{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:14:49Z","timestamp":1769523289253,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"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":["42001362"],"award-info":[{"award-number":["42001362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010242","name":"Jiangsu Planned Projects for Postdoctoral Research Funds","doi-asserted-by":"publisher","award":["2021K427C"],"award-info":[{"award-number":["2021K427C"]}],"id":[{"id":"10.13039\/501100010242","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Start-up Fund of NUIST","award":["2020R078"],"award-info":[{"award-number":["2020R078"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture (SM) has normally been estimated based on a linear relationship between SM and the surface reflectivity (\u0393) from the spaceborne Global Navigation Satellite System (GNSS)-Reflectometry, while it usually relies on inputs of SM data without considering vegetation optical depth (VOD\/\u03c4) effects. In this study, a new scheme is proposed for retrieving soil moisture from the Cyclone GNSS (CyGNSS) data. The variation of CyGNSS-derived \u0394\u0393 is modeled as a function of both variations in SM and VOD (\u0394SM and \u0394\u03c4). For retrieving SM, ancillary \u03c4 data can be obtained from the Soil Moisture Active Passive (SMAP) mission. In addition to this option, a model for simulating \u0394\u03c4 is suggested as an alternative. Experimental evaluation is performed for the time span from August 2019 to July 2021. Excellent agreements between the final retrievals and referenced SMAP SM products are achieved for both training (1-year period) and test (1-year duration) sets. On the whole, overall correlation coefficients (r) of 0.97 and 0.95 and root-mean-square errors (RMSEs) of 0.024 and 0.028 cm3\/cm3 are obtained based on models using the SMAP and simulated \u0394\u03c4, respectively. The model without \u03c4 generates an r of 0.95 and an RMSE of 0.031 cm3\/cm3. The efficiency and necessity of considering \u03c4 are thus confirmed by its enhancement based on correlation and RMSE against the one without \u03c4, and the usefulness of approximating \u0394\u03c4 by sinusoidal functions is also validated. Influences of SM statistics in terms of mean and variance on the retrieval accuracy are evaluated. This work unveils the interaction between CyGNSS data, SM, and \u03c4 and demonstrates the feasibility of integrating the \u0394\u03c4 approximation function into a bilinear regression model to obtain SM results.<\/jats:p>","DOI":"10.3390\/rs14091961","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"1961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Soil Moisture Retrieval from the CyGNSS Data Based on a Bilinear Regression"],"prefix":"10.3390","volume":"14","author":[{"given":"Sizhe","family":"Chen","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6693-957X","authenticated-orcid":false,"given":"Qingyun","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-4828","authenticated-orcid":false,"given":"Shuanggen","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454099, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9622-5041","authenticated-orcid":false,"given":"Weimin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University, St. John\u2019s, NL A1B 3X5, Canada"}]},{"given":"Tiexi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Yan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Shuci","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Qing","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1111\/ecog.05180","article-title":"Climate more important than soils for predicting forest biomass at the continental scale","volume":"43","author":"Bennett","year":"2020","journal-title":"Ecography"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jhydrol.2012.06.021","article-title":"A review of the methods available for estimating soil moisture and its implications for water resource management","volume":"458\u2013459","author":"Dobriyal","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TGRS.1985.289498","article-title":"Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models","volume":"GE-23","author":"Dobson","year":"1985","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","article-title":"The soil moisture active passive (SMAP) mission","volume":"98","author":"Entekhabi","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1109\/36.942551","article-title":"Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission","volume":"39","author":"Kerr","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1016\/j.rse.2011.02.021","article-title":"Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust","volume":"115","author":"Aubert","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.asr.2010.01.014","article-title":"GNSS reflectometry and remote sensing: New objectives and results","volume":"46","author":"Jin","year":"2010","journal-title":"Adv. Sp. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.asr.2011.01.036","article-title":"Remote sensing using GNSS signals: Current status and future directions","volume":"47","author":"Jin","year":"2011","journal-title":"Adv. Sp. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1109\/8.277216","article-title":"Bistatic specular scattering from rough dielectric surfaces","volume":"42","author":"Ulaby","year":"1994","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6829","DOI":"10.1109\/TGRS.2014.2303831","article-title":"Spaceborne GNSS-R minimum variance wind speed estimator","volume":"52","author":"Clarizia","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1002\/2015GL064204","article-title":"Spaceborne GNSS reflectometry for ocean winds: First results from the UK TechDemoSat-1 mission","volume":"42","author":"Foti","year":"2015","journal-title":"Geophys. Res. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9756","DOI":"10.1109\/TGRS.2019.2929002","article-title":"Application of neural network to GNSS-R wind speed retrieval","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4992","DOI":"10.1109\/TGRS.2013.2286257","article-title":"Consolidating the precision of interferometric GNSS-R ocean altimetry using airborne experimental data","volume":"52","author":"Cardellach","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1109\/JSTARS.2016.2582690","article-title":"Spaceborne GNSS-R sea ice detection using delay-Doppler maps: First results from the U.K. TechDemoSat-1 mission","volume":"9","author":"Yan","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4322","DOI":"10.1109\/TGRS.2018.2890646","article-title":"Time-series retrieval of soil moisture using CYGNSS","volume":"57","author":"Johnson","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chew, C., and Small, E. (2020). Description of the UCAR\/CU soil moisture product. Remote Sens., 12.","DOI":"10.3390\/rs12101558"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1029\/2018GL077905","article-title":"Soil moisture sensing using spaceborne GNSS reflections: Comparison of CYGNSS reflectivity to SMAP soil moisture","volume":"45","author":"Chew","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1109\/JSTARS.2019.2895510","article-title":"Analysis of CYGNSS data for soil moisture retrieval","volume":"12","author":"Clarizia","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dong, Z., and Jin, S. (2021). Evaluation of the land GNSS-Reflected DDM coherence on soil moisture estimation from CYGNSS data. Remote Sens., 13.","DOI":"10.3390\/rs13040570"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Eroglu, O., Kurum, M., Boyd, D., and Gurbuz, A.C. (2019). High spatio-temporal resolution CYGNSS soil moisture estimates using artificial neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11192272"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Santi, E., Pettinato, S., Paloscia, S., Clarizia, M.P., Dente, L., Guerriero, L., Comite, D., and Pierdicca, N. (October, January 26). Soil moisture and forest biomass retrieval on a global scale by using CyGNSS data and artificial neural networks. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323896"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Senyurek, V., Lei, F., Boyd, D., Gurbuz, A.C., Kurum, M., and Moorhead, R. (2020). Evaluations of machine learning-based CYGNSS soil moisture estimates against SMAP observations. Remote Sens., 12.","DOI":"10.3390\/rs12213503"},{"key":"ref_24","first-page":"1","article-title":"Near real-time soil moisture in China retrieved from CyGNSS reflectivity","volume":"19","author":"Yan","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111947","DOI":"10.1016\/j.rse.2020.111944","article-title":"Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data","volume":"247","author":"Yan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, T., Wan, W., Sun, Z., Liu, B., Li, S., and Chen, X. (2020). Comprehensive evaluation of using TechDemoSat-1 and CYGNSS data to estimate soil moisture over mainland China. Remote Sens., 12.","DOI":"10.3390\/rs12111699"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chew, C. (2021). Spatial interpolation based on previously-observed behavior: A framework for interpolating spaceborne GNSS-R data from CYGNSS. J. Spat. Sci.","DOI":"10.1080\/14498596.2021.1942253"},{"key":"ref_28","unstructured":"O\u2019Neill, P.E., Chan, S., Njoku, E.G., Jackson, T., and Bindlish, R. (2020). SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture, National Snow and Ice Data Center. Version 7."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1109\/TGRS.2015.2504242","article-title":"First results of a GNSS-R experiment from a stratospheric balloon over boreal forests","volume":"54","author":"Camps","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5699","DOI":"10.1029\/JC084iC09p05699","article-title":"Effect of surface roughness on the microwave emission from soils","volume":"89","author":"Choudhury","year":"1979","journal-title":"J. Geophys. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/JSTARS.2010.2052916","article-title":"Dense temporal series of C- and L-band SAR data for soil moisture retrieval over Agricultural Crops","volume":"4","author":"Balenzano","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","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 Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.rse.2017.01.024","article-title":"Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms","volume":"192","author":"Wigneron","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/1961\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:56:40Z","timestamp":1760137000000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/1961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,19]]},"references-count":33,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14091961"],"URL":"https:\/\/doi.org\/10.3390\/rs14091961","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,19]]}}}