{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T12:51:58Z","timestamp":1770814318345,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,8]],"date-time":"2017-06-08T00:00:00Z","timestamp":1496880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Basic Research Program of China","award":["2013CB733406"],"award-info":[{"award-number":["2013CB733406"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41531174"],"award-info":[{"award-number":["41531174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Foundation of State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["2017-FX-04"],"award-info":[{"award-number":["2017-FX-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R \u2265 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias \u2264 0.02) and root mean square error values (RMSE \u2264 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.<\/jats:p>","DOI":"10.3390\/rs9060574","type":"journal-article","created":{"date-parts":[[2017,6,8]],"date-time":"2017-06-08T10:26:09Z","timestamp":1496917569000},"page":"574","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0287-5826","authenticated-orcid":false,"given":"Zheng","family":"Lu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology and School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8295-8973","authenticated-orcid":false,"given":"Linna","family":"Chai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology and School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaomin","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology and School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanghua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9847-9034","authenticated-orcid":false,"given":"Lingmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science and Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Jin","sequence":"additional","affiliation":[{"name":"Heihe Remote Sensing Experimental Research Station, Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology and School of Natural Resources, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/36.739125","article-title":"Retrieval of land surface parameters using passive microwave measurements at 6\u201318 GHz","volume":"37","author":"Njoku","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"282","article-title":"Improvement of the AMSR-E algorithm for soil moisture estimation by introducing a fractional vegetation coverage dataset derived from MODIS data","volume":"29","author":"Fujii","year":"2009","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1002\/hyp.3360070205","article-title":"Measuring Surface Soil Moisture Using Passive Microwave Remote Sensing","volume":"7","author":"Jackson","year":"1993","journal-title":"Hydrol. Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1109\/TGRS.2010.2051035","article-title":"Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products","volume":"48","author":"Jackson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3390","DOI":"10.1016\/j.rse.2011.08.003","article-title":"Soil moisture estimation through ASCAT and AMSR-E sensors: An intercomparison and validation study across Europe","volume":"115","author":"Brocca","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4466","DOI":"10.1002\/jgrd.50301","article-title":"Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau","volume":"118","author":"Chen","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.rse.2008.11.011","article-title":"An evaluation of AMSR\u2013E derived soil moisture over Australia","volume":"113","author":"Draper","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1007\/s11430-013-4700-8","article-title":"Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau","volume":"56","author":"Liu","year":"2013","journal-title":"Sci. China Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"The role of satellite remote sensing in climate change studies","volume":"3","author":"Yang","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/2012EI000479.1","article-title":"Satellite Detection of Spatial Distribution and Temporal Changes of Surface Soil Moisture at Three Gorges Dam Region from 2003 to 2011","volume":"17","author":"Du","year":"2013","journal-title":"Earth Interact."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"19735","DOI":"10.1029\/1999JD900107","article-title":"Estimating soil moisture from satellite microwave observations: Past and ongoing projects, and relevance to GCIP","volume":"104","author":"Owe","year":"1999","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TGRS.2002.808243","article-title":"Soil moisture retrieval from AMSR-E","volume":"41","author":"Njoku","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"253","article-title":"Development of a Physically-based Soil Moisture Retrieval Algorithm for Spaceborne Passive Microwave Radiometers and its Application to AMSR-E","volume":"29","author":"Lu","year":"2009","journal-title":"J. Remote Sens. Soc. Jpn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"217","DOI":"10.2208\/prohe.48.217","article-title":"Development of an advanced microwave scanning radiometer (AMSR-E) algorithm for soil moisture and vegetation water content","volume":"48","author":"Koike","year":"2004","journal-title":"Annu. J. Hydraul. Eng. Jpn. Soc. Civ. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1029\/2007JF000769","article-title":"Multisensor historical climatology of satellite - derived global land surface moisture","volume":"113","author":"Owe","year":"2008","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1109\/36.942542","article-title":"A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index","volume":"39","author":"Owe","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2015.03.008","article-title":"Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations","volume":"163","author":"Zeng","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.5194\/hess-15-2303-2011","article-title":"The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products","volume":"15","author":"Su","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10040-006-0103-7","article-title":"Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE","volume":"15","author":"Rodell","year":"2007","journal-title":"Hydrogeol. J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/S0034-4257(03)00051-8","article-title":"Retrieving near-surface soil moisture from microwave radiometric observations: Current status and future plans","volume":"85","author":"Wigneron","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kothari, S.C., and Oh, H. (1993). Neural Networks for Pattern Recognition, MIT Press.","DOI":"10.1016\/S0065-2458(08)60404-0"},{"key":"ref_22","unstructured":"Nigrin, A. (2001). Neural Networks for Pattern Recognition, Oxford university press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1016\/S0893-6080(05)80117-0","article-title":"Extracting algorithms from pattern classification neural networks","volume":"6","author":"Abe","year":"1993","journal-title":"Neural Netw."},{"key":"ref_24","unstructured":"Li, R.P., Mukaidono, M., and Turksen, I.B. (1996, January 14\u201317). Study on feature weight and feature selection in pattern classification neural networks. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Beijing, China."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/BF01237942","article-title":"Pattern classification","volume":"1","author":"Guyon","year":"1998","journal-title":"Pattern Anal. Appl."},{"key":"ref_26","unstructured":"Haykin, S. (1994). Neural Networks: A Comprehensive Foundation, Prentice Hall PTR."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1086\/377335","article-title":"First-Year Wilkinson Microwave Anisotropy Probe (WMAP) Observations: Parameter Estimation Methodology","volume":"148","author":"Verde","year":"2003","journal-title":"Astrophys. J. Suppl. Ser."},{"key":"ref_28","first-page":"1119","article-title":"Prediction of Rainfall Using Backpropagation Neural Network Model","volume":"2","author":"Vamsidhar","year":"2010","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TGRS.2013.2237780","article-title":"Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/36.789630","article-title":"Nonlinear Principal Component Analysis for the Radiometric Inversion of Atmospheric Profiles by Using Neural Networks","volume":"37","author":"Schiavon","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/36.803419","article-title":"A Neural-Network Approach to Radiometric Sensing of Land-Surface Parameters","volume":"36","author":"Liou","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/36.942544","article-title":"Retrieving Soil Moisture from Simulated Brightness Temperatures by a Neural Network","volume":"39","author":"Liou","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1109\/TGRS.2002.800277","article-title":"Retrieval of Crop Biomass and Soil Moisture from Measured 1.4 and 10.65 GHz Brightness Temperatures","volume":"40","author":"Liu","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/S0034-4257(02)00105-0","article-title":"Retrieving soil moisture and agricultural variables by microwave radiometry using neural networks","volume":"84","author":"Ferrazzoli","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3390\/rs2010166","article-title":"Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture","volume":"2","author":"Chai","year":"2010","journal-title":"Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3659","DOI":"10.5194\/hess-16-3659-2012","article-title":"An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo","volume":"16","author":"Santi","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/TGRS.2007.909951","article-title":"Soil Moisture Retrieval from Remotely Sensed Data: Neural Network Approach Versus Bayesian Method","volume":"46","author":"Notarnicola","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5991","DOI":"10.1109\/TGRS.2015.2430845","article-title":"Soil Moisture Retrieval Using Neural Networks: Application to SMOS","volume":"53","author":"Aires","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1093\/nsr\/nwu017","article-title":"Integrated study of the water\u2013ecosystem\u2013economy in the Heihe River Basin","volume":"1","author":"Cheng","year":"2014","journal-title":"Nat. Sci. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1175\/BAMS-D-12-00154.1","article-title":"Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design","volume":"94","author":"Li","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/LGRS.2014.2319085","article-title":"A Nested Ecohydrological Wireless Sensor Network for Capturing the Surface Heterogeneity in the Midstream Areas of the Heihe River Basin, China","volume":"11","author":"Jin","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1080\/13658816.2014.948446","article-title":"Sampling design optimization of a wireless sensor network for monitoring ecohydrological processes in the Babao River basin, China","volume":"29","author":"Ge","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19095","DOI":"10.3390\/s141019095","article-title":"Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China","volume":"14","author":"Kang","year":"2014","journal-title":"Sensors"},{"key":"ref_44","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_45","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_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., Zhang, X., Xiao, Z., Cheng, J., Liu, Q., and Zhao, X. (2014). Global LAnd Surface Satellite (GLASS) Products, Springer International Publishing.","DOI":"10.1007\/978-3-319-02588-9"},{"key":"ref_48","unstructured":"Danielson, J.J., and Jeffrey, J. (1996, January 20\u201322). Delineation of drainage basins from 1 km African digital elevation data. Proceedings of the Pecora Thirteen, Human Interactions with the Environment-Perspectives from Space, Sioux Falls, SD, USA."},{"key":"ref_49","unstructured":"Gesch, D.B., and Larson, K.S. (1996, January 20\u201322). Techniques for development of global 1-kilometer digital elevation models. Proceedings of the Pecora Thirteen, Human Interactions with the Environment-Perspectives from Space, Sioux Falls, SD, USA."},{"key":"ref_50","first-page":"381","article-title":"The global land data assimilation system","volume":"85","author":"Rodell","year":"2004","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/j.envsoft.2005.07.004","article-title":"Land information system: An interoperable framework for high resolution land surface modeling","volume":"21","author":"Kumar","year":"2006","journal-title":"Environ. Model. Softw."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/3477.558801","article-title":"Computational Capabilities of Recurrent NARX Neural Networks","volume":"27","author":"Siegelmann","year":"1997","journal-title":"IEEE Trans. Syst. Man Cybern. Soc. Part B Cybern."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1109\/72.548162","article-title":"Learning Long-Term Dependencies in NARX Recurrent Neural Networks","volume":"7","author":"Lin","year":"1996","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1016\/j.neucom.2008.01.030","article-title":"Long-term time series prediction with the NARX network: An empirical evaluation","volume":"71","author":"Menezes","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5712","DOI":"10.1080\/01431161.2012.671553","article-title":"Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs","volume":"33","author":"Chai","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2014.12.011","article-title":"Spatio-temporal prediction of leaf area index of rubber plantation using HJ-1A\/1B CCD images and recurrent neural network","volume":"102","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogram. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","article-title":"Bayesian interpolation","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/BF00332914","article-title":"Accelerating the convergence of the back-propagation method","volume":"59","author":"Vogl","year":"1988","journal-title":"Biol. Cybern."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1080\/01431160110063788","article-title":"On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions","volume":"22","author":"Owe","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","unstructured":"De Jue, R.A.M. (2003). Retrieval of Land Surface Parameters Using Passive Microwave Remote Sensing. [Ph.D Thesis, Vrije Universiteit Amsterdam]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1029\/2008JD010257","article-title":"Land surface temperature from Ka band (37 GHz) passive microwave observations","volume":"114","author":"Holmes","year":"2009","journal-title":"J. Geophys. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/574\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:38:27Z","timestamp":1760207907000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/6\/574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,8]]},"references-count":61,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["rs9060574"],"URL":"https:\/\/doi.org\/10.3390\/rs9060574","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,8]]}}}