{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T20:07:41Z","timestamp":1774123661190,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T00:00:00Z","timestamp":1624320000000},"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":["41771402"],"award-info":[{"award-number":["41771402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2017YFB0502700"],"award-info":[{"award-number":["2017YFB0502700"]}]},{"name":"Sichuan Science and Technology Program","award":["2018JY0664"],"award-info":[{"award-number":["2018JY0664"]}]},{"name":"Sichuan Science and Technology Program","award":["2019ZDZX0042"],"award-info":[{"award-number":["2019ZDZX0042"]}]},{"name":"Sichuan Science and Technology Program","award":["2020JDTD0003"],"award-info":[{"award-number":["2020JDTD0003"]}]},{"name":"Sichuan Science and Technology Program","award":["2020YJ0322"],"award-info":[{"award-number":["2020YJ0322"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3\/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.<\/jats:p>","DOI":"10.3390\/rs13132442","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T22:10:59Z","timestamp":1624399859000},"page":"2442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2082-945X","authenticated-orcid":false,"given":"Jichao","family":"Lv","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0809-7682","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinsheng","family":"Tu","sequence":"additional","affiliation":[{"name":"College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8903-3535","authenticated-orcid":false,"given":"Mingjie","family":"Liao","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiatai","family":"Pang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kui","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8756-2211","authenticated-orcid":false,"given":"Wei","family":"Xiang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Fu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.1100217","article-title":"Regions of Strong Coupling between Soil Moisture and Precipitation","volume":"305","author":"Koster","year":"2004","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/02626669609491523","article-title":"Remote sensing applications to hydrology: Soil moisture","volume":"41","author":"Jackson","year":"1996","journal-title":"Int. Assoc. Sci. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Larson, K.M., and Small, E.E. (2014). GPS ground networks for water cycle sensing. IEEE Geosci. Remote Sens. Sym., 3822\u20133825.","DOI":"10.1109\/IGARSS.2014.6947317"},{"key":"ref_4","first-page":"775","article-title":"GPS interferometric reflectometry: Applications to surface soil moisture, snow depth, and vegetation water content in the western United States. Wiley Interdiscip","volume":"3","author":"Larson","year":"2016","journal-title":"Rev. Water."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/JSTARS.2009.2033608","article-title":"A Physical Model for GPS Multipath Caused by Land Reflections: Toward Bare Soil MoistureRetrievals","volume":"3","author":"Zavorotny","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1109\/JSTARS.2014.2300116","article-title":"Normalized Microwave Reflection Index: A Vegetation Measurement Derived from GPS Networks","volume":"7","author":"Larson","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TGRS.2013.2242332","article-title":"Effects of Near-Surface Soil Moisture on GPS SNR Data: Development of a Retrieval Algorithm for Soil Moisture","volume":"52","author":"Chew","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Roussel, N., Darrozes, J., Ha, C., Boniface, K., Frappart, F., and Ramillien, G. (2016). Multi-scale volumetric soil moisture detection from GNSS SNR data: Ground-based and airborne applications. MetroAeroSpace, 573\u2013578.","DOI":"10.1109\/MetroAeroSpace.2016.7573279"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10291-007-0076-6","article-title":"Using GPS Multipath to Measure Soil Moisture Fluctuations: Initial Results","volume":"12","author":"Larson","year":"2008","journal-title":"GPS Solut."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1029\/2008GL036013","article-title":"Use of GPS Receivers as a Soil Moisture Network for Water Cycle Studies","volume":"35","author":"Larson","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/JSTARS.2009.2033612","article-title":"GPS Multipath and Its Relation to Near-Surface Soil Moisture Content","volume":"3","author":"Larson","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2755","DOI":"10.1109\/TGRS.2014.2364513","article-title":"Vegetation Sensing Using GPS-Interferometric Reflectometry: Theoretical Effects of Canopy Parameters on Signal-to-Noise Ratio Data","volume":"53","author":"Chew","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/s10291-015-0462-4","article-title":"An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil","volume":"20","author":"Chew","year":"2016","journal-title":"GPS Solut."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10291-014-0383-7","article-title":"Using geodetic GPS receivers to measure vegetation water content","volume":"19","author":"Wan","year":"2015","journal-title":"GPS Solut."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"L12401","DOI":"10.1029\/2010GL042951","article-title":"Sensing vegetation growth with reflected GPS signals","volume":"37","author":"Small","year":"2010","journal-title":"Geophys. Res. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1512","DOI":"10.1109\/JSTARS.2014.2320597","article-title":"Normalized Microwave Reflection Index: Validation of Vegetation Water Content Estimates from Montana Grasslands","volume":"7","author":"Smal","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1080\/01431161.2018.1484961","article-title":"Research on soil moisture inversion method based on GA-BP neural network model","volume":"40","author":"Liang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4759","DOI":"10.1109\/JSTARS.2015.2504527","article-title":"Validation of GPS-IR Soil Moisture Retrievals: Comparison of Different Algorithms to Remove Vegetation Effects","volume":"9","author":"Small","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2104","DOI":"10.1080\/01431161.2018.1475778","article-title":"Research on the soil moisture sliding estimation method using the LS-SVM based on multi-satellite fusion","volume":"40","author":"Ren","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1080\/01621459.1991.10475126","article-title":"Nonlinear Modelling of Time Series Using Multivariate Adaptive Regression Splines (MARS)","volume":"86","author":"Lewis","year":"1991","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Roggenbuck, O., Reinking, J., and Lambertus, T. (2019). Determination of Significant Wave Heights Using Damping Coefficients of Attenuated GNSS SNR Data from Static and Kinematic Observations. Remote Sens., 11.","DOI":"10.3390\/rs11040409"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"RS6003","DOI":"10.1029\/2007RS003652","article-title":"Mapping the GPS multipath environment using the signal-to-noise ratio (SNR)","volume":"42","author":"Bilich","year":"2007","journal-title":"Radio Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, M., Zhu, Y., Yang, D., Hong, X., and Song, S. (2018). A Semi-Empirical SNR Model for Soil Moisture Retrieval Using GNSS SNR Data. Remote Sens., 10.","DOI":"10.3390\/rs10020280"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yuan, Q., Li, S., Yue, L., Li, T., Shen, H., and Zhang, L. (2019). Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point\u2013Surface Fusion of MODIS Products and GNSS-IR Observations. Remote Sens., 11.","DOI":"10.3390\/rs11121440"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Camps, A., Alonso-Arroyo, A., Park, H., Onrubia, R., Pascual, D., and Querol, J. (2020). L-Band Vegetation Optical Depth Estimation Using Transmitted GNSS Signals: Application to GNSS-Reflectometry and Positioning. Remote Sens., 12.","DOI":"10.3390\/rs12152352"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1080\/01431161.2014.894660","article-title":"Comparison of vegetation phenology in the western USA determined from reflected GPS microwave signals and NDVI","volume":"35","author":"Evans","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.ecolmodel.2006.05.022","article-title":"Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species","volume":"199","author":"Leathwick","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/10618600.2017.1360780","article-title":"A Generalized Estimating Equation Approach to Multivariate Adaptive Regression Splines","volume":"27","author":"Jakub","year":"2018","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1007\/s00704-017-2300-9","article-title":"Application of random forest time series, support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros, Iran)","volume":"134","author":"Hamidi","year":"2018","journal-title":"Theor Appl Climatol."},{"key":"ref_30","unstructured":"Jin, W., Li, Z.J., Wei, L.S., and Zhen, H. (2000, January 21\u201325). The Improvements of BP Neural Network Learning Algorithm. Proceedings of the IEEE 5th International Conference on Signal Processing Proceedings, Rotorua, New Zealand."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/asmb.537","article-title":"A tutorial on v-support vector machines","volume":"21","author":"Chen","year":"2005","journal-title":"Appl. Stoch. Models. Bus. Ind."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2442\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:21:26Z","timestamp":1760163686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2442"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,22]]},"references-count":31,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132442"],"URL":"https:\/\/doi.org\/10.3390\/rs13132442","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,22]]}}}