{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T11:08:19Z","timestamp":1762254499542,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"],"award-info":[{"award-number":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"]}]},{"name":"Science and Technology Project for Black Soil Granary","award":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"],"award-info":[{"award-number":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"]}]},{"name":"Environmental Protection Program of Jilin Province, China","award":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"],"award-info":[{"award-number":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"]}]},{"name":"Science and Technology Development Plan Project of Jilin Province","award":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"],"award-info":[{"award-number":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"]}]},{"name":"Science and Technology Development Plan of Changchun City","award":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"],"award-info":[{"award-number":["2021YFD1500103","XDA28080500","E139S311","20230508026RC","21ZGN26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The burning of straw is a very destructive process that threatens people\u2019s livelihoods and property and causes irreparable environmental damage. It is therefore essential to detect and control the burning of straw. In this study, we analyzed Sentinel-2 data to select the best separation bands based on the response characteristics of clouds, smoke, water bodies, and background (vegetation and bare soil) to the different bands. The selected bands were added to the red, green, and blue bands (RGB) as training sample data. The band that featured the highest detection accuracy, RGB_Band6, was finally selected, having an accuracy of 82.90%. The existing object detection model cannot directly handle multi-band images. This study modified the input layer structure based on the YOLOv5s model to build an object detection network suitable for multi-band remote sensing images. The Squeeze-and-Excitation (SE) network attention mechanism was introduced based on the YOLOv5s model so that the delicate features of smoke were enhanced, and the Convolution + Batch normalization + Leaky ReLU (CBL) module was replaced with the Convolution + Batch normalization + Mish (CBM) module. The accuracy of the model was improved to 75.63%, which was 1.81% better than before. We also discussed the effect of spatial resolution on model detection and where accuracies of 84.18%, 73.13%, and 45.05% for images of 60-, 20-, and 10-m resolution, respectively, were realized. The experimental results demonstrated that the accuracy of the model only sometimes improved with increasing spatial resolution. This study provides a technical reference for the monitoring of straw burning, which is vital for both the control of straw burning and ways to improve ambient air quality.<\/jats:p>","DOI":"10.3390\/rs15102641","type":"journal-article","created":{"date-parts":[[2023,5,19]],"date-time":"2023-05-19T00:55:29Z","timestamp":1684457729000},"page":"2641","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Jian","family":"Li","sequence":"first","affiliation":[{"name":"Computer Science and Technology, Faculty of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1488-9443","authenticated-orcid":false,"given":"Hua","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Faculty of Information Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Du","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weilin","family":"Yu","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Faculty of Information Technology, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4376-6498","authenticated-orcid":false,"given":"Zhi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Sun","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4408-7016","authenticated-orcid":false,"given":"Yuanhui","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Resource and Environment, Jilin Agricultural University, Changchun 130118, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.5194\/acp-11-4039-2011","article-title":"Emission Factors for Open and Domestic Biomass Burning for Use in Atmospheric Models","volume":"11","author":"Akagi","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.5194\/gmd-10-3329-2017","article-title":"Historic Global Biomass Burning Emissions for CMIP6 (BB4CMIP) Based on Merging Satellite Observations with Proxies and Fire Models (1750\u20132015)","volume":"10","author":"Kloster","year":"2017","journal-title":"Geosci. Model Dev."},{"key":"ref_3","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_4","first-page":"103","article-title":"Investigation of Straw Yield and Utilization Status and Analysis of Difficulty in Prohibition Straw Burning: A Case Study in A Township in Jiangsu Province, China","volume":"31","author":"Shi","year":"2014","journal-title":"J. Agric. Resour. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10311-017-0675-6","article-title":"Spatial and Temporal Distributions of Air Pollutant Emissions from Open Crop Straw and Biomass Burnings in China from 2002 to 2016","volume":"16","author":"Mehmood","year":"2018","journal-title":"Environ. Chem. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"114","DOI":"10.14346\/JKOSOS.2015.30.3.114","article-title":"A Study on the Verification Scheme for Electrical Circuit Analysis of Fire Hazard Analysis in Nuclear Power Plant","volume":"30","author":"Yim","year":"2015","journal-title":"J. Korean Soc. Saf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"852492","DOI":"10.3389\/fenvs.2022.852492","article-title":"Contributions of Open Biomass Burning and Crop Straw Burning to Air Quality: Current Research Paradigm and Future Outlooks","volume":"10","author":"Mehmood","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"ref_8","first-page":"49","article-title":"Analysis on the Impacts of Straw Burning on Air Quality in Beijing-Tianjing-Hebei Region","volume":"8","author":"Xiaohui","year":"2017","journal-title":"Meteorol. Environ. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1136\/ewjm.176.3.157","article-title":"Wildland Forest Fire Smoke: Health Effects and Intervention Evaluation, Hoopa, California, 1999","volume":"176","author":"Mott","year":"2002","journal-title":"West. J. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1109\/TPAMI.2007.1056","article-title":"Photo-Consistent Reconstruction of Semitransparent Scenes by Density-Sheet Decomposition","volume":"29","author":"Hasinoff","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103547","DOI":"10.1016\/j.firesaf.2022.103547","article-title":"A self-attention network for smoke detection","volume":"129","author":"Jiang","year":"2022","journal-title":"Fire Saf. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Avgeris, M., Spatharakis, D., Dechouniotis, D., Kalatzis, N., Roussaki, I., and Papavassiliou, S. (2019). Where There Is Fire There Is Smoke: A Scalable Edge Computing Framework for Early Fire Detection. Sensors, 19.","DOI":"10.3390\/s19030639"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tlig, L., Bouchouicha, M., Tlig, M., Sayadi, M., and Moreau, E. (2020). A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA. Sensors, 20.","DOI":"10.3390\/s20226429"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"46165","DOI":"10.1021\/acsami.9b16829","article-title":"Extremely Fast Self-Healable Bio-Based Supramolecular Polymer for Wearable Real-Time Sweat-Monitoring Sensor","volume":"11","author":"Yoon","year":"2019","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101656","DOI":"10.1016\/j.isci.2020.101656","article-title":"Integrating Machine Learning with Human Knowledge","volume":"23","author":"Deng","year":"2020","journal-title":"iScience"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nie, S., Zhang, Y., Wang, L., Wu, Q., and Wang, S. (2019). Preparation and Characterization of Nanocomposite Films Containing Nano-Aluminum Nitride and Cellulose Nanofibrils. Nanomaterials, 9.","DOI":"10.3390\/nano9081121"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13007-019-0416-x","article-title":"Evaluation of Grain Yield Based on Digital Images of Rice Canopy","volume":"15","author":"Liu","year":"2019","journal-title":"Plant Methods"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1016\/j.firesaf.2009.08.003","article-title":"Smoke Detection in Video Using Wavelets and Support Vector Machines","volume":"44","author":"Gubbi","year":"2009","journal-title":"Fire Saf. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1109\/ICIP.2004.1421401","article-title":"An Early Fire-Detection Method Based on Image Processing","volume":"Volume 3","author":"Chen","year":"2004","journal-title":"Proceedings of the 2004 International Conference on Image Processing (ICIP\u201904)"},{"key":"ref_20","first-page":"0772","article-title":"Image Based Smoke Detection Using Pyramid Texture and Edge Features","volume":"20","author":"Li","year":"2015","journal-title":"J. Image Graph."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1080\/01431160701236795","article-title":"Smoke Plume Detection in the Eastern United States Using MODIS","volume":"28","author":"Xie","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.3390\/rs2102347","article-title":"Dust and Smoke Detection for Multi-Channel Imagers","volume":"2","author":"Zhao","year":"2010","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/36.951076","article-title":"Automatic Detection of Fire Smoke Using Artificial Neural Networks and Threshold Approaches Applied to AVHRR Imagery","volume":"39","author":"Li","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yan, B., Fan, P., Lei, X., Liu, Z., and Yang, F. (2021). A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sens., 13.","DOI":"10.3390\/rs13091619"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","unstructured":"Misra, D. (2019). Mish: A Self Regularized Non-Monotonic Neural Activation Function. arXiv."},{"key":"ref_29","first-page":"4800","article-title":"Status and Change Characteristics of Farmland Soil Fertility in Jilin Province","volume":"48","author":"Yan","year":"2015","journal-title":"Sci. Agric. Sin."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, J., Du, J., Zhao, B., Hu, Y., Li, D., and Yu, W. (2022). Identification of Smoke from Straw Burning in Remote Sensing Images with the Improved YOLOv5s Algorithm. Atmosphere, 13.","DOI":"10.3390\/atmos13060925"},{"key":"ref_31","unstructured":"Xi, W., Sun, Y., Yu, G., and Zhang, Y. (2016). Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015, Springer."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guo, H., Xu, S., Wang, X., Shu, W., Chen, J., Pan, C., and Guo, C. (2021). Driving Mechanism of Farmers\u2019 Utilization Behaviors of Straw Resources\u2014An Empirical Study in Jilin Province, the Main Grain Producing Region in the Northeast Part of China. Sustainability, 13.","DOI":"10.3390\/su13052506"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15495","DOI":"10.1038\/s41598-021-95015-5","article-title":"Effects of Different Returning Method Combined with Decomposer on Decomposition of Organic Components of Straw and Soil Fertility","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_34","first-page":"1161","article-title":"Analysis on Effect of Straw Burning on Air Quality in Harbin","volume":"40","author":"Huo","year":"2018","journal-title":"Environ. Pollut. Control"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, J., Xie, X., and Fang, C. (2019). Temporal and Spatial Distribution Characteristics of Atmospheric Particulate Matter (PM10 and PM2.5) in Changchun and Analysis of Its Influencing Factors. Atmosphere, 10.","DOI":"10.3390\/atmos10110651"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, J., and Roy, D.P. (2017). A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sens., 9.","DOI":"10.3390\/rs9090902"},{"key":"ref_37","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A Processor for Users. Proceedings of the Proceedings Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.1093\/bioinformatics\/btn564","article-title":"SNAP: A Web-Based Tool for Identification and Annotation of Proxy SNPs Using HapMap","volume":"24","author":"Johnson","year":"2008","journal-title":"Bioinformatics"},{"key":"ref_39","first-page":"236","article-title":"Detection Method of Illegal Building Based on YOLOv5","volume":"57","author":"Juan","year":"2021","journal-title":"Comput. Eng. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ting, L., Baijun, Z., Yongsheng, Z., and Shun, Y. (2021, January 15\u201317). Ship Detection Algorithm Based on Improved YOLO V5. Proceedings of the 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China.","DOI":"10.1109\/CACRE52464.2021.9501331"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1038\/s41598-021-82704-4","article-title":"Remote Sensing Image Description Based on Word Embedding and End-to-End Deep Learning","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105812","DOI":"10.1016\/j.compag.2020.105812","article-title":"Identifying Sunflower Lodging Based on Image Fusion and Deep Semantic Segmentation with UAV Remote Sensing Imaging","volume":"179","author":"Song","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"78503","DOI":"10.1109\/ACCESS.2018.2885055","article-title":"Improving Transfer Learning and Squeeze- and-Excitation Networks for Small-Scale Fine-Grained Fish Image Classification","volume":"6","author":"Qiu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_46","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16\u201321). Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., and Wang, Y. (2016, January 12\u201314). Optimizing Intersection-over-Union in Deep Neural Networks for Image Segmentation. Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-50835-1_22"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2605140","DOI":"10.1155\/2022\/2605140","article-title":"Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement","volume":"2022","author":"Zhou","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"597","DOI":"10.3389\/fmicb.2019.00597","article-title":"Approaches to Computational Strain Design in the Multiomics Era","volume":"10","author":"John","year":"2019","journal-title":"Front. Microbiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.3390\/rs70404473","article-title":"Forest Fire Smoke Detection Using Back-Propagation Neural Network Based on MODIS Data","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"971","DOI":"10.14358\/PERS.80.10.971","article-title":"Automatic Smoke Detection in Modis Satellite Data Based on K-Means Clustering and Fisher Linear Discrimination","volume":"80","author":"Li","year":"2014","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"105405","DOI":"10.1016\/j.still.2022.105405","article-title":"Integration of Tillage Indices and Textural Features of Sentinel-2A Multispectral Images for Maize Residue Cover Estimation","volume":"221","author":"Xiang","year":"2022","journal-title":"Soil Tillage Res."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, P., Liang, H., Zheng, C., Yin, J., Tian, Y., and Cui, W. (2022). Semantic Segmentation and Analysis on Sensitive Parameters of Forest Fire Smoke Using Smoke-Unet and Landsat-8 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14010045"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bai, H., Shi, Y., Seong, M., Gao, W., and Li, Y. (2022). Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment. Remote Sens., 14.","DOI":"10.3390\/rs14122933"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"117451","DOI":"10.1016\/j.atmosenv.2020.117451","article-title":"Estimating Ground-Level PM2.5 Using Micro-Satellite Images by a Convolutional Neural Network and Random Forest Approach","volume":"230","author":"Zheng","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_57","first-page":"159","article-title":"Influence of the Varied Spatial Resolution of Remote Sensing Images on Urban and Rural Residential Information Extraction","volume":"34","author":"Wang","year":"2012","journal-title":"Resour. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","article-title":"Artificial Intelligence in Cancer Diagnosis and Prognosis: Opportunities and Challenges","volume":"471","author":"Huang","year":"2020","journal-title":"Cancer Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13007-019-0394-z","article-title":"Modeling Maize Above-Ground Biomass Based on Machine Learning Approaches Using UAV Remote-Sensing Data","volume":"15","author":"Han","year":"2019","journal-title":"Plant Methods"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"10474","DOI":"10.1021\/acs.est.5b03368","article-title":"Contribution of Brown Carbon to Direct Radiative Forcing over the Indo-Gangetic Plain","volume":"49","author":"Shamjad","year":"2015","journal-title":"Environ. Sci. Technol."},{"key":"ref_62","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep Learning in Environmental Remote Sensing: Achievements and Challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-Use Mapping Using a Deep Convolutional Neural Network with High Spatial Resolution Multispectral Remote Sensing Imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Li, Y., Zheng, C., Ma, Z., and Quan, W. (2019). Acute and Cumulative Effects of Haze Fine Particles on Mortality and the Seasonal Characteristics in Beijing, China, 2005\u20132013: A Time-Stratified Case-Crossover Study. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16132383"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"21682","DOI":"10.1007\/s11356-021-17415-4","article-title":"The Short-Term Impact of the COVID-19 Epidemic on Socioeconomic Activities in China Based on the OMI-NO2 Data","volume":"29","author":"Cao","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"6241","DOI":"10.1038\/s41598-021-85121-9","article-title":"Urban Objects Detection from C-Band Synthetic Aperture Radar (SAR) Satellite Images through Simulating Filter Properties","volume":"11","author":"Kumar","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"10088","DOI":"10.1038\/srep10088","article-title":"Mapping Paddy Rice Planting Area in Wheat-Rice Double-Cropped Areas through Integration of Landsat-8 OLI, MODIS and PALSAR images","volume":"5","author":"Wang","year":"2015","journal-title":"Sci. Rep."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2641\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:38:01Z","timestamp":1760125081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2641"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":69,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102641"],"URL":"https:\/\/doi.org\/10.3390\/rs15102641","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}