{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T12:53:43Z","timestamp":1769604823459,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French Space Study Center (CNES, TOSCA 2022 project)"},{"name":"French Agency for Ecological Transition (ADEME, RSEAU project)"},{"name":"National Research Institute for Agriculture, Food and the Environment (INRAE)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The radar-backscattering coefficient (\u03c30) depends on surface characteristics and instrumental parameters (wavelength, polarization, and incidence angle). For Sentinel-1 (S1), with incidence angles ranging from 25\u00b0 to 45\u00b0, \u03c30 for similar targets typically differs by a few dB depending on their localization in the S1 swath. Overcoming this angular dependence is crucial for the operational applications of radar data. In theory, \u03c30 follows a cosine function with an exponent \u201cN\u201d that represents the degree of dependence between \u03c30 and the incidence angle. In order to reduce the effect of the incidence angle on \u03c30, dynamic N normalizations based on vegetation descriptors, NDVI and SAR Ratio (VV\/VH), were applied and then compared to the results obtained with temporally fixed N normalizations. N was estimated at each S1 date during the period of the study for three main summer crops: corn, soybean, and sunflower. Analysis shows that the angular dependence of the S1 \u03c30 is similar for all three crops. N varies from 3.0 for low NDVI values to 2.0 for high NDVI values (stage of maximal vegetation development) in the VV polarization and from 2.5 to 1.5 for the VH polarization. Furthermore, N fluctuates strongly during the periods before plant emergence and after harvesting, due to variations in the soil roughness. Finally, the results demonstrated that the dynamic normalization of \u03c30 significantly reduces its angular dependence compared to fixed N (N = 1 and N = 2), with SAR ratio-based normalization performing similarly to NDVI-based normalization.<\/jats:p>","DOI":"10.3390\/rs16203838","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T04:45:35Z","timestamp":1729485935000},"page":"3838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Incidence Angle Normalization of C-Band Radar Backscattering Coefficient over Agricultural Surfaces Using Dynamic Cosine Method"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3312-752X","authenticated-orcid":false,"given":"Sami","family":"Najem","sequence":"first","affiliation":[{"name":"National Research Institute for Agriculture, Food and Environment (INRAE), UMR TETIS, University of Montpellier, 500 rue Fran\u00e7ois Breton, 34093 Montpellier, CEDEX 5, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"National Research Institute for Agriculture, Food and Environment (INRAE), UMR TETIS, University of Montpellier, 500 rue Fran\u00e7ois Breton, 34093 Montpellier, CEDEX 5, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5510-1832","authenticated-orcid":false,"given":"Hassan","family":"Bazzi","sequence":"additional","affiliation":[{"name":"National Research Institute for Agriculture, Food and Environment (INRAE), UMR TETIS, University of Montpellier, 500 rue Fran\u00e7ois Breton, 34093 Montpellier, CEDEX 5, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"CESBIO, UT3, CNES, CNRS, INRAE, IRD, 18 Avenue Edouard Belin, 31401 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isprsjprs.2015.05.001","article-title":"TerraSAR-X dual-pol time-series for mapping of wetland vegetation","volume":"107","author":"Betbeder","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8884","DOI":"10.1109\/JSTARS.2023.3316304","article-title":"Artificial Intelligence Algorithms for Rapeseed Fields Mapping Using Sentinel-1 Time Series: Temporal Transfer Scenario and Ground Sampling Constraints","volume":"16","author":"Maleki","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1109\/JSTARS.2023.3337989","article-title":"Detection and Mapping of Cover Crops using Sentinel-1 SAR Remote Sensing data","volume":"17","author":"Najem","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., Najem, S., Jaafar, H., Le Page, M., Zribi, M., Faraslis, I., and Spiliotopoulos, M. (2022). Detecting Irrigation Events over Semi-Arid and Temperate Climatic Areas Using Sentinel-1 Data: Case of Several Summer Crops. Agronomy, 12.","DOI":"10.3390\/agronomy12112725"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3366","DOI":"10.1109\/JSTARS.2019.2927430","article-title":"A Comparison of Two Soil Moisture Products S2MP and Copernicus-SSM Over Southern France","volume":"12","author":"Bazzi","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Dimov, D., L\u00f6w, F., Ibrakhimov, M., Stulina, G., and Conrad, C. (2017, January 23\u201328). SAR and optical time series for crop classification. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127076"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1109\/36.885195","article-title":"Estimation of snow water equivalence using SIR-C\/X-SAR. I. Inferring snow density and subsurface properties","volume":"38","author":"Shi","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1109\/36.317448","article-title":"Modeling L-band radar backscatter of Alaskan boreal forest","volume":"31","author":"Wang","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/JSTARS.2020.2977506","article-title":"First-Year and Multiyear Sea Ice Incidence Angle Normalization of Dual-Polarized Sentinel-1 SAR Images in the Beaufort Sea","volume":"13","author":"Aldenhoff","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.rse.2005.04.005","article-title":"New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion","volume":"96","author":"Zribi","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2015.06.005","article-title":"Sensitivity of C-band synthetic aperture radar polarimetric parameters to snow thickness over landfast smooth first-year sea ice","volume":"166","author":"Gill","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6170","DOI":"10.1109\/TGRS.2017.2721981","article-title":"Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery Over the Kara Sea","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9109","DOI":"10.1109\/TGRS.2019.2924868","article-title":"Detection of First-Year and Multi-Year Sea Ice from Dual-Polarization SAR Images Under Cold Conditions","volume":"57","author":"Komarov","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1109\/TGRS.2002.806991","article-title":"Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice","volume":"40","author":"Makynen","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Paluba, D., La\u0161tovi\u010dka, J., Mouratidis, A., and \u0160tych, P. (2021). Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems. Remote Sens., 13.","DOI":"10.3390\/rs13091743"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1109\/TGRS.2012.2205264","article-title":"Incidence Angle Normalization of Radar Backscatter Data","volume":"51","author":"Mladenova","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Feng, Z., Zheng, X., Li, L., Li, B., Chen, S., Guo, T., Wang, X., Jiang, T., Li, X., and Li, X. (2021). Dynamic Cosine Method for Normalizing Incidence Angle Effect on C-band Radar Backscattering Coefficient for Maize Canopies Based on NDVI. Remote Sens., 13.","DOI":"10.3390\/rs13152856"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2014.08.037","article-title":"Multi-model radiometric slope correction of SAR images of complex terrain using a two-stage semi-empirical approach","volume":"156","author":"Hoekman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2120616","article-title":"Flattening Gamma: Radiometric Terrain Correction for SAR Imagery","volume":"49","author":"Small","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, L., Chen, E., Li, Z., Zhang, W., and Gu, X. (2017). Three-Step Semi-Empirical Radiometric Terrain Correction Approach for PolSAR Data Applied to Forested Areas. Remote Sens., 9.","DOI":"10.3390\/rs9030269"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1109\/TGRS.2015.2513159","article-title":"Incidence Angle Correction of SAR Sea Ice Data Based on Locally Linear Mapping","volume":"54","author":"Lang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Widhalm, B., Bartsch, A., and Goler, R. (2018). Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments. Remote Sens., 10.","DOI":"10.3390\/rs10040551"},{"key":"ref_24","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1982). Microwave Remote Sensing: Active and Passive. Volume 2-Radar Remote Sensing and Surface Scattering and Emission Theory, Artech House."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1515\/geo-2016-0029","article-title":"Incidence angle normalization of Wide Swath SAR data for oceanographic applications","volume":"8","author":"Topouzelis","year":"2016","journal-title":"Open Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1109\/LGRS.2010.2048411","article-title":"Angular Backscatter Variation in L-Band ALOS ScanSAR Images of Tropical Forest Areas","volume":"7","author":"Ardila","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","unstructured":"Clapp, R.E. (1946). A Theoretical and Experimental Study of Radar Ground Return, Radiation Laboratory, Massachusetts Institute of Technology."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/17538947.2014.878969","article-title":"Digital Earth: Big Earth Data","volume":"7","author":"Guo","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"345","DOI":"10.5194\/hess-15-345-2011","article-title":"Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation","volume":"15","author":"Zribi","year":"2011","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/014311601750038857","article-title":"Evaluation of C-band SAR data for wetlands mapping","volume":"22","author":"Baghdadi","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1109\/LGRS.2013.2294725","article-title":"Seasonality in the Angular Dependence of ASAR Wide Swath Backscatter","volume":"11","author":"Wagner","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"162","DOI":"10.4236\/ars.2013.22020","article-title":"Monitoring Wheat and Rapeseed by Using Synchronous Optical and Radar Satellite Data\u2014From Temporal Signatures to Crop Parameters Estimation","volume":"2","author":"Fieuzal","year":"2013","journal-title":"Adv. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1109\/JSTARS.2020.2993067","article-title":"Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images","volume":"13","author":"Cristea","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/36.298018","article-title":"An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the NDVI for the MODIS-EOS","volume":"32","author":"Huete","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1080\/014311600210830","article-title":"Monitoring vegetation cover across semi-arid regions: Comparison of remote observations from various scales","volume":"21","author":"Leprieur","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.asr.2019.09.034","article-title":"Monitoring of maize lodging using multi-temporal Sentinel-1 SAR data","volume":"65","author":"Shu","year":"2020","journal-title":"Adv. Space Res."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"2357878","DOI":"10.1080\/15481603.2024.2357878","article-title":"NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region","volume":"61","author":"Ayari","year":"2024","journal-title":"GIScience Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rolle, M., Tamea, S., Claps, P., Ayari, E., Baghdadi, N., and Zribi, M. (2022). Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy. Remote Sens., 14.","DOI":"10.3390\/rs14153712"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/36.285197","article-title":"Snow mapping in alpine regions with synthetic aperture radar","volume":"32","author":"Shi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"564","article-title":"Growing degree days and yield relationship in sunflower (Helianthus annuus L.)","volume":"9","author":"Qadir","year":"2007","journal-title":"Int. J. Agric. Biol. Pak."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10002","DOI":"10.3390\/rs61010002","article-title":"Irrigated Grassland Monitoring Using a Time Series of TerraSAR-X and COSMO-SkyMed X-Band SAR Data","volume":"6","author":"Hajj","year":"2014","journal-title":"Remote Sens."},{"key":"ref_43","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3838\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:14:14Z","timestamp":1760112854000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/20\/3838"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"references-count":43,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16203838"],"URL":"https:\/\/doi.org\/10.3390\/rs16203838","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}