],"award-info":[{"award-number":["201806"]}]},{"name":"National Natural Science Foundation of China","award":["22JR5RA090"],"award-info":[{"award-number":["22JR5RA090"]}]},{"name":"LZJTU EP","award":["42130113"],"award-info":[{"award-number":["42130113"]}]},{"name":"LZJTU EP","award":["201806"],"award-info":[{"award-number":["201806"]}]},{"name":"LZJTU EP","award":["22JR5RA090"],"award-info":[{"award-number":["22JR5RA090"]}]},{"name":"Gansu Science and Technology Program","award":["42130113"],"award-info":[{"award-number":["42130113"]}]},{"name":"Gansu Science and Technology Program","award":["201806"],"award-info":[{"award-number":["201806"]}]},{"name":"Gansu Science and Technology Program","award":["22JR5RA090"],"award-info":[{"award-number":["22JR5RA090"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D\/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model\u2019s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection.<\/jats:p>","DOI":"10.3390\/rs15061541","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T03:03:57Z","timestamp":1678676637000},"page":"1541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Da","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730000, China"},{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730000, China"},{"name":"National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1366-5170","authenticated-orcid":false,"given":"Chunlin","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Juan","family":"Gu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Western China\u2019s Environmental Systems, Ministry of Education, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Jinliang","family":"Hou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-3159","authenticated-orcid":false,"given":"Weixiao","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Peng","family":"Dou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"given":"Yaya","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Akhoondzadeh, M., Amani, M., and Mahdavi, S. (2021). Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform. Remote Sens., 13.","DOI":"10.3390\/rs13020220"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.foreco.2012.10.022","article-title":"Global wildland fire season severity in the 21st century","volume":"294","author":"Flannigan","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, J., Jian, H., Fan, X., and Yan, F. (2021). Fire Diurnal Cycle Derived from a Combination of the Himawari-8 and VIIRS Satellites to Improve Fire Emission Assessments in Southeast Australia. Remote Sens., 13.","DOI":"10.3390\/rs13152852"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1080\/2150704X.2017.1350303","article-title":"Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8","volume":"8","author":"Xu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1139\/x86-171","article-title":"Forest fire monitoring using the NOAA satellite AVHRR","volume":"16","author":"Flannigan","year":"1986","journal-title":"Can. J. For. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"32215","DOI":"10.1029\/98JD01644","article-title":"Potential global fire monitoring from EOS-MODIS","volume":"103","author":"Kaufman","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1080\/01431169608949018","article-title":"A contextual algorithm for AVHRR fire detection","volume":"17","author":"Flasse","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(03)00184-6","article-title":"An Enhanced Contextual Fire Detection Algorithm for MODIS","volume":"87","author":"Giglio","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2016.02.054","article-title":"The collection 6 MODIS active fire detection algorithm and fire products","volume":"178","author":"Giglio","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2013.12.008","article-title":"The New VIIRS 375m active fire detection data product: Algorithm description and initial assessment","volume":"143","author":"Schroeder","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2015.01.010","article-title":"Assessment of VIIRS 375m active fire detection product for direct burned area mapping","volume":"160","author":"Oliva","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hally, B., Wallace, L., Reinke, K., and Jones, S. (2016, January 12\u201319). Assessment of the Utility of the Advanced Himawari Imager to Detect Active Fire over Australia. Proceedings of the ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprsarchives-XLI-B8-65-2016"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hally, B., Wallace, L., Reinke, K., and Jones, S. (2017). A Broad-Area Method for the Diurnal Characterisation of Upwelling Medium Wave Infrared Radiation. Remote Sens., 9.","DOI":"10.3390\/rs9020167"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wickramasinghe, C.H., Jones, S., Reinke, K., and Wallace, L. (2016). Development of a Multi-Spatial Resolution Approach to the Surveillance of Active Fire Lines Using Himawari-8. Remote Sens., 8.","DOI":"10.3390\/rs8110932"},{"key":"ref_16","unstructured":"Chen, H., Duan, S., Ge, X., Huang, S., Wang, T., Xu, D., and Xu, B. (2020, January 23\u201325). Multi-temporal remote sensing fire detection based on GBDT in Yunnan area. Proceedings of the 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jang, E., Kang, Y., Im, J., Lee, D.-W., Yoon, J., and Kim, S.-K. (2019). Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea. Remote Sens., 11.","DOI":"10.3390\/rs11030271"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5040","DOI":"10.1080\/01431161.2022.2119110","article-title":"Wildfire detection through deep learning based on Himawari-8 satellites platform","volume":"43","author":"Ding","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1080\/15481603.2022.2143872","article-title":"A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency","volume":"59","author":"Kang","year":"2022","journal-title":"GISci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3844\/ajassp.2009.157.166","article-title":"Active Fire Monitoring with Level 1.5 MSG Satellite Images","volume":"6","author":"Hassini","year":"2009","journal-title":"Am. J. Appl. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2016.08.008","article-title":"RST-FIRES, an exportable algorithm for early-fire detection and monitoring: Description, implementation, and field validation in the case of the MSG-SEVIRI sensor","volume":"186","author":"Filizzola","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rostami, A., Shah-Hosseini, R., Asgari, S., Zarei, A., Aghdami-Nia, M., and Homayouni, S. (2022). Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning. Remote Sens., 14.","DOI":"10.3390\/rs14040992"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sulova, A., and Jokar Arsanjani, J. (2021). 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","unstructured":"Milanovi\u0107, S., Markovi\u0107, N., Pamu\u010dar, D., Gigovi\u0107, L., Kosti\u0107, P., and Milanovi\u0107, S.D. (2021). Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. Forests, 12.","DOI":"10.3390\/f12010005"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Y., He, B., Kong, P., Xu, H., Zhang, Q., Quan, X., and Gengke, L. (2021, January 11\u201316). Near Real-Time Wildfire Detection in Southwestern China Using Himawari-8 Data. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554636"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1016\/j.rse.2008.03.003","article-title":"Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER)","volume":"112","author":"Giglio","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1109\/TGRS.2006.875941","article-title":"Validation of active fire detection from moderate-resolution satellite sensors: The MODIS example in northern eurasia","volume":"44","author":"Csiszar","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1016\/j.rse.2008.01.005","article-title":"Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data","volume":"112","author":"Schroeder","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI141.1","article-title":"Validation of MODIS Active Fire Detection Products Derived from Two Algorithms","volume":"9","author":"Morisette","year":"2005","journal-title":"Earth Interact"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1080\/01431160500113526","article-title":"Validation of the MODIS active fire product over Southern Africa with ASTER data","volume":"26","author":"Morisette","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_32","unstructured":"Hennessy, K., Lucas, C., Nicholls, N., Bathols, J., Suppiah, R., and Ricketts, J. (2005). Climate Change Impacts on Fire-Weather in South-East Australia, Csiro Publishing."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.rse.2017.06.028","article-title":"Approaches for synergistically exploiting VIIRS I- and M-Band data in regional active fire detection and FRP assessment: A demonstration with respect to agricultural residue burning in Eastern China","volume":"198","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2017.02.024","article-title":"Major advances in geostationary fire radiative power (FRP) retrieval over Asia and Australia stemming from use of Himarawi-8 AHI","volume":"193","author":"Xu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112971","DOI":"10.1016\/j.rse.2022.112971","article-title":"Machine learning-based retrieval of day and night cloud macrophysical parameters over East Asia using Himawari-8 data","volume":"273","author":"Yang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_37","unstructured":"(2018, May 14). User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product, Available online: https:\/\/lpdaac.usgs.gov\/documents\/101\/MCD12_User_Guide_V6.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TGRS.2008.915751","article-title":"Fire Detection and Fire Characterization Over Africa Using Meteosat SEVIRI","volume":"46","author":"Roberts","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13217","DOI":"10.5194\/acp-15-13217-2015","article-title":"LSA SAF Meteosat FRP products\u2014Part 1: Algorithms, product contents, and analysis","volume":"15","author":"Wooster","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jang, E., Im, J., Park, G.-H., and Park, Y.-G. (2017). Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data. Remote Sens., 9.","DOI":"10.3390\/rs9080821"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1080\/15481603.2017.1364837","article-title":"Evaluation of Goddard\u2019s LiDAR, hyperspectral, and thermal data products for mapping urban land-cover types","volume":"55","author":"Zhang","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2032s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2018.01.018","article-title":"Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data","volume":"137","author":"Yoo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/15481603.2016.1250328","article-title":"Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data","volume":"54","author":"Guo","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.landurbplan.2014.07.005","article-title":"Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data","volume":"130","author":"Lu","year":"2014","journal-title":"Landsc. Urban Plan."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1080\/15481603.2017.1302181","article-title":"A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada)","volume":"54","author":"Richardson","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/15481603.2017.1331510","article-title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","volume":"54","author":"Amani","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.rse.2017.10.030","article-title":"Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data","volume":"204","author":"Chen","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s41651-022-00110-4","article-title":"Estimating the Agricultural Farm Soil Moisture Using Spectral Indices of Landsat 8, and Sentinel-1, and Artificial Neural Networks","volume":"6","author":"Ghasemloo","year":"2022","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Diaz-Uriarte, R., and Alvarez de Andr\u00e9s, S. (2006). Gene Selection and Classification of Microarray Data Using Random Forest. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-3"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yu, F., Wu, X., Shao, X., and Kondratovich, V. (2016, January 10\u201315). Evaluation of Himawari-8 AHI geospatial calibration accuracy using SNPP VIIRS SNO data. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729755"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/17538947.2018.1527402","article-title":"Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products","volume":"13","author":"Wickramasinghe","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.rse.2010.10.009","article-title":"Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery","volume":"115","author":"Stehman","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_62","unstructured":"Lazar, J., Feng, J.H., and Hochheiser, H. (2017). Research Methods in Human Computer Interaction, Morgan Kaufmann. [2nd ed.]."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene Selection for Cancer Classification using Support Vector Machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1080\/15481603.2017.1408892","article-title":"Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application","volume":"55","author":"Georganos","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/1882471.1882479","article-title":"Apples-to-Apples in Cross-Validation Studies: Pitfalls in Classifier Performance Measurement ABSTRACT","volume":"12","author":"Forman","year":"2010","journal-title":"SIGKDD Explor."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s41651-022-00104-2","article-title":"Constraint-Based Evaluation of Map Images Generalized by Deep Learning","volume":"6","author":"Courtial","year":"2022","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Minh, H.V., Avtar, R., Mohan, G., Misra, P., and Kurasaki, M. (2019). Monitoring and Mapping of Rice Cropping Pattern in Flooding Area in the Vietnamese Mekong Delta Using Sentinel-1A Data: A Case of An Giang Province. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8050211"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s41651-022-00105-1","article-title":"Performance Assessment of Spatial Interpolation Methods for the Estimation of Atmospheric Carbon Dioxide in the Wider Geographic Extent","volume":"6","author":"Uddin","year":"2022","journal-title":"J. Geovis. Spat. Anal."},{"key":"ref_69","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_70","unstructured":"(2023, January 12). README_H08_L2WLF. Available online: ftp:\/\/ftp.ptree.jaxa.jp\/pub\/README_H08_L2WLF.txt."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"111600","DOI":"10.1016\/j.rse.2019.111600","article-title":"A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records","volume":"237","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1541\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:52:46Z","timestamp":1760122366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1541"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,11]]},"references-count":71,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061541"],"URL":"https:\/\/doi.org\/10.3390\/rs15061541","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,11]]}}}