{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T03:30:30Z","timestamp":1778124630418,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801339, 41890822, 41771422, 41601406"],"award-info":[{"award-number":["41801339, 41890822, 41771422, 41601406"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["no.2018YFB2100500"],"award-info":[{"award-number":["no.2018YFB2100500"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Air temperature is one of the most essential variables in understanding global warming as well as variations of climate, hydrology, and eco-systems. However, current products and assimilation approaches alone can provide temperature data with high resolution, high spatio-temporal continuity, and high accuracy simultaneously (refer to 3H data). To explore this kind of potential, we proposed an integrated temperature downscaling framework by fusing multiple remotely sent, model-based, and in-situ datasets, which was inspired by point-surface data fusion and deep learning. First, all of the predictor variables were processed to maintain spatial seamlessness and temporal continuity. Then, a deep belief neural network was applied to downscale temperature with a spatial resolution of 1 km. To further enhance the model performance, calibration techniques were adopted by integrating station-based data. The results of the validation over the Yangtze River Basin indicated that the average Pearson correlation coefficient, RMSE, and MAE of downscaled temperature achieved 0.983, 1.96 \u00b0C, and 1.57 \u00b0C, respectively. After calibration, the RMSE and MAE were further decreased by ~20%. In general, the results and comparative analysis confirmed the effectiveness of the framework for generating 3H temperature datasets, which would be valuable for earth science studies.<\/jats:p>","DOI":"10.3390\/rs13193904","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Generating 1 km Spatially Seamless and Temporally Continuous Air Temperature Based on Deep Learning over Yangtze River Basin, China"],"prefix":"10.3390","volume":"13","author":[{"given":"Rui","family":"Li","sequence":"first","affiliation":[{"name":"Changjiang Wuhan Waterway Bureau, Wuhan 430014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tailai","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Song","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2815-3266","authenticated-orcid":false,"given":"Shuzhe","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1017-742X","authenticated-orcid":false,"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jhydrol.2009.08.005","article-title":"Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin","volume":"377","author":"Wang","year":"2009","journal-title":"J. Hydrol."},{"key":"ref_2","first-page":"128","article-title":"Evaluation of estimating daily maximum and minimum air temperature with MODIS data in east Africa","volume":"18","author":"Lin","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qin, W., Yan, H., Zou, B., Guo, R., Ci, D., Tang, Z., Zou, X., Zhang, X., Yu, X., and Wang, Y. (2021). Arbuscular mycorrhizal fungi alleviate salinity stress in peanut: Evidence from pot-grown and field experiments. Food Energy Secur., e34.","DOI":"10.1002\/fes3.314"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1038\/nclimate2563","article-title":"Elevation-dependent warming in mountain regions of the world","volume":"5","author":"Pepin","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rse.2016.10.045","article-title":"Multi-sensor integrated framework and index for agricultural drought monitoring","volume":"188","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1029\/2018GL080768","article-title":"Attribution of Global Soil Moisture Drying to Human Activities: A Quantitative Viewpoint","volume":"46","author":"Gu","year":"2019","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.1029\/2018JD029776","article-title":"Intensification and Expansion of Soil Moisture Drying in Warm Season Over Eurasia Under Global Warming","volume":"124","author":"Gu","year":"2019","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, D., Chen, N., Xiang, Z., Ma, H., and Chen, Z. (2021). Next-Generation Soil Moisture Sensor Web: High Density In-situ Observation over NB-IoT. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2021.3065077"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.compag.2014.12.009","article-title":"Integrated open geospatial web service enabled cyber-physical information infrastructure for precision agriculture monitoring","volume":"111","author":"Chen","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.2151\/jmsj.2015-001","article-title":"The JRA-55 Reanalysis: General Specifications and Basic Characteristics","volume":"93","author":"Kobayashi","year":"2015","journal-title":"J. Meteorol. Soc. Jpn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1175\/BAMS-85-3-381","article-title":"The Global Land Data Assimilation System","volume":"85","author":"Rodell","year":"2004","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1175\/BAMS-83-11-1631","article-title":"NCEP-DOE AMIP-II Reanalysis (R-2)","volume":"83","author":"Kanamitsu","year":"2002","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q.J.R. Meteorol. Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1002\/joc.5425","article-title":"Defining spatiotemporal characteristics of climate change trends from downscaled GCMs ensembles: How climate change reacts in Xinjiang, China","volume":"38","author":"Luo","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, F., Tian, D., Lowe, L., Kalin, L., and Lehrter, J. (2021). Deep Learning for Daily Precipitation and Temperature Downscaling. Water Resour. Res., 57.","DOI":"10.1029\/2020WR029308"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1002\/hyp.10125","article-title":"Snow cover and runoff modelling in a high mountain catchment with scarce data: Effects of temperature and precipitation parameters","volume":"29","author":"Zhang","year":"2015","journal-title":"Hydrol. Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1002\/2013WR014506","article-title":"The importance of observed gradients of air temperature and precipitation for modeling runoff from a glacierized watershed in the Nepalese Himalayas","volume":"50","author":"Immerzeel","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1175\/JAMC-D-19-0048.1","article-title":"Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs","volume":"59","author":"Zhang","year":"2020","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1002\/wcc.8","article-title":"State-of-the-art with regional climate model","volume":"1","author":"Rummukainen","year":"2010","journal-title":"Wiley Interdiscip. Rev. Clim. Chang."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.agrformet.2011.03.011","article-title":"Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain","volume":"151","author":"Holden","year":"2011","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s00382-017-3687-9","article-title":"Dynamically-downscaled temperature and precipitation changes over Saskatchewan using the PRECIS model","volume":"50","author":"Zhou","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00704-012-0629-7","article-title":"Near-surface air temperature retrieval from satellite images and influence by wetlands in urban region","volume":"111","author":"Hou","year":"2012","journal-title":"Theor. Appl. Climatol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"78","DOI":"10.2747\/1548-1603.43.1.78","article-title":"Statistical Estimation of Daily Maximum and Minimum Air Temperatures from MODIS LST Data over the State of Mississippi","volume":"43","author":"Mostovoy","year":"2013","journal-title":"GISci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ma, H., Zeng, J., Zhang, X., Fu, P., Zheng, D., Wigneron, J.-P., Chen, N., and Niyogi, D. (2021). Evaluation of six satellite- and model-based surface soil temperature datasets using global ground-based observations. Remote Sens. Environ., 264.","DOI":"10.1016\/j.rse.2021.112605"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111364","DOI":"10.1016\/j.rse.2019.111364","article-title":"Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution","volume":"233","author":"Long","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111692","DOI":"10.1016\/j.rse.2020.111692","article-title":"Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data","volume":"240","author":"Shen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2018.05.034","article-title":"Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States","volume":"215","author":"Li","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1029\/2018WR023354","article-title":"Downscaling SMAP Radiometer Soil Moisture over the CONUS Using an Ensemble Learning Method","volume":"55","author":"Abbaszadeh","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103601","DOI":"10.1016\/j.advwatres.2020.103601","article-title":"Generating high-resolution soil moisture by using spatial downscaling techniques: A comparison of six machine learning algorithms","volume":"141","author":"Liu","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, H., Immerzeel, W.W., Zhang, F., de Kok, R.J., Gorrie, S.J., and Ye, M. (2021). Creating 1-km long-term (1980\u20132014) daily average air temperatures over the Tibetan Plateau by integrating eight types of reanalysis and land data assimilation products downscaled with MODIS-estimated temperature lapse rates based on machine learning. Int. J. Appl. Earth Obs. Geoinf., 97.","DOI":"10.1016\/j.jag.2021.102295"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111462","DOI":"10.1016\/j.rse.2019.111462","article-title":"Estimating daily average surface air temperature using satellite land surface temperature and top-of-atmosphere radiation products over the Tibetan Plateau","volume":"234","author":"Rao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mao, Q., Peng, J., and Wang, Y. (2021). Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives. Remote Sens., 13.","DOI":"10.3390\/rs13071306"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhang, F., Jiang, H., Hu, H., Zhong, K., Jing, W., Yang, J., and Jia, B. (2020). Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging. Remote Sens., 12.","DOI":"10.3390\/rs12071082"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhao, Q., Yin, K., He, G., Zhang, Z., Wang, G., Wen, M., and Zhang, N. (2021). Spatial Downscaling of Land Surface Temperature Based on a Multi-Factor Geographically Weighted Machine Learning Model. Remote Sens., 13.","DOI":"10.3390\/rs13061186"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"147140","DOI":"10.1016\/j.scitotenv.2021.147140","article-title":"Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of Upper Indus Basin (UIB)","volume":"784","author":"Arshad","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e2019WR026444","DOI":"10.1029\/2019WR026444","article-title":"Improving Global Monthly and Daily Precipitation Estimation by Fusing Gauge Observations, Remote Sensing, and Reanalysis Data Sets","volume":"56","author":"Xu","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8558","DOI":"10.1029\/2018WR022643","article-title":"A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists","volume":"54","author":"Shen","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"144244","DOI":"10.1016\/j.scitotenv.2020.144244","article-title":"Changes in precipitation extremes in the Yangtze River Basin during 1960\u20132019 and the association with global warming, ENSO, and local effects","volume":"760","author":"Li","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.quaint.2017.01.017","article-title":"Assessing changes of river discharge under global warming of 1.5 \u00b0C and 2 \u00b0C in the upper reaches of the Yangtze River Basin: Approach by using multiple-GCMs and hydrological models","volume":"453","author":"Chen","year":"2017","journal-title":"Quat. Int."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1080\/02723646.1992.10642451","article-title":"Surface-Air Temperature Relationships in the Urban Environment of Phoenix, Arizona","volume":"13","author":"Stoll","year":"2013","journal-title":"Phys. Geogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1080\/01431161.2012.659354","article-title":"Comparing night-time satellite land surface temperature from MODIS and ground measured air temperature across a conurbation","volume":"3","author":"Tomlinson","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1109\/TGRS.2019.2892417","article-title":"A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1109\/TGRS.2017.2698828","article-title":"A Thermal Sampling Depth Correction Method for Land Surface Temperature Estimation from Satellite Passive Microwave Observation Over Barren Land","volume":"55","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"112437","DOI":"10.1016\/j.rse.2021.112437","article-title":"A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature","volume":"260","author":"Zhang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_47","first-page":"725","article-title":"Reconstruction of NDVI time-series datasets of MODIS based on Savitzky-Golay filter","volume":"14","author":"Jinhu","year":"2010","journal-title":"J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1002\/2017GL075710","article-title":"Estimating Ground-Level PM2.5 by Fusing Satellite and Station Observations: A Geo-Intelligent Deep Learning Approach","volume":"44","author":"Li","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shen, H., Li, T., Yuan, Q., and Zhang, L. (2018). Estimating Regional Ground-Level PM2.5 Directly from Satellite Top-Of-Atmosphere Reflectance Using Deep Belief Networks. J. Geophys. Res. Atmos., 123.","DOI":"10.1029\/2018JD028759"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TGRS.2011.2120615","article-title":"Downscaling SMOS-Derived Soil Moisture Using MODIS Visible\/Infrared Data","volume":"49","author":"Piles","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2603","DOI":"10.1080\/01431161.2011.617397","article-title":"Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin","volume":"33","author":"Cheema","year":"2011","journal-title":"Int. J. Remote. Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.12.002","article-title":"First results from Version 7 TRMM 3B43 precipitation product in combination with a new downscaling-calibration procedure","volume":"131","author":"Duan","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"124664","DOI":"10.1016\/j.jhydrol.2020.124664","article-title":"A spatiotemporal deep fusion model for merging satellite and gauge precipitation in China","volume":"584","author":"Wu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.atmosenv.2017.01.004","article-title":"Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment","volume":"152","author":"Li","year":"2016","journal-title":"Atmos. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","article-title":"AutoML: A survey of the state-of-the-art","volume":"212","author":"He","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5185","DOI":"10.1109\/TGRS.2016.2558109","article-title":"Reconstruction of GF-1 Soil Moisture Observation Based on Satellite and In Situ Sensor Collaboration Under Full Cloud Contamination","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"112248","DOI":"10.1016\/j.rse.2020.112248","article-title":"In-situ and triple-collocation based evaluations of eight global root zone soil moisture products","volume":"254","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3904\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:07:26Z","timestamp":1760166446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,29]]},"references-count":60,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193904"],"URL":"https:\/\/doi.org\/10.3390\/rs13193904","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,29]]}}}