{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T09:18:05Z","timestamp":1778923085935,"version":"3.51.4"},"reference-count":73,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2020YFE0200700, 2019YFE0127300"],"award-info":[{"award-number":["2020YFE0200700, 2019YFE0127300"]}]},{"name":"Major Special Project-the China High-Resolution Earth Observation System","award":["30-Y30F06-9003-20\/22"],"award-info":[{"award-number":["30-Y30F06-9003-20\/22"]}]},{"name":"Guangxi Science and Technology Development Project of Major Projects","award":["Guike AA18118048-2"],"award-info":[{"award-number":["Guike AA18118048-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant from cloud obstruction in optical images and the malfunction of in situ SMC-measuring instruments. With this method, data typically excluded in conventional approaches can be adequately employed. On the other hand, apart from the basic input parameters commonly discussed in previous studies, a couple of new parameters were optimized to improve the feature description. The Sentinel-1 SAR and Landsat-8 images were adopted to retrieve SMC in the study area in eastern Austria. By the SSE method, the number of available samples increased from 264 to 635 for ANN training and testing, and the retrieval accuracy could be markedly improved. Furthermore, the optimized parameters also improve the inversion effect, and the elevation was the most influential input parameter.<\/jats:p>","DOI":"10.3390\/s22041611","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:23:29Z","timestamp":1645431809000},"page":"1611","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization"],"prefix":"10.3390","volume":"22","author":[{"given":"Qixin","family":"Liu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingfa","family":"Gu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinran","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1322-126X","authenticated-orcid":false,"given":"Faisal","family":"Mumtaz","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunmei","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Long March Vehicle, Beijing 100076, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dakang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5771-4168","authenticated-orcid":false,"given":"Yulin","family":"Zhan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5239","DOI":"10.1080\/2150704X.2014.933277","article-title":"An appraisal of the accuracy of operational soil moisture estimates from SMOS MIRAS using validated in situ observations acquired in a Mediterranean environment","volume":"35","author":"Petropoulos","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1175\/JHM480.1","article-title":"Seasonal variations in terrestrial water storage for major midlatitude river basins","volume":"7","author":"Hirschi","year":"2006","journal-title":"J. Hydrometeorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"358","DOI":"10.2136\/vzj2007.0143","article-title":"Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review","volume":"7","author":"Robinson","year":"2008","journal-title":"Vadose Zone J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating soil moisture\u2013climate interactions in a changing climate: A review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth-Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1029\/RS015i005p00977","article-title":"The dielectric properties of soil-water mixtures at microwave frequencies","volume":"15","author":"Wang","year":"1980","journal-title":"Radio Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1109\/TGRS.2011.2168533","article-title":"Validation of soil moisture and ocean salinity (SMOS) soil moisture over watershed networks in the US","volume":"50","author":"Jackson","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.5194\/hess-14-2355-2010","article-title":"An inversion method based on multi-angular approaches for estimating bare soil surface parameters from RADARSAT-1","volume":"14","author":"Sahebi","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, B., Fan, H., Huang, J., and Zhao, H. (2016). The potential use of multi-band SAR data for soil moisture retrieval over bare agricultural areas: Hebei, China. Remote Sens., 8.","DOI":"10.3390\/rs8010007"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., and Baghdadi, N. (2017). Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/36.134085","article-title":"Backscattering from a randomly rough dielectric surface","volume":"30","author":"Fung","year":"1992","journal-title":"IEEE Trans. Geosci. Rremote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1109\/36.951094","article-title":"A transition model for the reflection coefficient in surface scattering","volume":"39","author":"Wu","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/36.134086","article-title":"An empirical model and an inversion technique for radar scattering from bare soil surfaces","volume":"30","author":"Oh","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/36.406677","article-title":"Measuring soil moisture with imaging radars","volume":"33","author":"Dubois","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1080\/01431169008955090","article-title":"Michigan microwave canopy scattering model","volume":"11","author":"Ulaby","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1029\/RS013i002p00357","article-title":"Vegetation modeled as a water cloud","volume":"13","author":"Attema","year":"1978","journal-title":"Radio Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11229","DOI":"10.1029\/JC087iC13p11229","article-title":"A model for microwave emission from vegetation-covered fields","volume":"87","author":"Mo","year":"1982","journal-title":"J. Geophys. Rese. Oceans"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1109\/TGRS.2002.807003","article-title":"A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer","volume":"40","author":"Shi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0273-1177(01)00626-3","article-title":"Application of multisensor data for evaluation of soil moisture","volume":"29","author":"Gruszczynska","year":"2002","journal-title":"Adv. Space Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TGRS.2006.872287","article-title":"Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas","volume":"44","author":"Notarnicola","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7162","DOI":"10.1109\/TGRS.2018.2849009","article-title":"A coupling model for soil moisture retrieval in sparse vegetation covered areas based on microwave and optical remote sensing data","volume":"56","author":"Kong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10966","DOI":"10.3390\/rs61110966","article-title":"A synergistic methodology for soil moisture estimation in an alpine prairie using radar and optical satellite data","volume":"6","author":"He","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, L., Meng, Q., Yao, S., Wang, Q., Zeng, J., Zhao, S., and Ma, J. (2018). Soil moisture retrieval from the Chinese GF-3 satellite and optical data over agricultural fields. Sensors, 18.","DOI":"10.3390\/s18082675"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, L., Wang, C., Yu, T., Gu, X., and Liu, Q. (2020). High-precision soil moisture mapping based on multi-model coupling and background knowledge, over vegetated areas using chinese Gf-3 and GF-1 satellite data. Remote Sens., 12.","DOI":"10.3390\/rs12132123"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khabazan, S., Motagh, M., and Hosseini, M. (2013, January 5\u20138). Evaluation of radar backscattering models IEM, OH, and dubois using L and C-Bands SAR Data over different vegetation canopy covers and soil depths. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1\/W3 2013, SMPPR 2013, Tehran, Iran.","DOI":"10.5194\/isprsarchives-XL-1-W3-225-2013"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/LGRS.2014.2326890","article-title":"Method for soil moisture and surface temperature estimation in the Tibetan Plateau using spaceborne radiometer observations","volume":"12","author":"Zeng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yao, P., Shi, J., Zhao, T., Lu, H., and Al-Yaari, A. (2017). Rebuilding long time series global soil moisture products using the neural network adopting the microwave vegetation index. Remote Sens., 9.","DOI":"10.3390\/rs9010035"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/JSTARS.2015.2464698","article-title":"Coupling SAR C-band and optical data for soil moisture and leaf area index retrieval over irrigated grasslands","volume":"9","author":"Baghdadi","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2016.01.027","article-title":"Soil moisture retrieval over irrigated grassland using X-band SAR data","volume":"176","author":"Baghdadi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.11.011","article-title":"Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 1: Satellite data analysis","volume":"173","author":"Kolassa","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4961","DOI":"10.3390\/rs5104961","article-title":"Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site","volume":"5","author":"Santi","year":"2013","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Alexakis, D.D., Mexis, F.D., Vozinaki, A.E., Daliakopoulos, I.N., and Tsanis, I.K. (2017). Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach. Sensors, 17.","DOI":"10.3390\/s17061455"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.jhydrol.2016.10.005","article-title":"Validation and reconstruction of FY-3B\/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau","volume":"543","author":"Cui","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xing, C., Chen, N., Zhang, X., and Gong, J. (2017). A machine learning based reconstruction method for satellite remote sensing of soil moisture images with in situ observations. Remote Sens., 9.","DOI":"10.3390\/rs9050484"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.rse.2012.03.014","article-title":"Trend-preserving blending of passive and active microwave soil moisture retrievals","volume":"123","author":"Liu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_38","unstructured":"British Broadcasting Corporation (2022, January 20). Average Conditions, Vienna, Austria. Available online: https:\/\/web.archive.org\/web\/20101202042009\/http:\/\/www.bbc.co.uk\/weather\/world\/city_guides\/results.shtml?tt=TT000033."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1007\/s10040-016-1442-7","article-title":"Assessment of small-diameter shallow wells for managed aquifer recharge at a site in southern Styria, Austria","volume":"24","author":"Liu","year":"2016","journal-title":"Hydrogeol. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.rse.2019.02.008","article-title":"Assessment and inter-comparison of recently developed\/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements","volume":"224","author":"Wigneron","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"vzj2012.0097","DOI":"10.2136\/vzj2012.0097","article-title":"Global automated quality control of in situ soil moisture data from the International Soil Moisture Network","volume":"12","author":"Dorigo","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gruber, A., Wagner, W., Hegyiova, A., Greifeneder, F., and Schlaffer, S. (2013, January 21\u201326). Potential of Sentinel-1 for high-resolution soil moisture monitoring. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723717"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2013.02.027","article-title":"Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation","volume":"134","author":"Paloscia","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/JSTARS.2012.2190136","article-title":"Potential for high resolution systematic global surface soil moisture retrieval via change detection using Sentinel-1","volume":"5","author":"Hornacek","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Attarzadeh, R., Amini, J., Notarnicola, C., and Greifeneder, F. (2018). Synergetic Use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at plot scale. Remote Sens., 10.","DOI":"10.3390\/rs10081285"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Angelliaume, S. (2016). Analysis of Sentinel-1 radiometric stability and quality for land surface applications. Remote Sens., 8.","DOI":"10.3390\/rs8050406"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2017.12.011","article-title":"Quantifying the relative contributions of vegetation and soil moisture conditions to polarimetric C-Band SAR response in a temperate peatland","volume":"206","author":"Millard","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/0034-4257(95)00212-X","article-title":"Taking into account vegetation effects to estimate soil moisture from C-band radar measurements","volume":"56","author":"Taconet","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.catena.2007.09.009","article-title":"Surface soil hydraulic properties in four soil series under different land uses and their temporal changes","volume":"73","author":"Zhou","year":"2008","journal-title":"Catena"},{"key":"ref_50","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\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3127","DOI":"10.1007\/s11269-013-0337-9","article-title":"Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application","volume":"27","author":"Srivastava","year":"2013","journal-title":"Water Resour. Manag."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of surface moisture status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/014311697219286","article-title":"Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site","volume":"18","author":"Goetz","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1109\/LGRS.2014.2314617","article-title":"Subsurface soil moisture estimation by VI\u2013LST method","volume":"11","author":"Holzman","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1175\/1520-0450(1993)032<0548:DSDEOS>2.0.CO;2","article-title":"Developing satellite-derived estimates of surface moisture status","volume":"32","author":"Nemani","year":"1993","journal-title":"J. Appl. Meteor."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"W01508","DOI":"10.1029\/2009WR009002","article-title":"Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions","volume":"47","author":"Joshi","year":"2011","journal-title":"Water Resour. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/S0309-1708(01)00034-3","article-title":"Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation","volume":"24","author":"Mohanty","year":"2001","journal-title":"Adv. Water Resour."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-2113(04)85001-6","article-title":"Advances in hydropedology","volume":"85","author":"Lin","year":"2005","journal-title":"Adv. Agron."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/0022-1694(83)90102-6","article-title":"Surface soil moisture variation on small agricultural watersheds","volume":"62","author":"Hawley","year":"1983","journal-title":"J. Hydrol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.2136\/sssaj2006.0046","article-title":"Tillage effects on hydraulic properties and macroporosity in silty and sandy soils","volume":"70","author":"Buczko","year":"2006","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"3193","DOI":"10.1016\/j.jhydrol.2014.10.040","article-title":"A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation","volume":"519","author":"Tapoglou","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1061\/(ASCE)1084-0699(2008)13:6(461)","article-title":"ANN-based soil moisture retrieval over bare and vegetated areas using ERS-2 SAR data","volume":"13","author":"Said","year":"2008","journal-title":"J. Hydrol. Eng."},{"key":"ref_65","first-page":"61","article-title":"Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors","volume":"48","author":"Santi","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","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_67","unstructured":"Gavin, H. (2022, January 20). The Levenberg-Marquardt Method for Nonlinear Least Squares Curve-Fitting Problems. Department of Civil and Environmental Engineering, Duke University. 9 October 2013; pp. 1\u201317. Available online: https:\/\/www.academia.edu\/9985415\/The_Levenberg_Marquardt_method_for_nonlinear_least_squares_curve_fitting_problems."},{"key":"ref_68","first-page":"85","article-title":"Estimation of soil moisture in vegetation-covered floodplains with sentinel-1 SAR data using support vector regression","volume":"86","author":"Holtgrave","year":"2018","journal-title":"PFG\u2013J. Photogram. Remote Sens. Geoinf. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1080\/01431160500239032","article-title":"Soil moisture estimation using multi-incidence and multi-polarization ASAR data","volume":"27","author":"Baghdadi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1175\/1520-0450(1978)017<1549:RSOSSM>2.0.CO;2","article-title":"Remote Sensing of Surface Soil Moisture","volume":"17","author":"Schmugge","year":"1978","journal-title":"J. Appl. Meteor."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"18987","DOI":"10.1029\/92JD00882","article-title":"Soil moisture variability within remote sensing pixels","volume":"97","author":"Charpentier","year":"1992","journal-title":"J. Geophys. Res."},{"key":"ref_72","first-page":"63","article-title":"Coupling use-dependent and use-invariant data for soil quality evaluation in the United States","volume":"56","author":"Grossman","year":"2001","journal-title":"J. Soil Water Conserv."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wagner, W., Reimer, C., Bauer-Marschallinger, B., Enenkel, M., Hahn, S., Melzer, T., Naeimi, V., Paulik, C., and Dorigo, W. (2015, January 11\u201315). Long-term soil moisture time series analyses based on active microwave backscatter measurements. Proceedings of the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7\/W3, 2015 36th International Symposium on Remote Sensing of Environment, Berlin, Germany.","DOI":"10.5194\/isprsarchives-XL-7-W3-545-2015"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1611\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:22:28Z","timestamp":1760134948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,18]]},"references-count":73,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041611"],"URL":"https:\/\/doi.org\/10.3390\/s22041611","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,18]]}}}