{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:31:01Z","timestamp":1777390261928,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T00:00:00Z","timestamp":1684713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Watershed Earth Surface Processes and Ecological Security, Zhejiang Normal University","award":["KF-2022-26"],"award-info":[{"award-number":["KF-2022-26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models\u2019 application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472.<\/jats:p>","DOI":"10.3390\/rs15102684","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T05:04:44Z","timestamp":1684731884000},"page":"2684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters"],"prefix":"10.3390","volume":"15","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4189-7366","authenticated-orcid":false,"given":"Xingwen","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Sun","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguang","family":"Wen","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academic of Sciences and University of Chinese Academic of Sciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4602-502X","authenticated-orcid":false,"given":"Xiaodan","family":"Wu","sequence":"additional","affiliation":[{"name":"The College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongqin","family":"You","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academic of Sciences and University of Chinese Academic of Sciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Cheng","sequence":"additional","affiliation":[{"name":"The Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5663-3267","authenticated-orcid":false,"given":"Zhenzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaofan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kechen","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaxue","family":"Jian","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/S0065-2687(08)60176-4","article-title":"Land Surface Processes and Climate\u2014Surface Albedos and Energy Balance","volume":"Volume 25","author":"Dickinson","year":"1983","journal-title":"Advances in Geophysics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19336","DOI":"10.1073\/pnas.0810021105","article-title":"Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks","volume":"105","author":"Ollinger","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1038\/nclimate1590","article-title":"Inhibition of the positive snow-albedo feedback by precipitation in interior Antarctica","volume":"2","author":"Picard","year":"2012","journal-title":"Nat. Clim. Change"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6218","DOI":"10.1002\/2017GL073661","article-title":"How robust are in situ observations for validating satellite-derived albedo over the dark zone of the Greenland Ice Sheet?","volume":"44","author":"Ryan","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1038\/326655a0","article-title":"Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate","volume":"326","author":"Charlson","year":"1987","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.rse.2011.10.002","article-title":"Evaluation of Moderate-resolution Imaging Spectroradiometer (MODIS) snow albedo product (MCD43A) over tundra","volume":"117","author":"Wang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"D17","article-title":"Evaluation of Moderate Resolution Imaging Spectroradiometer land surface visible and shortwave albedo products at FLUXNET sites","volume":"115","author":"Wang","year":"2010","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2016.06.013","article-title":"Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: A case of MODIS albedo products preliminary validation over northern China","volume":"184","author":"Wu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, X., Wen, J., Xiao, Q., You, D., Dou, B., Lin, X., and Hueni, A. (2018). Accuracy assessment on MODIS (V006), GLASS and MuSyQ land-surface albedo products: A case study in the Heihe River Basin, China. Remote Sens., 10.","DOI":"10.3390\/rs10122045"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1080\/17538947.2013.804601","article-title":"Preliminary evaluation of the long-term GLASS albedo product","volume":"6","author":"Liu","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2017.10.031","article-title":"Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach","volume":"204","author":"He","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9757","DOI":"10.1073\/pnas.1317323111","article-title":"Preferential cooling of hot extremes from cropland albedo management","volume":"111","author":"Davin","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1029\/2008GL033567","article-title":"Radiative forcing over the conterminous United States due to contemporary land cover land use albedo change","volume":"35","author":"Barnes","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.rse.2017.09.020","article-title":"Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record","volume":"201","author":"Liu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.rse.2015.09.021","article-title":"The MODIS (collection V006) BRDF\/albedo product MCD43D: Temporal course evaluated over agricultural landscape","volume":"170","author":"Mira","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1038\/s41561-022-00953-y","article-title":"High Mountain Asia hydropower systems threatened by climate-driven landscape instability","volume":"15","author":"Li","year":"2022","journal-title":"Nat. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2018.08.025","article-title":"Preliminary assessment of 20-m surface albedo retrievals from sentinel-2A surface reflectance and MODIS\/VIIRS surface anisotropy measures","volume":"217","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"6784","DOI":"10.3390\/rs70606784","article-title":"Development of a high resolution BRDF\/Albedo product by fusing airborne CASI reflectance with MODIS daily reflectance in the oasis area of the Heihe River Basin, China","volume":"7","author":"You","year":"2015","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/TGRS.2019.2946598","article-title":"Development of the direct-estimation albedo algorithm for snow-free Landsat TM albedo retrievals using field flux measurements","volume":"58","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1109\/LGRS.2020.2967085","article-title":"Albedo retrieval from Sentinel-2 by new narrow-to-broadband conversion coefficients","volume":"17","author":"Bonafoni","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(00)00205-4","article-title":"Narrowband to broadband conversions of land surface albedo I: Algorithms","volume":"76","author":"Liang","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2014.07.009","article-title":"An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge","volume":"152","author":"Shuai","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1016\/j.rse.2011.04.019","article-title":"An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF","volume":"115","author":"Shuai","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cao, C., Lee, X., Muhlhausen, J., Bonneau, L., and Xu, J. (2018). Measuring landscape albedo using unmanned aerial vehicles. Remote Sens., 10.","DOI":"10.3390\/rs10111812"},{"key":"ref_26","unstructured":"Lewis, P., and Barnsley, M. (1994, January 17\u201321). Influence of the sky radiance distribution on various formulations of the earth surface albedo. Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, ISPRS, Val D\u2019Isere, France."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2022.09.016","article-title":"Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine","volume":"194","author":"Lin","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","first-page":"1","article-title":"Sloping surface reflectance: The best option for satellite-based albedo retrieval over mountainous areas","volume":"19","author":"Lin","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"2415","DOI":"10.1175\/1520-0477(2001)082<2415:FANTTS>2.3.CO;2","article-title":"FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities","volume":"82","author":"Baldocchi","year":"2001","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108350","DOI":"10.1016\/j.agrformet.2021.108350","article-title":"Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites","volume":"301","author":"Chu","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2017.06.019","article-title":"Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance","volume":"199","author":"Roy","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","unstructured":"Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., and Fritz, S. (2022, October 13). ESA WorldCover 10 m 2021 v200. Available online: https:\/\/pure.iiasa.ac.at\/id\/eprint\/18478\/."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0034-4257(02)00091-3","article-title":"First operational BRDF, albedo nadir reflectance products from MODIS","volume":"83","author":"Schaaf","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TGRS.2013.2245670","article-title":"Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data","volume":"52","author":"Qu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0034-4257(00)00125-5","article-title":"A comparison of satellite-derived spectral albedos to ground-based broadband albedo measurements modeled to satellite spatial scale for a semidesert landscape","volume":"74","author":"Lucht","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1109\/36.841980","article-title":"An algorithm for the retrieval of albedo from space using semiempirical BRDF models","volume":"38","author":"Lucht","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","first-page":"42","article-title":"MODIS BRDF\/albedo product: Algorithm theoretical basis document version 5.0","volume":"23","author":"Strahler","year":"1999","journal-title":"MODIS Doc."},{"key":"ref_39","first-page":"254","article-title":"Spectral Database System of Typical Objects in China","volume":"1","author":"Wang","year":"2009","journal-title":"Beijing Sci. Press"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.agsy.2005.11.003","article-title":"Quantitative Remote Sensing of Land Surfaces","volume":"Volume 90","author":"Liang","year":"2006","journal-title":"Agricultural Systems"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Clark, R.N., Swayze, G.A., Wise, R.A., Livo, K.E., Hoefen, T.M., Kokaly, R.F., and Sutley, S.J. (2007). USGS Digital Spectral Library splib06a, 2327-638X.","DOI":"10.3133\/ds231"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0034-4257(02)00092-5","article-title":"Validating MODIS land surface reflectance and albedo products: Methods and preliminary results","volume":"83","author":"Liang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Amazirh, A., Bouras, E.H., Olivera-Guerra, L.E., Er-Raki, S., and Chehbouni, A. (2021). Retrieving crop albedo based on radar sentinel-1 and random forest approach. Remote Sens., 13.","DOI":"10.3390\/rs13163181"},{"key":"ref_44","first-page":"1","article-title":"Upscaling in situ site-based albedo using machine learning models: Main controlling factors on results","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sarafanov, M., Kazakov, E., Nikitin, N.O., and Kalyuzhnaya, A.V. (2020). A machine learning approach for remote sensing data gap-filling with open-source implementation: An example regarding land surface temperature, surface albedo and NDVI. Remote Sens., 12.","DOI":"10.3390\/rs12233865"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tariq, A., Yan, J., Gagnon, A.S., Riaz Khan, M., and Mumtaz, F. (2022). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spat. Inf. Sci., 1\u201319.","DOI":"10.1080\/10095020.2022.2100287"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4818","DOI":"10.1109\/JSTARS.2014.2337273","article-title":"A remote sensing-based approach for debris-flow susceptibility assessment using artificial neural networks and logistic regression modeling","volume":"7","author":"Elkadiri","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2016.02.001","article-title":"A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data","volume":"176","author":"Chirici","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"109067","DOI":"10.1016\/j.asoc.2022.109067","article-title":"A neural network boosting regression model based on XGBoost","volume":"125","author":"Dong","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"493","DOI":"10.5194\/essd-11-493-2019","article-title":"Theia Snow collection: High-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data","volume":"11","author":"Gascoin","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2018.11.037","article-title":"Watershed-scale mapping of fractional snow cover under conifer forest canopy using lidar","volume":"222","author":"Kostadinov","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"7159","DOI":"10.1109\/JSTARS.2021.3089655","article-title":"Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes","volume":"14","author":"Muhuri","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"567","DOI":"10.5194\/tc-17-567-2023","article-title":"Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets","volume":"17","author":"Stillinger","year":"2023","journal-title":"Cryosphere"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","article-title":"ML-KNN: A lazy learning approach to multi-label learning","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_56","first-page":"327","article-title":"Introduction to artificial neural network (ANN) methods: What they are and how to use them","volume":"41","author":"Zupan","year":"1994","journal-title":"Acta Chim. Slov."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Friedjungov\u00e1, M., Ji\u0159ina, M., and Va\u0161ata, D. (2019, January 18\u201320). Missing features reconstruction and its impact on classification accuracy. Proceedings of the International Conference on Computational Science, Faro, Portugal.","DOI":"10.1007\/978-3-030-22744-9_16"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, C., Meadows, M.E., Zhang, Z., Lin, X., Zhang, Z., Chi, Y., Feng, M., Li, E., and Hu, Y. (2021). Factors underlying spatiotemporal variations in atmospheric pm2. 5 concentrations in zhejiang province, china. Remote Sens., 13.","DOI":"10.3390\/rs13153011"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2684\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:39:48Z","timestamp":1760125188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2684"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":58,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102684"],"URL":"https:\/\/doi.org\/10.3390\/rs15102684","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,22]]}}}