{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:25:02Z","timestamp":1780511102505,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0503905"],"award-info":[{"award-number":["2017YFB0503905"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1505205"],"award-info":[{"award-number":["2018YFC1505205"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871241"],"award-info":[{"award-number":["41871241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"publisher","award":["ZYGX2019J069"],"award-info":[{"award-number":["ZYGX2019J069"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25\u00b0. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1\u00b0, respectively. MODIS LST played the role of \u201cground truth\u201d in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1\u20130.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19\u20133.58 K for the daytime and 1.43\u20132.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10\u20134.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA\u2019s GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA\u2019s global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.<\/jats:p>","DOI":"10.3390\/rs12172691","type":"journal-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T09:35:31Z","timestamp":1597916131000},"page":"2691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network"],"prefix":"10.3390","volume":"12","author":[{"given":"Shaofei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9926-7693","authenticated-orcid":false,"given":"Ji","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianjie","family":"Lei","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research (IWHR), Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-8422","authenticated-orcid":false,"given":"Hua","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailing","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.5194\/hess-6-85-2002","article-title":"The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes","volume":"6","author":"Su","year":"2002","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2012.12.008","article-title":"Satellite-derived land surface temperature: Current status and perspectives","volume":"131","author":"Li","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"5952","DOI":"10.1109\/TGRS.2013.2294031","article-title":"Disaggregation of Remotely Sensed Land Surface Temperature: A Generalized Paradigm","volume":"52","author":"Chen","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A generalized split-window algorithm for retrieving land-surface temperature from space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","unstructured":"Gillespie, A.R., Rokugawa, S., Hook, S.J., Matsunaga, T., and Kahle, A.B. (1999). Temperature\/Emissivity Separation Algorithm Theoretical Basis Document, Version 2.4, NASA. ATBD Contract NAS5-31372."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1080\/01431160010006971","article-title":"A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region","volume":"22","author":"Qin","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Martin, M., Ghent, D., Pires, A., G\u00f6ttsche, F.M., Cermak, J., and Remedios, J. (2019). Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years. Remote Sens., 11.","DOI":"10.3390\/rs11050479"},{"key":"ref_9","first-page":"140","article-title":"A simple retrieval method of land surface temperature from AMSR-E passive microwave data\u2014A case study over Southern China during the strong snow disaster of 2008","volume":"13","author":"Chen","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5699","DOI":"10.1002\/2015JD024402","article-title":"Toward \u201call weather,\u201d long record, and real-time land surface temperature retrievals from microwave satellite observations: Microwave land surface temperature","volume":"121","author":"Prigent","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Martins, J.P.A., Trigo, I.F., Ghilain, N., Jimenez, C., G\u00f6ttsche, F.M., Ermida, S.L., Olesen, F.S., Gellens-Meulenberghs, F., and Arboleda, A. (2019). An All-Weather Land Surface Temperature Product Based on MSG\/SEVIRI Observations. Remote Sens., 11.","DOI":"10.20944\/preprints201911.0238.v1"},{"key":"ref_12","first-page":"35","article-title":"Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass","volume":"34","author":"Zhou","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1080\/01431161.2018.1508920","article-title":"A physically based algorithm for retrieving land surface temperature under cloudy conditions from AMSR2 passive microwave measurements","volume":"40","author":"Huang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/36.58971","article-title":"Land surface temperature derived from the SSM\/I passive microwave brightness temperatures","volume":"28","author":"McFarland","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"D04113","article-title":"Land surface temperature from Ka band (37 GHz) passive microwave observations","volume":"114","author":"Holmes","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/S0034-4257(03)00011-7","article-title":"A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas","volume":"85","author":"Fily","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/TGRS.2007.906478","article-title":"A Practical Method for Retrieving Land Surface Temperature from AMSR-E Over the Amazon Forest","volume":"46","author":"Gao","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Royer, A., and Poirier, S. (2010). Surface temperature spatial and temporal variations in North America from homogenized satellite SMMR-SSM\/I microwave measurements and reanalysis for 1979\u20132008. J. Geophys. Res. Atmos., 115.","DOI":"10.1029\/2009JD012760"},{"key":"ref_19","first-page":"D08116","article-title":"A physically based statistical methodology for surface soil moisture retrieval in the Tibet Plateau using microwave vegetation indices","volume":"116","author":"Zhao","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.028","article-title":"Land surface temperature retrieval over circumpolar Arctic using SSM\/I\u2013SSMIS and MODIS data","volume":"162","author":"Royer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8839","DOI":"10.1029\/98JD00275","article-title":"Physical retrieval of land surface temperature using the special sensor microwave imager","volume":"103","author":"Weng","year":"1998","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1002\/qj.803","article-title":"A Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction","volume":"137","author":"Aires","year":"2011","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"D17105","DOI":"10.1029\/2010JD015431","article-title":"Subsurface emission effects in AMSR-E measurements: Implications for land surface microwave emissivity retrieval","volume":"116","author":"Galantowicz","year":"2011","journal-title":"J. Geophys. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12147","DOI":"10.1029\/1999JD900153","article-title":"Microwave radiometric signatures of different surface types in deserts","volume":"104","author":"Prigent","year":"1999","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14887","DOI":"10.1029\/2001JD900085","article-title":"A new neural network approach including first guess for retrieval of atmospheric water vapor, cloud liquid water path, surface temperature, and emissivities over land from satellite microwave observations","volume":"106","author":"Aires","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3330","DOI":"10.1002\/2016JD026144","article-title":"Inversion of AMSR-E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature: AMSR-E LAND SURFACE TEMPERATURE","volume":"122","author":"Prigent","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3348","DOI":"10.1002\/2016JD026148","article-title":"Inversion of AMSR-E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature: AMSR-E LAND SURFACE TEMPERATURE","volume":"122","author":"Ermida","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_28","unstructured":"Moncet, J.L., Liang, P., Galantowicz, J.F., Lipton, A.E., Uymin, G., Prigent, C., and Grassotti, C. (2019, July 01). Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control. Available online: https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/abs\/10.1029\/2010JD015429."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6777","DOI":"10.1029\/2018JD028422","article-title":"Intercomparison of Six Upscaling Evapotranspiration Methods: From Site to the Satellite Pixel","volume":"123","author":"Li","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Ge, L., Hang, R., Liu, Y., and Liu, Q. (2018). Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations. Remote Sens., 10.","DOI":"10.3390\/rs10091327"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tan, J., NourEldeen, N., Mao, K., Shi, J., Li, Z., Xu, T., and Yuan, Z. (2019). Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China. Sensors, 19.","DOI":"10.3390\/s19132987"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1175\/JHM-D-19-0110.1","article-title":"PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks\u2013Convolutional Neural Networks","volume":"20","author":"Sadeghi","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1109\/LGRS.2016.2581140","article-title":"Global Land Surface Emissivity Estimation from AMSR2 Observations","volume":"13","author":"Prakash","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1175\/JAMC-D-17-0213.1","article-title":"Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations","volume":"57","author":"Prakash","year":"2018","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1109\/JSTARS.2018.2870130","article-title":"The GLASS Land Surface Temperature Product","volume":"12","author":"Zhou","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.rse.2013.08.027","article-title":"New refinements and validation of the collection-6 MODIS land-surface temperature\/emissivity product","volume":"140","author":"Wan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_40","unstructured":"Dong, W. (2020, May 13). EOL Data Archive\u2014CAMP: Tongyu (Inner Mongolia) Surface Meteorology and Radiation Data Set. Available online: https:\/\/data.eol.ucar.edu\/dataset\/76.141."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jhydrol.2013.02.025","article-title":"Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China","volume":"487","author":"Liu","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.5194\/hess-15-1291-2011","article-title":"A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem","volume":"15","author":"Liu","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe Watershed Allied Telemetry Experimental Research (Hiwater)","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, X., Xu, Z., Che, T., Xiao, Q., Ma, M., Liu, Q., Jin, R., Guo, J., and Wang, L. (2018). The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China. Vadose Zone J., 17.","DOI":"10.2136\/vzj2018.04.0072"},{"key":"ref_45","first-page":"102136","article-title":"Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data","volume":"91","author":"Yang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"13140","DOI":"10.1002\/2013JD020260","article-title":"Intercomparison of surface energy flux measurement systems used during the HiWATER-MUSOEXE","volume":"118","author":"Xu","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Liang, S.L. (2004). Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons.","DOI":"10.1002\/047172372X"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/87.974338","article-title":"Outliers in process modeling and identification","volume":"10","author":"Pearson","year":"2002","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"G\u00f6ttsche, F.M., Olesen, F.S., Trigo, I.F., Bork-Unkelbach, A., and Martin, M.A. (2016). Long term validation of land surface temperature retrieved from MSG\/SEVIRI with continuous in-situ measurements in Africa. Remote Sens., 8.","DOI":"10.3390\/rs8050410"},{"key":"ref_50","unstructured":"Du, J., Jones, L.A., and Kimball, J.S. (2017). Daily Global Land Surface Parameters Derived from AMSR-E and AMSR2, Version 2, NASA National Snow and Ice Data Center Distributed Active Archive Center."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"791","DOI":"10.5194\/essd-9-791-2017","article-title":"A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations","volume":"9","author":"Du","year":"2017","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/JSTARS.2010.2041530","article-title":"Satellite Microwave Remote Sensing of Daily Land Surface Air Temperature Minima and Maxima From AMSR-E","volume":"3","author":"Jones","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1109\/TGRS.2007.898436","article-title":"Satellite Microwave Remote Sensing of Boreal and Arctic Soil Temperatures From AMSR-E","volume":"45","author":"Jones","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2015). Striving for Simplicity: The All Convolutional Net. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.rse.2018.03.011","article-title":"Impact of surface roughness, vegetation opacity and soil permittivity on L-band microwave emission and soil moisture retrieval in the third pole environment","volume":"209","author":"Zheng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.12.004","article-title":"Remotely sensed soil temperatures beneath snow-free skin-surface using thermal observations from tandem polar-orbiting satellites: An analytical three-time-scale model","volume":"143","author":"Zhan","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1016\/j.rse.2009.03.014","article-title":"Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority","volume":"113","author":"Foody","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2015.08.018","article-title":"Using multiple remote sensing perspectives to identify and attribute land surface dynamics in Central Asia 2001\u20132013","volume":"170","author":"Henebry","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1080\/17538947.2018.1452300","article-title":"Parameterization of the freeze\/thaw discriminant function algorithm using dense in-situ observation network data","volume":"12","author":"Wang","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1080\/01431161.2011.577836","article-title":"Microwave emission of soil freezing and thawing observed by a truck-mounted microwave radiometer","volume":"33","author":"Zhao","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","first-page":"D19102","article-title":"Snow characterization at a global scale with passive microwave satellite observations","volume":"111","author":"Cordisco","year":"2006","journal-title":"J. Geophys. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.rse.2016.04.001","article-title":"Representativeness errors of point-scale ground-based solar radiation measurements in the validation of remote sensing products","volume":"181","author":"Huang","year":"2016","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2691\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:03:49Z","timestamp":1760177029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2691"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":63,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12172691"],"URL":"https:\/\/doi.org\/10.3390\/rs12172691","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,20]]}}}