{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:54:54Z","timestamp":1784908494831,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T00:00:00Z","timestamp":1602374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"research program of the Chinese Academy of Science (CAS)","award":["XDA19030303"],"award-info":[{"award-number":["XDA19030303"]}]},{"name":"national natural science foundation project of china","award":["41631180, 41701432, 41571373"],"award-info":[{"award-number":["41631180, 41701432, 41571373"]}]},{"DOI":"10.13039\/501100012166","name":"national key research and development program of China","doi-asserted-by":"publisher","award":["2016YFA0600103, 2016YFC0500201-06"],"award-info":[{"award-number":["2016YFA0600103, 2016YFC0500201-06"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics.<\/jats:p>","DOI":"10.3390\/rs12203301","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"3301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1214-2359","authenticated-orcid":false,"given":"Amin","family":"Naboureh","sequence":"first","affiliation":[{"name":"Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ainong","family":"Li","sequence":"additional","affiliation":[{"name":"Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1472-5259","authenticated-orcid":false,"given":"Jinhu","family":"Bian","sequence":"additional","affiliation":[{"name":"Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7333-4325","authenticated-orcid":false,"given":"Guangbin","family":"Lei","sequence":"additional","affiliation":[{"name":"Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"Wood Environment &amp; Infrastructure Solutions, Ottawa, ON K2E 7K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"iii","DOI":"10.1111\/j.1931-0846.2002.tb00001.x","article-title":"Mountain geography in 2002: The international year of mountains","volume":"92","author":"Friend","year":"2002","journal-title":"Geogr. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2020.02.011","article-title":"Global high-resolution mountain green cover index mapping based on landsat images and google earth engine","volume":"162","author":"Bian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chu, D. (2020). Remote Sensing of Land Use and Land Cover in Mountain Region, Springer.","DOI":"10.1007\/978-981-13-7580-4"},{"key":"ref_4","unstructured":"Adepoju, K., and Adelabu, S. (2018, January 1\u20135). Improved landsat-8 OLI and sentinel-2 MSI classification in mountainous terrain using machine learning on google earth engine. Proceedings of the Biennial Conference of the Society of South African Geographers, Bloemfontein, South Africa."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Valizadeh Kamran, K., Blaschke, T., Aryal, J., Naboureh, A., Einali, J., and Bian, J. (2019). Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches. Fire, 2.","DOI":"10.3390\/fire2030043"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Moharrami, M., Naboureh, A., Gudiyangada Nachappa, T., Ghorbanzadeh, O., Guan, X., and Blaschke, T. (2020). National-scale landslide susceptibility mapping in Austria using fuzzy best-worst multi-criteria decision-making. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9060393"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., and Aryal, J. (2019). Forest fire susceptibility and risk mapping using social\/infrastructural vulnerability and environmental variables. Fire, 2.","DOI":"10.3390\/fire2030050"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1080\/07038992.2017.1346468","article-title":"Wetland classification using multi-source and multi-temporal optical remote sensing data in newfoundland and Labrador, Canada","volume":"43","author":"Amani","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lei, G., Li, A., Bian, J., Zhang, Z., Jin, H., Xi, N., Wei, Z., Wang, J., Cao, X., and Tan, J. (2016). Land cover mapping in southwestern china using the HC-MMK approach. Remote Sens., 8.","DOI":"10.3390\/rs8040305"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1080\/07038992.2017.1342206","article-title":"Object-based classification of wetlands in Newfoundland and Labrador using multi-temporal PolSAR data","volume":"43","author":"Mahdavi","year":"2017","journal-title":"Can. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Jeangros, N., Hering, A.S., Kaiser, T., and McCray, J.E. (2017). ScaMF\u2013RM: A fused high-resolution land cover product of the Rocky Mountains. Remote Sens., 9.","DOI":"10.3390\/rs9101015"},{"key":"ref_12","first-page":"49","article-title":"Snow cover mapping for mountainous areas by fusion of MODIS L1B and geographic data based on stacked denoising auto-encoders","volume":"57","author":"Kan","year":"2018","journal-title":"Comput. Mater. Contin."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, C., Huang, X., Li, X., and Liang, T. (2020). MODIS fractional snow cover mapping using machine learning technology in a mountainous area. Remote Sens., 12.","DOI":"10.3390\/rs12060962"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lei, G., Li, A., Bian, J., Yan, H., Zhang, L., Zhang, Z., and Nan, X. (2020). OIC-MCE: A practical land cover mapping approach for limited samples based on multiple classifier ensemble and iterative classification. Remote Sens., 12.","DOI":"10.3390\/rs12060987"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"014530","DOI":"10.1117\/1.JRS.13.014530","article-title":"Land-use and land-cover classification using sentinel-2 data and machine-learning algorithms: Operational method and its implementation for a mountainous area of Nepal","volume":"13","author":"Delalay","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2015.03.014","article-title":"Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin","volume":"105","author":"Mellor","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","first-page":"277","article-title":"Synergy of sampling techniques and ensemble classifiers for classification of urban environments using full-waveform LidAR data","volume":"73","author":"Azadbakht","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1109\/JSTARS.2019.2922297","article-title":"Dynamic synthetic minority over-sampling technique-based rotation forest for the classification of imbalanced hyperspectral data","volume":"12","author":"Feng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X.-Y., and Zhou, Z.-H. (2006, January 18\u201322). The influence of class imbalance on cost-sensitive learning: An empirical study. Proceedings of the Sixth International Conference on Data Mining (ICDM\u201906), Hong Kong, China.","DOI":"10.1109\/ICDM.2006.158"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Smote: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","volume":"29","author":"Khan","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chawla, N.V. (2009). Data mining for imbalanced datasets: An overview. Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/978-0-387-09823-4_45"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111375","DOI":"10.1016\/j.rse.2019.111375","article-title":"Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods","volume":"233","author":"Waldner","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Feng, W., Boukir, S., and Huang, W. (August, January 28). Margin-based random forest for imbalanced land cover classification. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898652"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Douzas, G., Bacao, F., Fonseca, J., and Khudinyan, M. (2019). Imbalanced learning in land cover classification: Improving minority classes\u2019 prediction accuracy using the geometric smote algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11243040"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bogner, C., Seo, B., Rohner, D., and Reineking, B. (2018). Classification of rare land cover types: Distinguishing annual and perennial crops in an agricultural catchment in South Korea. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0190476"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111354","DOI":"10.1016\/j.rse.2019.111354","article-title":"Auxiliary datasets improve accuracy of object-based land use\/land cover classification in heterogeneous savanna landscapes","volume":"233","author":"Hurskainen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xie, S., Liu, L., Zhang, X., Yang, J., Chen, X., and Gao, Y. (2019). Automatic land-cover mapping using Landsat time-series data based on google earth engine. Remote Sens., 11.","DOI":"10.3390\/rs11243023"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2015.09.004","article-title":"Regional detection, characterization, and attribution of annual forest change from 1984 to 2012 using Landsat-derived time-series metrics","volume":"170","author":"Hermosilla","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Eskandari, S., Reza Jaafari, M., Oliva, P., Ghorbanzadeh, O., and Blaschke, T. (2020). Mapping land cover and tree canopy cover in Zagros forests of Iran: Application of sentinel-2, google earth, and field data. Remote Sens., 12.","DOI":"10.3390\/rs12121912"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/20964471.2019.1690404","article-title":"A generalized supervised classification scheme to produce provincial wetland inventory maps: An application of google earth engine for big geo data processing","volume":"3","author":"Amani","year":"2019","journal-title":"Big Earth Data"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., and Hopkinson, C. (2019). Canadian wetland inventory using google earth engine: The first map and preliminary results. Remote Sens., 11.","DOI":"10.3390\/rs11070842"},{"key":"ref_36","first-page":"419","article-title":"Koppen-Geiger climate classification of Iran and investigation of its changes during 20th century","volume":"43","author":"Raziei","year":"2017","journal-title":"J. Earth Space Phys."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.ins.2019.06.007","article-title":"Geometric smote a geometrically enhanced drop-in replacement for smote","volume":"501","author":"Douzas","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using sentinel imagery within google earth engine and a novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s12517-017-3012-2","article-title":"An integrated object-based image analysis and CA-Markov model approach for modeling land use\/land cover trends in the Sarab plain","volume":"10","author":"Naboureh","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from tm imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, X., Zhao, S., Qin, X., Zhao, N., and Liang, L. (2017). Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sens., 9.","DOI":"10.3390\/rs9060596"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the normalized difference water index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1016\/j.jaridenv.2010.03.012","article-title":"Standardized FAO-LCCS land cover mapping in heterogeneous tree savannas of West Africa","volume":"74","author":"Cord","year":"2010","journal-title":"J. Arid Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431160903252327","article-title":"Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic","volume":"1","author":"Ghimire","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_48","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_49","first-page":"170","article-title":"Effects of pre-processing methods on Landsat oli-8 land cover classification using obia and random forests classifier","volume":"73","author":"Phiri","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_50","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_51","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_52","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/MCI.2018.2866730","article-title":"Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches [research frontier]","volume":"13","author":"Santos","year":"2018","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in google earth engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Carrasco, L., O\u2019Neil, A.W., Morton, R.D., and Rowland, C.S. (2019). Evaluating combinations of temporally aggregated sentinel-1, sentinel-2 and Landsat 8 for land cover mapping with google earth engine. Remote Sens., 11.","DOI":"10.3390\/rs11030288"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/LGRS.2019.2917788","article-title":"Toward spatio\u2013spectral analysis of sentinel-2 time series data for land cover mapping","volume":"17","author":"Gbodjo","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2020). Dimensionality reduction and feature selection for object-based land cover classification based on sentinel-1 and sentinel-2 time series using google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Stow, D., Chen, H.L., Lewison, R., An, L., and Shi, L. (2018). Mapping vegetation and land use types in Fanjingshan national nature reserve using google earth engine. Remote Sens., 10.","DOI":"10.3390\/rs10060927"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.isprsjprs.2016.11.004","article-title":"Optimizing selection of training and auxiliary data for operational land cover classification for the lcmap initiative","volume":"122","author":"Zhu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","unstructured":"Choi, J.M. (2010). A selective sampling method for imbalanced data learning on support vector machines. Grad. Theses Diss."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Johnson, J.M., and Khoshgoftaar, T.M. (August, January 30). Deep learning and data sampling with imbalanced big data. Proceedings of the 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA.","DOI":"10.1109\/IRI.2019.00038"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:19:15Z","timestamp":1760177955000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/20\/3301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,11]]},"references-count":61,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12203301"],"URL":"https:\/\/doi.org\/10.3390\/rs12203301","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,11]]}}}