{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:55Z","timestamp":1760146015561,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["22-17-20042"],"award-info":[{"award-number":["22-17-20042"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, the advantages of the joint use of MHz- and GHz-frequency band impulses when employing contactless ground penetration radar (GPR) for the remote sensing of biomass, the height of the wheat canopy, and underlying soil moisture were experimentally investigated. A MHz-frequency band nanosecond impulse with a duration of 1.2 ns (average frequency of 750 MHz and spectrum bandwidth of 580 MHz, at a level of \u20136 dB) was emitted and received by a GPR OKO-3 equipped with an AB-900 M3 antenna unit. A GHz-frequency band sub-nanosecond impulse with a duration of 0.5 ns (average frequency of 3.2 GHz and spectral bandwidth of 1.36 GHz, at a level of \u22126 dB) was generated using a horn antenna and a Keysight FieldFox N9917B 18 GHz vector network analyzer. It has been shown that changes in the relative amplitudes and time delays of nanosecond impulses, reflected from a soil surface covered with wheat at a height from 0 to 87 cm and fresh above-ground biomass (AGB) from 0 to 1.5 kg\/m2, do not exceed 6% and 0.09 ns, respectively. GPR nanosecond impulses reflected\/scattered by the wheat canopy have not been detected. In this research, sub-nanosecond impulses reflected\/scattered by the wheat canopy have been confidently identified and make it possible to measure the wheat height (fresh AGB up to 2.3 kg\/m2 and height up to 104 cm) with a determination coefficient (R2) of ~0.99 and a bias of ~\u22127 cm, as well as fresh AGB where R2 = 0.97, with a bias = \u22120.09 kg\/m2, and a root-mean-square error of 0.1 kg\/m2. The joint use of impulses in two different MHz- and GHz-frequency bands will, in the future, make it possible to create UAV-based reflectometers for simultaneously mapping the soil moisture, height, and biomass of vegetation for precision farming systems.<\/jats:p>","DOI":"10.3390\/rs16193547","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:54:17Z","timestamp":1727168057000},"page":"3547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Measuring Biophysical Parameters of Wheat Canopy with MHz- and GHz-Frequency Range Impulses Employing Contactless GPR"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2624-7223","authenticated-orcid":false,"given":"Konstantin","family":"Muzalevskiy","sequence":"first","affiliation":[{"name":"Laboratory of Radiophysics of the Earth Remote Sensing, Kirensky Institute of Physics, Federal Research Center, KSC Siberian Branch Russian Academy of Science, Krasnoyarsk 660036, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4912-1893","authenticated-orcid":false,"given":"Sergey","family":"Fomin","sequence":"additional","affiliation":[{"name":"Laboratory of Radiophysics of the Earth Remote Sensing, Kirensky Institute of Physics, Federal Research Center, KSC Siberian Branch Russian Academy of Science, Krasnoyarsk 660036, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5410-1788","authenticated-orcid":false,"given":"Andrey","family":"Karavayskiy","sequence":"additional","affiliation":[{"name":"Laboratory of Radiophysics of the Earth Remote Sensing, Kirensky Institute of Physics, Federal Research Center, KSC Siberian Branch Russian Academy of Science, Krasnoyarsk 660036, Russia"}]},{"given":"Julia","family":"Leskova","sequence":"additional","affiliation":[{"name":"Laboratory of Radiophysics of the Earth Remote Sensing, Kirensky Institute of Physics, Federal Research Center, KSC Siberian Branch Russian Academy of Science, Krasnoyarsk 660036, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0536-3452","authenticated-orcid":false,"given":"Alexey","family":"Lipshin","sequence":"additional","affiliation":[{"name":"Krasnoyarsk Research Institute of Agriculture Federal Research Center, KSC Siberian Branch Russian Academy of Science, Krasnoyarsk 630090, Russia"}]},{"given":"Vasily","family":"Romanov","sequence":"additional","affiliation":[{"name":"Krasnoyarsk Research Institute of Agriculture Federal Research Center, KSC Siberian Branch Russian Academy of Science, Krasnoyarsk 630090, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khang, A. (2023). Handbook of Research on AI-Equipped IoT Applications in High-Tech Agriculture, IGI Global.","DOI":"10.4018\/978-1-6684-9231-4"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zaman, Q. (2023). Precision Agriculture Evolution, Insights and Emerging Trends, Elsevier.","DOI":"10.1016\/B978-0-443-18953-1.00013-1"},{"key":"ref_3","unstructured":"(2024, July 01). Cognitive Technologies. Available online: https:\/\/cognitivepilot.com\/breaking-news\/vopros-otvet-o-rabote-novogo-avtopilota-na-traktorah-kirovecz-k-7m\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, D., Li, R., Zhu, B., Liu, T., Sun, C., and Guo, W. (2023). Estimation of Wheat Plant Height and Biomass by Combining UAV Imagery and Elevation Data. Agriculture, 13.","DOI":"10.3390\/agriculture13010009"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"K\u00fcmmerer, R., Noack, P.O., and Bauer, B. (2023). Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass. Remote Sens., 15.","DOI":"10.3390\/rs15061520"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107874","DOI":"10.1016\/j.compag.2023.107874","article-title":"Evaluation of UAV multispectral cameras for yield and biomass prediction in wheat under different sun elevation angles and phenological stages","volume":"210","author":"Shafiee","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Harkel, J.T., Bartholomeus, H., and Kooistra, L. (2020). Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar. Remote Sens., 12.","DOI":"10.3390\/rs12010017"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bates, J.S., Montzka, C., Schmidt, M., and Jonard, F. (2021). Estimating Canopy Density Parameters Time-Series for Winter Wheat Using UAS Mounted LiDAR. Remote Sens., 13.","DOI":"10.3390\/rs13040710"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., Zhang, Q., Duan, R., Liu, J., Qin, Y., and Wang, X. (2023). Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation. Remote Sens., 15.","DOI":"10.3390\/rs15010007"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, T., Liu, Y., Wang, M., Fan, Q., Tian, H., Qiao, X., and Li, Y. (2021). Applications of UAS in Crop Biomass Monitoring: A Review. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.616689"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tang, Z., Parajuli, A., Chen, C.J., Hu, Y., Revolinski, S., Medina, C.A., Lin, S., Zhang, Z., and Yu, L.-X. (2021). Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-82797-x"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108685","DOI":"10.1016\/j.compag.2024.108685","article-title":"Maize height estimation using combined unmanned aerial vehicle oblique photography and LIDAR canopy dynamic characteristics","volume":"218","author":"Liu","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2920","DOI":"10.3390\/s150202920","article-title":"Estimation of Biomass and Canopy Height in Bermudagrass, Alfalfa, and Wheat Using Ultrasonic, Laser, and Spectral Sensors","volume":"15","author":"Pittman","year":"2015","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107262","DOI":"10.1016\/j.compag.2022.107262","article-title":"UAV-based multispectral and thermal cameras to predict soil water content\u2013A machine learning approach","volume":"200","author":"Bertalan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guan, Y., and Grote, K. (2024). Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods. Remote Sens., 16.","DOI":"10.3390\/rs16010061"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lu, F., Sun, Y., and Hou, F. (2020). Using UAV Visible Images to Estimate the Soil Moisture of Steppe. Water, 12.","DOI":"10.3390\/w12092334"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7684","DOI":"10.1109\/JSTARS.2024.3382045","article-title":"Precision Soil Moisture Monitoring With Passive Microwave L-Band UAS Mapping","volume":"17","author":"Kim","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4062","DOI":"10.1109\/TGRS.2020.3005385","article-title":"High Spatial Resolution Soil Moisture Mapping Using a Lobe Differencing Correlation Radiometer on a Small Unmanned Aerial System","volume":"59","author":"Dai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gleich, D. (2023, January 23\u201327). SAR UAV for soil moisture estimation. Proceedings of the 2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Bali, Indonesia.","DOI":"10.1109\/APSAR58496.2023.10388873"},{"key":"ref_20","unstructured":"Farhad, M., Gurbuz, A.C., Kurum, M., and Moorhead, R. (2021). Soil Moisture Mapper: A GNSS-R approach for soil moisture retrieval on UAV. AI for Agriculture and Food Systems, Association for the Advancement of Artificial Intelligence."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111456","DOI":"10.1016\/j.rse.2019.111456","article-title":"A new drone-borne GPR for soil moisture mapping","volume":"235","author":"Wu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2055392","DOI":"10.1080\/08839514.2022.2055392","article-title":"Above-Ground Biomass Wheat Estimation: Deep Learning with UAV-Based RGB Images","volume":"36","author":"Schreiber","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhai, W., Li, C., Cheng, Q., Mao, B., Li, Z., Li, Y., Ding, F., Qin, S., Fei, S., and Chen, Z. (2023). Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications. Remote Sens., 15.","DOI":"10.3390\/rs15143653"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dhakal, R., Maimaitijiang, M., Chang, J., and Caffe, M. (2023). Utilizing Spectral, Structural and Textural Features for Estimating Oat Above-Ground Biomass Using UAV-Based Multispectral Data and Machine Learning. Sensors, 23.","DOI":"10.3390\/s23249708"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., and Xu, B. (2017). Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yuan, W., Li, J., Bhatta, M., Shi, Y., Baenziger, P.S., and Ge, Y. (2018). Wheat Height Estimation Using LiDAR in Comparison to Ultrasonic Sensor and UAS. Sensors, 18.","DOI":"10.3390\/s18113731"},{"key":"ref_27","first-page":"65","article-title":"UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial","volume":"91","author":"Bolten","year":"2023","journal-title":"J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_28","first-page":"476","article-title":"Measuring soil water content with ground penetrating radar: A review","volume":"2","author":"Huisman","year":"2003","journal-title":"Vadose Zone J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.jhydrol.2015.01.065","article-title":"High-resolution space\u2013time quantification of soil moisture along a hillslope using joint analysis of ground penetrating radar and frequency domain reflectometry data","volume":"523","author":"Tran","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_30","unstructured":"Dehem, M. (2020). Soil Moisture Mapping Using a Drone-Borne Ground Penetrating Radar. [Master\u2019s Thesis, Facult\u00e9 des bioing\u00e9nieurs, Universit\u00e9 catholique de Louvain]. Available online: https:\/\/dial.uclouvain.be\/memoire\/ucl\/object\/thesis:27331."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Di Mauro, A., Scozzari, A., and Soldovieri, F. (2022). Instrumentation and Measurement Technologies for Water Cycle Management, Springer Water.","DOI":"10.1007\/978-3-031-08262-7"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, K., Desesquelles, H., Cockenpot, R., Guyard, L., Cuisiniez, V., and Lambot, S. (2022). Ground-Penetrating Radar Full-Wave Inversion for Soil Moisture Mapping in Trench-Hill Potato Fields for Precise Irrigation. Remote Sens., 14.","DOI":"10.3390\/rs14236046"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2022WR032621","DOI":"10.1029\/2022WR032621","article-title":"Estimation of surface soil moisture by a multi-elevation UAV-based ground penetrating radar","volume":"59","author":"Cheng","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_34","unstructured":"Karpukhin, V.I., and Peshkov, A.N. (1985). Measurement of height and biomass of vegetation canopy by radar method. Proceedings of Theory and Technology of Radar, Radio Navigation and Radio Communications in Civil Aviation, The Riga Institute of Civil Aviation Engineers."},{"key":"ref_35","first-page":"500","article-title":"Near-surface soil water content measurements using horn antenna radar: Methodology and overview","volume":"2","author":"Serbin","year":"2003","journal-title":"Vadose Zone J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2005.01.018","article-title":"Ground-penetrating radar measurement of crop and surface water content dynamics","volume":"96","author":"Serbin","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_37","first-page":"274","article-title":"Frequency-domain analyses of GPR waveforms: Enhancing near-surface observational capabilities","volume":"Volume 303","author":"Serbin","year":"2006","journal-title":"Proceedings of the Symposium S7 Held during the Seventh IAHS Scientific Assembly"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/JSTARS.2015.2418093","article-title":"A Layered Vegetation Model for GPR Full-Wave Inversion","volume":"9","author":"Ardekani","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","unstructured":"Ardekani, M.R., Neyt, X., Nottebaere, M., Jacques, D., and Lambot, S. (July, January 30). GPR data inversion for vegetation layer. Proceedings of the 15th International Conference on Ground Penetrating Radar, Brussels, Belgium."},{"key":"ref_40","unstructured":"Carlson, N.L. (1967). Dielectric Constant of Vegetation at 8.5 GHz, Ohio State Univ., EiectroScience Lab."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"734","DOI":"10.21273\/JASHS.123.4.734","article-title":"Measuring water content of soil substitutes with time-domain reflectometry (TDR)","volume":"123","author":"Wallach","year":"1998","journal-title":"J.-Am. Soc. Hortic. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"85213","DOI":"10.1109\/ACCESS.2022.3197636","article-title":"Soil water content estimation with the presence of vegetation using ultra wideband radar-drone","volume":"10","author":"Pramudita","year":"2022","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1029\/WR016i003p00574","article-title":"Electromagnetic determination of soil water content: Measurements in coaxial transmission lines","volume":"16","author":"Topp","year":"1980","journal-title":"Water Resour. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.1109\/TGRS.2011.2114890","article-title":"Mapping field-scale soil moisture with L-band radiometer and ground-penetrating radar over bare soil","volume":"49","author":"Jonard","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.geoderma.2010.12.023","article-title":"Mapping shallow soil moisture profiles at the field scale using full-waveform inversion of ground penetrating radar data","volume":"161","author":"Minet","year":"2011","journal-title":"Geoderma"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"H1","DOI":"10.1190\/geo2011-0054.1","article-title":"Accounting for soil surface roughness in the inversion of ultrawideband off-ground GPR signal for soil moisture retrieval","volume":"77","author":"Jonard","year":"2012","journal-title":"Geophysics"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Andr\u00e9, F., Jonard, F., Jonard, M., Vereecken, H., and Lambot, S. (2019). Accounting for Surface Roughness Scattering in the Characterization of Forest Litter with Ground-Penetrating Radar. Remote Sens., 11.","DOI":"10.3390\/rs11070828"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/8.486303","article-title":"A comparison of theoretical and empirical reflection coefficients for typical exterior wall surfaces in a mobile radio environment","volume":"44","author":"Landron","year":"1996","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1109\/TGRS.2005.863861","article-title":"Indoor C-band polarimetric interferometry observations of a mature wheat canopy","volume":"44","author":"Quegan","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2003.814132","article-title":"High-resolution measurements of scattering in wheat canopies-implications for crop parameter retrieval","volume":"41","author":"Brown","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/TGRS.2013.2250508","article-title":"Tomographic Profiling\u2014A Technique for Multi-Incidence-Angle Retrieval of the Vertical SAR Backscattering Profiles of Biogeophysical Targets","volume":"52","author":"Morrison","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","unstructured":"Geotech LLC (2023, July 01). Available online: https:\/\/geotechru.com\/products\/geophysical-equipment\/antenna\/."},{"key":"ref_53","unstructured":"(2023, July 01). Nizhny Novgorod Scientific and Production Association named after M. V. Frunze. Available online: https:\/\/frunze.nt-rt.ru\/price\/product\/443160."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1109\/LAWP.2023.3278333","article-title":"LPDA Calibration Using an UAV for Synthesizing UWB Impulses","volume":"22","author":"Muzalevskiy","year":"2023","journal-title":"IEEE Antennas Wirel. Propag. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Muzalevskiy, K., Mikhaylov, M., and Ruzicka, Z. (2022, January 11\u201313). Synthesizing of UltraWide Band Impulse by means of a Log-Periodic Dipole Antenna. Case Study for a Radar Stand Experiment. Proceedings of the IEEE International Multi Conference on Engineering, Computer and Information Sciences (SIBIRCON), Yekaterinburg, Russia.","DOI":"10.1109\/SIBIRCON56155.2022.10017008"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1007\/s11141-023-10242-2","article-title":"Synthesis of an Ultra-Wideband Pulse by a Log-Periodic Antenna with Continuous Excitation by Harmonic Oscillations","volume":"65","author":"Muzalevsky","year":"2023","journal-title":"Radiophys. Quantum Electron."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Mironov, L., Bobrov, P.P., and Fomin, S.V. (2013, January 12\u201313). Dielectric model of moist soils with varying clay content in the 0.04 to 26.5 GHz frequency range. Proceedings of the International Siberian Conference on Control and Communications (SIBCON), Tomsk, Russia.","DOI":"10.1109\/SIBCON.2013.6693613"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1109\/LGRS.2020.3014374","article-title":"Dielectric Properties of Water in Saline Soil and its Solonchak Vegetation at a Frequency of 1.41 GHz","volume":"18","author":"Romanov","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s11182-017-1142-3","article-title":"Dielectric properties of marsh vegetation in a frequency range of 0.1\u201318 GHz under variation of temperature and moisture","volume":"60","author":"Romanov","year":"2017","journal-title":"Russ. Phys. J."},{"key":"ref_60","first-page":"88","article-title":"Measurement of height and moisture of an agricultural vegetation using GPS\/GLONASS receiver","volume":"15","author":"Mironov","year":"2014","journal-title":"Sib. Aerosp. J."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1109\/36.158870","article-title":"A dielectric model of the vegetation effects on the microwave emission from soils","volume":"30","author":"Schmugge","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/TGRS.1987.289833","article-title":"Microwave Dielectric Spectrum of Vegetation\u2014Part II: Dual-Dispersion Model","volume":"GE-25","author":"Ulaby","year":"1987","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","first-page":"1260","article-title":"Retrieval of crop biomass and soil moisture from measured 1.4 and 10.65 GHz brightness temperatures","volume":"40","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/36.368208","article-title":"A composite discrete-continuous approach to model the microwave emission of vegetation","volume":"33","author":"Wigneron","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1109\/TGRS.2004.832243","article-title":"On the measurement of microwave vegetation properties: Some guidelines for a protocol","volume":"42","author":"Wigneron","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/TGRS.2003.821889","article-title":"The b-factor as a function of frequency and canopy type at H-polarization","volume":"42","author":"Wigneron","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","first-page":"2269","article-title":"Attenuation of microwave radiation in vegetation [Oslablenie SVCH izlucheniya v rastitel\u2019nom pokrove]","volume":"34","author":"Chukhlantsev","year":"1989","journal-title":"J. Commun. Technol. Electron. [Radiotekhnika I Elektron.]"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1137\/0715063","article-title":"Algorithms for Nonlinear Least-Squares Problem","volume":"15","author":"Gill","year":"1978","journal-title":"SIAM J. Numer. Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3547\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:17Z","timestamp":1760112077000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":68,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193547"],"URL":"https:\/\/doi.org\/10.3390\/rs16193547","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,9,24]]}}}