{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:36:31Z","timestamp":1765233391623,"version":"build-2065373602"},"reference-count":77,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901278","BK20180798","#300405"],"award-info":[{"award-number":["41901278","BK20180798","#300405"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004610","name":"Science and Technology Project of Jiangsu Province","doi-asserted-by":"publisher","award":["41901278","BK20180798","#300405"],"award-info":[{"award-number":["41901278","BK20180798","#300405"]}],"id":[{"id":"10.13039\/501100004610","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Korea Aerospace Research Institute","award":["41901278","BK20180798","#300405"],"award-info":[{"award-number":["41901278","BK20180798","#300405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3\/cm3 and 0.032 cm3\/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.<\/jats:p>","DOI":"10.3390\/rs14164042","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4042","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Comparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model"],"prefix":"10.3390","volume":"14","author":[{"given":"Liangliang","family":"Tao","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5335-6209","authenticated-orcid":false,"given":"Dongryeol","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"given":"Andrew","family":"Western","sequence":"additional","affiliation":[{"name":"Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC 3010, Australia"}]},{"given":"Sun-Gu","family":"Lee","sequence":"additional","affiliation":[{"name":"Satellite Operation and Application Center, Korea Aerospace Research Institute, Daejeon 34133, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.1109\/JSTARS.2022.3166974","article-title":"Estimating High-Resolution Soil Moisture Over Mountainous Regions Using Remotely-Sensed Multispectral and Topographic Data","volume":"15","author":"Fan","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e13990","DOI":"10.1002\/hyp.13990","article-title":"The role of landscape morphology on soil moisture variability in semi-arid ecosystems","volume":"35","author":"Srivastava","year":"2021","journal-title":"Hydrol. Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ivanov, V.Y., Fatichi, S., Jenerette, G.D., Espeleta, J.F., Troch, P.A., and Huxman, T.E. (2010). Hysteresis of soil moisture spatial heterogeneity and the \u201chomogenizing\u201d effect of vegetation. Water Resour. Res., 46.","DOI":"10.1029\/2009WR008611"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3505","DOI":"10.1002\/2014WR016102","article-title":"Abiotic and biotic controls of soil moisture spatiotemporal variability and the occurrence of hysteresis","volume":"51","author":"Fatichi","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4966","DOI":"10.1109\/TGRS.2013.2286203","article-title":"Evaluation of IEM, Dubois, and Oh Radar Backscatter Models Using Airborne L-Band SAR","volume":"52","author":"Panciera","year":"2014","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S274","DOI":"10.5589\/m10-055","article-title":"Evaluation of the Dubois, Oh, and IEM radar backscatter models over agricultural fields using C-band RADARSAT-2 SAR image data","volume":"36","author":"Merzouki","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1080\/01431160600658123","article-title":"Evaluation of radar backscatter models IEM, OH and Dubois using experimental observations","volume":"27","author":"Baghdadi","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"40198","DOI":"10.1109\/ACCESS.2020.2976815","article-title":"Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method","volume":"8","author":"Shahrezaei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rse.2018.02.058","article-title":"Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data","volume":"209","author":"Han","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2017.1285501","article-title":"A study of the feasibility of using KOMPSAT-5 SAR data to map sea ice in the Chukchi Sea in late summer","volume":"8","author":"Han","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hwang, J.-I., and Jung, H.-S. (2018). Automatic Ship Detection Using the Artificial Neural Network and Support Vector Machine from X-Band Sar Satellite Images. Remote Sens., 10.","DOI":"10.3390\/rs10111799"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hwang, J.-I., Chae, S.-H., Kim, D., and Jung, H.-S. (2017). Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery. Appl. Sci., 7.","DOI":"10.3390\/app7090961"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Back, M., Kim, D., Kim, S.-W., and Won, J.-S. (2019). Two-Dimensional Ship Velocity Estimation Based on KOMPSAT-5 Synthetic Aperture Radar Data. Remote Sens., 11.","DOI":"10.3390\/rs11121474"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"208","DOI":"10.2112\/SI102-026.1","article-title":"Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network","volume":"102","author":"Park","year":"2020","journal-title":"J. Coastal Res."},{"key":"ref_15","first-page":"1399","article-title":"Classification of natural and artificial forests from KOMPSAT-3\/3A\/5 images using artificial neural network","volume":"34","author":"Lee","year":"2018","journal-title":"Korean J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Park, S.-E., and Lee, S.-G. (October, January 26). Change Detection of Urban Areas Affected by Earthquake Using Kompsat-5 Data. Proceedings of the IGARSS\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323162"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"539","DOI":"10.7780\/kjrs.2016.32.5.11","article-title":"Detection of water bodies from Kompsat-5 SAR data","volume":"32","author":"Park","year":"2016","journal-title":"Korean J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Refice, A., Zingaro, M., D\u2019Addabbo, A., and Chini, M. (2020). Integrating C- and L-Band SAR Imagery for Detailed Flood Monitoring of Remote Vegetated Areas. Water, 12.","DOI":"10.3390\/w12102745"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Anzidei, M., Scicchitano, G., Scardino, G., Bignami, C., Tolomei, C., Vecchio, A., Serpelloni, E., De Santis, V., Monaco, C., and Milella, M. (2021). Relative Sea-Level Rise Scenario for 2100 along the Coast of South Eastern Sicily (Italy) by InSAR Data, Satellite Images and High-Resolution Topography. Remote Sens., 13.","DOI":"10.5194\/egusphere-egu21-2889"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, D.-J., Moon, W.M., Hwang, J.-H., and Kim, Y.-S. (2010, January 25\u201330). Application of KOMPSAT-5 Data for Emergent Oil Spill Monitoring. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5651230"},{"key":"ref_21","unstructured":"Kim, D.-J. (2011, January 26\u201330). Monitoring of coastal wind and oil spill using KOMPSAT-5. Proceedings of the 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Seoul, Korea."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.5194\/isprs-archives-XLI-B8-1115-2016","article-title":"Oil spill detection and monitoring of Abu Dhabi coastal zone using KOMPSAT-5 SAR imagery","volume":"41","author":"Harahsheh","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/36.628792","article-title":"Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data","volume":"35","author":"Shi","year":"1997","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TGRS.2002.800232","article-title":"Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces","volume":"40","author":"Oh","year":"2002","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_25","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 Tract. Geosci. Remote Sens."},{"key":"ref_26","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 Tract. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/TGRS.2002.807587","article-title":"Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations","volume":"41","author":"Chen","year":"2003","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1191\/0309133303pp378ra","article-title":"Extracting biophysical parameters from remotely sensed radar data: A review of the water cloud model","volume":"27","author":"Graham","year":"2003","journal-title":"Prog. Phys. Geog."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/36.917912","article-title":"A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion","volume":"39","author":"Du","year":"2001","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"5621","DOI":"10.1109\/JSTARS.2016.2596541","article-title":"Estimation of Vegetation Parameters of Water Cloud Model for Global Soil Moisture Retrieval Using Time-Series L-Band Aquarius Observations","volume":"9","author":"Liu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, J., and Wang, S. (2018). Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval. Remote Sens., 10.","DOI":"10.3390\/rs10091370"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1080\/15481603.2020.1857123","article-title":"Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands","volume":"58","author":"Wang","year":"2021","journal-title":"GISci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Musial, J., Malinska, A., Budzynska, M., Gurdak, R., Kiryla, W., Bartold, M., and Grzybowski, P. (2018). Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.20944\/preprints201810.0453.v1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"17","DOI":"10.15201\/hungeobull.69.1.2","article-title":"Parameterizing the modified water cloud model to improve soil moisture data retrieval using vegetation models","volume":"69","author":"Rawat","year":"2020","journal-title":"Hungarian Geograph. Bull."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JSTARS.2019.2891583","article-title":"Soil Moisture Retrieval From SAR and Optical Data Using a Combined Model","volume":"12","author":"Tao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1109\/36.752212","article-title":"A study of vegetation cover effects on ERS scatterometer data","volume":"37","author":"Wagner","year":"1999","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_39","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_40","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_41","doi-asserted-by":"crossref","first-page":"9159","DOI":"10.1080\/01431161.2019.1629503","article-title":"Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: Application of Artificial Neural Networks techniques","volume":"40","author":"Hachani","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","first-page":"1","article-title":"Ensemble Learning Embedded with Gaussian Process Regression for Soil Moisture Estimation: A Case Study of the Continental US","volume":"60","author":"Xue","year":"2022","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2008.08.012","article-title":"On the relevance of using artificial neural networks for estimating soil moisture content","volume":"362","author":"Elshorbagy","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9371","DOI":"10.3390\/rs70709371","article-title":"The Sentinel-1 mission: New opportunities for ice sheet observations","volume":"7","author":"Nagler","year":"2015","journal-title":"Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"757","DOI":"10.5194\/isprs-archives-XLI-B7-757-2016","article-title":"Land cover mapping using sentinel-1 SAR data","volume":"41","author":"Abdikan","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"111954","DOI":"10.1016\/j.rse.2020.111954","article-title":"Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data","volume":"247","author":"Mandal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Khabbazan, S., Vermunt, P., Steele-Dunne, S., Arntz Lexy, R., Marinetti, C., van der Valk, D., Iannini, L., Molijn, R., Westerdijk, K., and van der Sande, C. (2019). Crop Monitoring Using Sentinel-1 Data: A Case Study from The Netherlands. Remote Sens., 11.","DOI":"10.3390\/rs11161887"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1080\/2150704X.2018.1475770","article-title":"Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_49","first-page":"332","article-title":"A systematic study of earthquake detectability using Sentinel-1 Interferometric Wide-Swath data","volume":"216","author":"Funning","year":"2019","journal-title":"Geophys. J. Int."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2794","DOI":"10.1109\/JSTARS.2016.2514402","article-title":"A prototype system for flood monitoring based on flood forecast combined with COSMO-SkyMed and Sentinel-1 data","volume":"9","author":"Boni","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112283","DOI":"10.1016\/j.rse.2021.112283","article-title":"A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains","volume":"255","author":"Nguyen","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_52","first-page":"1","article-title":"Soil Moisture Retrieval From Sentinel-1 Time-Series Data Over Croplands of Northeastern Thailand","volume":"19","author":"Fan","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"3569","DOI":"10.1109\/JSTARS.2020.3004062","article-title":"Retrieval of Surface Soil Moisture From Sentinel-1 Time Series for Reclamation of Wetland Sites","volume":"13","author":"Zakharov","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Huang, J., and Hartemink, A.E. (2020). Establishing an Empirical Model for Surface Soil Moisture Retrieval at the US Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data. Remote Sens., 12.","DOI":"10.3390\/rs12081242"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/36.841999","article-title":"Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery","volume":"38","author":"Saatchi","year":"2000","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/s12524-016-0626-x","article-title":"An Effective Model to Retrieve Soil Moisture from L- and C-Band SAR Data","volume":"45","author":"Tao","year":"2017","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1080\/01431161.2019.1658239","article-title":"Integration of soil moisture as an auxiliary parameter for the anchor pixel selection process in SEBAL using Landsat 8 and Sentinel-1A images","volume":"41","author":"Rajitha","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2017.11.020","article-title":"Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region","volume":"205","author":"Fan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_59","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (1974). Monitoring vegetation systems in the Great Plains with ERTS: Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, NASA SP-351."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/S0034-4257(03)00053-1","article-title":"Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA\/AVHRR","volume":"85","author":"Moreau","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/S0034-4257(02)00069-X","article-title":"A new empirical model to retrieve soil moisture and roughness from C-band radar data","volume":"84","author":"Zribi","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/TGRS.2005.863483","article-title":"Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions","volume":"44","author":"Mattia","year":"2006","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.rse.2003.10.021","article-title":"Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans","volume":"92","author":"Jackson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_66","first-page":"76","article-title":"Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model","volume":"72","author":"Bao","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Tao, L., Ryu, D., Western, A., and Lee, S.-G. (October, January 26). Intercomparison of X-and C-Bands Active Microwave Soil Moisture Retrievals Over Dryland Wheat Fields. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323441"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.5194\/hess-15-1415-2011","article-title":"Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER)","volume":"15","author":"Wang","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/JSTARS.2011.2169236","article-title":"A Fusion Approach to Retrieve Soil Moisture With SAR and Optical Data","volume":"5","author":"Prakash","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2011.2106109","article-title":"On the Retrieval of Soil Moisture in Wheat Fields From L-Band SAR Based on Water Cloud Modeling, the IEM, and Effective Roughness Parameters","volume":"8","author":"Lievens","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/S0034-4257(00)00200-5","article-title":"Parameterization of vegetation backscatter in radar-based, soil moisture estimation","volume":"76","author":"Bindlish","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"096062","DOI":"10.1117\/1.JRS.9.096062","article-title":"Method for soil moisture retrieval in arid prairie using TerraSAR-X data","volume":"9","author":"Bai","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Baghdadi, N., El Hajj, M., Zribi, M., and Bousbih, S. (2017). Calibration of the Water Cloud Model at C-Band for Winter Crop Fields and Grasslands. Remote Sens., 9.","DOI":"10.3390\/rs9090969"},{"key":"ref_74","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 Tract. Geosci. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2166\/nh.2007.029","article-title":"Operational readiness of microwave remote sensing of soil moisture for hydrologic applications","volume":"38","author":"Wagner","year":"2007","journal-title":"Nord. Hydrol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1109\/TGRS.2005.860488","article-title":"Parameterization of tillage-induced single-scale soil roughness from 4-m profiles","volume":"44","author":"Callens","year":"2006","journal-title":"IEEE Tract. Geosci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1080\/014311697217783","article-title":"Remote sensing of bare surface soil moisture using EMAC\/ESAR data","volume":"18","author":"Su","year":"1997","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4042\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:06Z","timestamp":1760141526000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,19]]},"references-count":77,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14164042"],"URL":"https:\/\/doi.org\/10.3390\/rs14164042","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,19]]}}}