{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T03:04:56Z","timestamp":1760151896944,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42030509","41901317"],"award-info":[{"award-number":["42030509","41901317"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The variability of surface roughness may lead to relatively large dynamic of backscatter coefficient observed by the synthetic aperture radar (SAR), which complicates the soil moisture (SM) retrieval process based on active remote sensing. The effective roughness parameters are commonly used for parameterizing the soil scattering models, the values of which are often assumed to be constant during different study periods for the same site. This paper investigates the reasonableness of this hypothesis from the perspective of backscatter coefficient simulation and SM retrieval using high resolution SAR data. Three years of Sentinel-1A data from 2016 to 2018 were collected over a sparsely vegetated field within the REMEDHUS SM monitoring network. The advanced integral equation model (AIEM) and Dobson dielectric mixing model were combined for optimizing the effective roughness parameters, as well as simulating the backscatter coefficient and retrieving the SM. The effective roughness parameters were optimized at different temporal periods, such as 2016, 2017, 2018, 2016 + 2017, 2017 + 2018, and 2016 + 2017 + 2018, to analyze their temporal dynamics. It was found that: (1) the effective roughness parameters optimized at different temporal periods are very close to each other; (2) the simulated backscatter from AIEM is consistent with Sentinel-1A observation with root mean square errors (RMSEs) between 1.133 and 1.163 dB and correlation coefficient \u00ae value equals to 0.616; (3) the seasonal dynamics ofin situ SM is well-captured by the retrieved SM with R values floating at 0.685 and RMSEs ranging from 0.049 to 0.052 m3\/m3; and (4) inverse of the AIEM with the implementation of effective roughness parameters achieves better performance for SM retrieval than the change detection method. These findings demonstrate that the assumption on the constant effective roughness parameters during the study period of at least three years is reasonable.<\/jats:p>","DOI":"10.3390\/rs14236020","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"6020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Assessment of Effective Roughness Parameters for Simulating Sentinel-1A Observation and Retrieving Soil Moisture over Sparsely Vegetated Field"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiaojing","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.jhydrol.2012.10.044","article-title":"Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications","volume":"476","author":"Kornelsen","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1175\/JHM-D-14-0198.1","article-title":"Augmentations to the Noah Model Physics for Application to the Yellow River Source Area. Part I: Soil Water Flow","volume":"16","author":"Zheng","year":"2015","journal-title":"J. Hydrometeorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1002\/2013WR013807","article-title":"Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data","volume":"50","author":"Sutanudjaja","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.jhydrol.2018.06.024","article-title":"Impact of soil freeze-thaw mechanism on the runoff dynamics of two Tibetan rivers","volume":"563","author":"Zheng","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S0034-4257(97)00045-X","article-title":"Opportunities and limitations for image-based remote sensing in precision crop management","volume":"61","author":"Moran","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","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_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jhydrol.2004.01.008","article-title":"In situ measurement of soil moisture: A comparison of techniques","volume":"293","author":"Walker","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3075","DOI":"10.5194\/essd-13-3075-2021","article-title":"Status of the Tibetan Plateau observatory (Tibet-Obs) and a 10-year (2009\u20132019) surface soil moisture dataset","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.pce.2015.02.009","article-title":"Surface soil moisture retrievals from remote sensing: Current status, products & future trends","volume":"83\u201384","author":"Petropoulos","year":"2015","journal-title":"Phys. Chem. Earth Parts A\/B\/C"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.rse.2018.03.011","article-title":"Impact of surface roughness, vegetation opacity and soil permittivity on L-band microwave emission and soil moisture retrieval in the third pole environment","volume":"209","author":"Zheng","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advwatres.2017.09.006","article-title":"Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms","volume":"109","author":"Karthikeyan","year":"2017","journal-title":"Adv. Water Resour."},{"key":"ref_14","first-page":"4301814","article-title":"Active and Passive Microwave Signatures of Diurnal Soil Freeze-Thaw Transitions on the Tibetan Plateau","volume":"60","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"210","DOI":"10.3390\/rs1030210","article-title":"Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques","volume":"1","author":"Barrett","year":"2009","journal-title":"Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TGRS.2018.2858004","article-title":"Toward Global Soil Moisture Monitoring with Sentinel-1: Harnessing Assets and Overcoming Obstacles","volume":"57","author":"Freeman","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bai, X., He, B., Li, X., Zeng, J., Wang, X., Wang, Z., Zeng, Y., and Su, Z. (2017). First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model over the Tibetan Plateau. Remote Sens., 9.","DOI":"10.3390\/rs9070714"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ma, C., Li, X., and McCabe, M.F. (2020). Retrieval of High-Resolution Soil Moisture through Combination of Sentinel-1 and Sentinel-2 Data. Remote Sens., 12.","DOI":"10.3390\/rs12142303"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ezzahar, J., Ouaadi, N., Zribi, M., Elfarkh, J., Aouade, G., Khabba, S., Er-Raki, S., Chehbouni, A., and Jarlan, L. (2019). Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data. Remote Sens., 12.","DOI":"10.3390\/rs12010072"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112554","DOI":"10.1016\/j.rse.2021.112554","article-title":"Sentinel-1 soil moisture at 1 km resolution: A validation study","volume":"263","author":"Balenzano","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_22","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_23","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 Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/TGRS.2003.821065","article-title":"Quantitative Retrieval of Soil Moisture Content and Surface Roughness from Multipolarized Radar Observations of Bare Soil Surfaces","volume":"42","author":"Oh","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","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_26","unstructured":"Fung, A.K. (1994). Microwave Scattering and Emission Models and Their Applications, Artech House Publishers."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1109\/36.951094","article-title":"A transition model for the reflection coefficients in surface scattering","volume":"39","author":"Wu","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","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 Trans. Geosci. Remote Sens."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/0034-4257(95)00151-4","article-title":"A fully polarimetric multiple scattering model for crops","volume":"54","author":"Bracaglia","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/36.485121","article-title":"Passive microwave remote sensing of forests: A model investigation","volume":"34","author":"Ferrazzoli","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5324","DOI":"10.1109\/TGRS.2017.2705248","article-title":"L-Band Microwave Emission of Soil Freezesc-Thaw Process in the Third Pole Environment","volume":"55","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4080","DOI":"10.1109\/TGRS.2020.3024971","article-title":"Impact of Soil Permittivity and Temperature Profile on L-Band Microwave Emission of Frozen Soil","volume":"59","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1109\/TGRS.2008.917214","article-title":"Soil Moisture Retrieval during a Corn Growth Cycle Using L-Band (1.6 GHz) Radar Observations","volume":"46","author":"Joseph","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.1016\/j.rse.2010.05.017","article-title":"Effects of corn on C- and L-band radar backscatter: A correction method for soil moisture retrieval","volume":"114","author":"Joseph","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"151","DOI":"10.5194\/hess-15-151-2011","article-title":"Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR","volume":"15","author":"Lievens","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.1080\/01431161.2015.1103920","article-title":"Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data","volume":"36","author":"Bai","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111433","DOI":"10.1016\/j.rse.2019.111433","article-title":"A multi-frequency framework for soil moisture retrieval from time series radar data","volume":"235","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112099","DOI":"10.1016\/j.rse.2020.112099","article-title":"Stochastic ensemble methods for multi-SAR-mission soil moisture retrieval","volume":"251","author":"Zhu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111237","DOI":"10.1016\/j.rse.2019.111237","article-title":"Soil moisture retrieval from time series multi-angular radar data using a dry down constraint","volume":"231","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_44","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":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_45","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."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Han, Y., Bai, X., Shao, W., and Wang, J. (2020). Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields. Water, 12.","DOI":"10.3390\/w12061726"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1080\/01431160110107671","article-title":"An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils","volume":"23","author":"Baghdadi","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.1080\/01431160310001654392","article-title":"Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements","volume":"25","author":"Baghdadi","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01431160500212278","article-title":"Calibration of the Integral Equation Model for SAR data in C-band and HH and VV polarizations","volume":"27","author":"Baghdadi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2019.02.027","article-title":"Roughness and vegetation change detection: A pre-processing for soil moisture retrieval from multi-temporal SAR imagery","volume":"225","author":"Zhu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/LGRS.2013.2264159","article-title":"A Bayesian Change Detection Approach for Retrieval of Soil Moisture Variations Under Different Roughness Conditions","volume":"11","author":"Notarnicola","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2012.2186971","article-title":"Validation of the SMOS L2 Soil Moisture Data in the REMEDHUS Network (Spain)","volume":"50","author":"Sanchez","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2782","DOI":"10.1109\/TGRS.2019.2955542","article-title":"A Physically Based Soil Moisture Index From Passive Microwave Brightness Temperatures for Soil Moisture Variation Monitoring","volume":"58","author":"Zeng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Cui, C., Xu, J., Zeng, J., Chen, K.-S., Bai, X., Lu, H., Chen, Q., and Zhao, T. (2017). Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales. Remote Sens., 10.","DOI":"10.3390\/rs10010033"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/01431169608949001","article-title":"Cover A colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981\u20131992)","volume":"17","author":"Verhoef","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TGRS.1985.289498","article-title":"Microwave dielectric behavior of wet soil\u2014Part II: Dielectric mixing models","volume":"23","author":"Dobson","year":"1985","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1109\/TGRS.2016.2629759","article-title":"A Comprehensive Analysis of Rough Soil Surface Scattering and Emission Predicted by AIEM With Comparison to Numerical Simulations and Experimental Measurements","volume":"55","author":"Zeng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.rse.2014.08.031","article-title":"Combined use of active and passive microwave satellite data to constrain a discrete scattering model","volume":"155","author":"Dente","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"113308","DOI":"10.1016\/j.rse.2022.113308","article-title":"Simulation of Sentinel-1A observations and constraint of water cloud model at the regional scale using a discrete scattering model","volume":"283","author":"Bai","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/S0034-4257(99)00036-X","article-title":"A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data","volume":"70","author":"Wagner","year":"1999","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:28:14Z","timestamp":1760146094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,28]]},"references-count":60,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236020"],"URL":"https:\/\/doi.org\/10.3390\/rs14236020","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,28]]}}}