{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T23:53:41Z","timestamp":1778198021584,"version":"3.51.4"},"reference-count":86,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The objective of this paper was to estimate soil moisture in pepper crops with drip irrigation in a semi-arid area in the center of Tunisia using synthetic aperture radar (SAR) data. Within this context, the sensitivity of L-band (ALOS-2) in horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations and C-band (Sentinel-1) data in vertical-vertical (VV) and vertical-horizontal (VH) polarizations is examined as a function of soil moisture and vegetation properties using statistical correlations. SAR signals scattered by pepper-covered fields are simulated with a modified version of the water cloud model using L-HH and C-VV data. In spatially heterogeneous soil moisture cases, the total backscattering is the sum of the bare soil contribution weighted by the proportion of bare soil (one-cover fraction) and the vegetation fraction cover contribution. The vegetation fraction contribution is calculated as the volume scattering contribution of the vegetation and underlying soil components attenuated by the vegetation cover. The underlying soil is divided into irrigated and non-irrigated parts owing to the presence of drip irrigation, thus generating different levels of moisture underneath vegetation. Based on signal sensitivity results, the potential of L-HH data to retrieve soil moisture is demonstrated. L-HV data exhibit a higher potential to retrieve vegetation properties regarding a lower potential for soil moisture estimation. After calibration and validation of the proposed model, various simulations are performed to assess the model behavior patterns under different conditions of soil moisture and pepper biophysical properties. The results highlight the potential of the proposed model to simulate a radar signal over heterogeneous soil moisture fields using L-HH and C-VV data.<\/jats:p>","DOI":"10.3390\/s22020580","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T23:17:07Z","timestamp":1642029427000},"page":"580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8660-2437","authenticated-orcid":false,"given":"Emna","family":"Ayari","sequence":"first","affiliation":[{"name":"CESBIO (CNRS\/UPS\/IRD\/CNES\/INRAE), 18 Av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France"},{"name":"National Agronomic Institute of Tunisia, Carthage University, LR17AGR01 InteGRatEd Management of Natural Resources: remoTE Sensing, Spatial Analysis and Modeling (GREEN-TEAM), Tunis 1082, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeineb","family":"Kassouk","sequence":"additional","affiliation":[{"name":"National Agronomic Institute of Tunisia, Carthage University, LR17AGR01 InteGRatEd Management of Natural Resources: remoTE Sensing, Spatial Analysis and Modeling (GREEN-TEAM), Tunis 1082, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zohra","family":"Lili-Chabaane","sequence":"additional","affiliation":[{"name":"National Agronomic Institute of Tunisia, Carthage University, LR17AGR01 InteGRatEd Management of Natural Resources: remoTE Sensing, Spatial Analysis and Modeling (GREEN-TEAM), Tunis 1082, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"CIRAD, CNRS, INRAE, TETIS, University of Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"CESBIO (CNRS\/UPS\/IRD\/CNES\/INRAE), 18 Av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/02508060208686989","article-title":"Global Water Demand and Supply Projections: Part 1. A Modeling Approach","volume":"27","author":"Cai","year":"2002","journal-title":"Water Int."},{"key":"ref_2","unstructured":"FAO, FIDA, OMS, and PAM et UNICEF (2021). L\u2019\u00c9tat de la S\u00e9curit\u00e9 Alimentaire et de la Nutrition dans le Monde 2021. Transformer les Syst\u00e8mes Alimentaires Pour que la S\u00e9curit\u00e9 Alimentaire, une Meilleure Nutrition et une Alimentation Saine et Abordable Soient une R\u00e9alit\u00e9 Pour Tous, FAO."},{"key":"ref_3","first-page":"270","article-title":"IoT-based monitoring and data-driven modelling of drip irrigation system for mustard leaf cultivation experiment","volume":"8","author":"Abioye","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103348","DOI":"10.1016\/j.earscirev.2020.103348","article-title":"Challenges for drought assessment in the Mediterranean region under future climate scenarios","volume":"210","author":"Tramblay","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/S0378-3774(02)00075-6","article-title":"Irrigation management under water scarcity","volume":"57","author":"Pereira","year":"2002","journal-title":"Agric. Water Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"eaaz6031","DOI":"10.1126\/sciadv.aaz6031","article-title":"Global agricultural economic water scarcity","volume":"6","author":"Rosa","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.3390\/rs2092274","article-title":"Remote sensing of irrigated agriculture: Opportunities and challenges","volume":"2","author":"Amiri","year":"2010","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Massari, C., Modanesi, S., Dari, J., Gruber, A., De Lannoy, G.J.M., Girotto, M., Quintana-Segu\u00ed, P., Le Page, M., Jarlan, L., and Zribi, M. (2021). A review of irrigation information retrievals from space and their utility for users. Remote Sens., 13.","DOI":"10.3390\/rs13204112"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Motte, E., Zribi, M., Fanise, P., Egido, A., Darrozes, J., Al-Yaari, A., Baghdadi, N., Baup, F., Dayau, S., and Fieuzal, R. (2016). GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring. Sensors, 16.","DOI":"10.3390\/s16050732"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Zribi, M., and Bazzi, H. (2017). Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens., 9.","DOI":"10.3390\/rs9121292"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1109\/TGRS.2007.904582","article-title":"A method for soil moisture estimation in Western Africa based on the ERS scatterometer","volume":"46","author":"Zribi","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sekertekin, A., Marangoz, A.M., Abdikan, S., and Esetlili, M.T. (2016, January 16\u201317). Preliminary results of estimating soil moisture over bare soil using full-polarimetric ALOS-2 data. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u2014ISPRS Archives, Istanbul, Turkey.","DOI":"10.5194\/isprs-archives-XLII-2-W1-173-2016"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.rse.2003.12.001","article-title":"Soil moisture estimation in a semiarid rangeland using ERS-2 and TM imagery","volume":"90","author":"Wang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2010.07.011","article-title":"Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data","volume":"115","author":"Gherboudj","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Attarzadeh, R., Amini, J., Notarnicola, C., and Greifeneder, F. (2018). Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at plot scale. Remote Sens., 10.","DOI":"10.3390\/rs10081285"},{"key":"ref_16","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 modi fi ed water-cloud model","volume":"72","author":"Bao","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","first-page":"101933","article-title":"Synthetic aperture radar and optical satellite data for estimating the biomass of corn","volume":"83","author":"Hosseini","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zribi, M., Muddu, S., Bousbih, S., Al Bitar, A., Tomer, S.K., Baghdadi, N., and Bandyopadhyay, S. (2019). Analysis of L-band SAR data for soil moisture estimations over agricultural areas in the tropics. Remote Sens., 11.","DOI":"10.3390\/rs11091122"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., Fayad, I., Zribi, M., Belhouchette, H., and Demarez, V. (2020). Near real-time irrigation detection at plot scale using sentinel-1 data. Remote Sens., 12.","DOI":"10.3390\/rs12091456"},{"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. (2020). 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","unstructured":"Zhang, L., Lv, X., Chen, Q., Sun, G., and Yao, J. (2020). Estimation of surface soil moisture during corn growth stage from SAR and optical data using a combined scattering model. Remote Sens., 12.","DOI":"10.3390\/rs12111844"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/JSTARS.2020.3033132","article-title":"A New Reflectivity Index for the Retrieval of Surface Soil Moisture from Radar Data","volume":"14","author":"Zribi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.5194\/hess-16-1607-2012","article-title":"Estimation of soil parameters over bare agriculture areas from C-band polarimetric SAR data using neural networks","volume":"16","author":"Baghdadi","year":"2012","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3844","DOI":"10.1109\/TGRS.2012.2185934","article-title":"A potential use for the C-band polarimetric SAR parameters to characterize the soil surface over bare agriculture fields","volume":"50","author":"Baghdadi","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1109\/JSTARS.2012.2220124","article-title":"Toward an operational bare soil moisture mapping using terrasar-x data acquired over agricultural areas","volume":"6","author":"Aubert","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/JSTARS.2015.2464698","article-title":"Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval over Irrigated Grasslands","volume":"9","author":"Baghdadi","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.agwat.2016.05.017","article-title":"Integration of remote sensing derived parameters in crop models: Application to the PILOTE model for hay production","volume":"176","author":"Baghdadi","year":"2016","journal-title":"Agric. Water Manag."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., and Baghdadi, N. (2017). Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors, 17.","DOI":"10.3390\/s17091966"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0034-4257(00)00082-1","article-title":"Validation of a Rough Surface Model Based on Fractional Brownian Geometry with SIRC and ERASME Radar Data over Orgeval","volume":"73","author":"Zribi","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1080\/01431169408954211","article-title":"Radar Response of Periodic Vegetation Canopies","volume":"15","author":"Whitt","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar Remote Sensing of Agricultural Canopies: A Review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1109\/36.7711","article-title":"Electromagnetic Wave Scattering from Some Vegetation Samples","volume":"26","author":"Karam","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/0034-4257(95)00048-6","article-title":"A microwave polarimetric scattering model for forest canopies based on vector radiative transfer theory","volume":"53","author":"Karam","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_35","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_36","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 Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1080\/01431161.2010.483486","article-title":"The development of HJ SAR soil moisture retrieval algorithm","volume":"31","author":"Du","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wei\u00df, T., Ramsauer, T., L\u00f6w, A., and Marzahn, P. (2020). Evaluation of Different Radiative Transfer Models for Microwave Backscatter Estimation of Wheat Fields. Remote Sens., 12.","DOI":"10.3390\/rs12183037"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0034-4257(84)90010-5","article-title":"Relating the microwave backscattering coefficient to leaf area index","volume":"14","author":"Ulaby","year":"1984","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.asr.2006.02.032","article-title":"Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis","volume":"39","author":"Inoue","year":"2007","journal-title":"Adv. Space Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1080\/02626667.2012.678583","article-title":"Estimation of water cloud model vegetation parameters using a genetic algorithm","volume":"57","author":"Kumar","year":"2012","journal-title":"Hydrol. Sci. J."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Baghdadi, N. (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_44","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., and Mougenot, B. (2017). Potential of sentinel-1 radar data for the assessment of soil and cereal cover parameters. Sensors, 17.","DOI":"10.3390\/s17112617"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Ouaadi, N., Jarlan, L., Ezzahar, J., Khabba, S., Le Dantec, V., Rafi, Z., Zribi, M., and Frison, P.L. (2020, January 9\u201311). Water Stress Detection over Irrigated Wheat Crops in Semi-Arid Areas Using the Diurnal Differences of Sentinel-1 Backscatter. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105171"},{"key":"ref_47","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_48","first-page":"47","article-title":"Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing","volume":"80","author":"Qiu","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ayari, E., Kassouk, Z., Lili-Chabaane, Z., Baghdadi, N., Bousbih, S., and Zribi, M. (2021). Cereal crops soil parameters retrieval using L-band ALOS-2 and C-band sentinel-1 sensors. Remote Sens., 13.","DOI":"10.3390\/rs13071393"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"Ouaadi, N., Jarlan, L., Ezzahar, J., Zribi, M., Khabba, S., Bouras, E., and Frison, P.L. (2020, January 9\u201311). Surface Soil Moisture Retrieval over Irrigated Wheat Crops in Semi-Arid Areas Using Sentinel-1 Data. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105282"},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2016.01.027","article-title":"Soil moisture retrieval over irrigated grassland using X-band SAR data","volume":"176","author":"Baghdadi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_54","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. Geogr."},{"key":"ref_55","first-page":"1","article-title":"Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2","volume":"24","author":"Gao","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I., R\u00fcdiger, C., and Strauss, P. (2018). Sensitivity of Sentinel-1 backscatter to vegetation dynamics: An Austrian case study. Remote Sens., 10.","DOI":"10.3390\/rs10091396"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gorrab, A., Ameline, M., Albergel, C., and Baup, F. (2021). Use of sentinel-1 multi-configuration and multi-temporal series for monitoring parameters of winter wheat. Remote Sens., 13.","DOI":"10.3390\/rs13040553"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., El Hajj, M., and Baghdadi, N. (2018). Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1. Remote Sens., 22.","DOI":"10.3390\/rs10121953"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"8128","DOI":"10.3390\/rs70608128","article-title":"Retrieval and multi-scale validation of Soil Moisture from multi-temporal SAR Data in a semi-arid tropical region","volume":"7","author":"Tomer","year":"2015","journal-title":"Remote Sens."},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/JSTARS.2013.2257698","article-title":"A prototype software package to retrieve soil moisture from sentinel-1 data by using a bayesian multitemporal algorithm","volume":"7","author":"Pierdicca","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","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":"Baghdadi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_64","first-page":"14","article-title":"Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks","volume":"57","author":"Fieuzal","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Bazzi, H., and Zribi, M. (2019). Penetration analysis of SAR signals in the C and L bands for wheat, maize, and grasslands. Remote Sens., 11.","DOI":"10.3390\/rs11010031"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Mirsoleimani, H.R., Sahebi, M.R., Baghdadi, N., and El Hajj, M. (2019). Bare soil surface moisture retrieval from sentinel-1 SAR data based on the calibrated IEM and dubois models using neural networks. Sensors, 19.","DOI":"10.3390\/s19143209"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hamze, M., Baghdadi, N., El Hajj, M.M., Zribi, M., Bazzi, H., Cheviron, B., and Faour, G. (2021). Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data. Remote Sens., 13.","DOI":"10.3390\/rs13112102"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4089","DOI":"10.1080\/01431160110115924","article-title":"SAR-based estimation of areal aboveground biomass (AAB) of herbaceous vegetation in the semi-arid zone: A modification of the water-cloud model","volume":"23","author":"Svoray","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Xing, M., He, B., Ni, X., Wang, J., An, G., Shang, J., and Huang, X. (2019). Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11161956"},{"key":"ref_70","unstructured":"Davidson, M., Chini, M., Dierking, W., Djavidnia, S., Haarpaintner, J., Hajduch, G., Laurin, G.V., Lavalle, M., L\u00f3pez-Martinez, C., and Nagler, T. (2019). Copernicus L-band SAR Mission Requirements Document, ESA. ESA-EOPSM-CLIS-MRD-3371."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Amiri, Z., Gheysari, M., Mosaddeghi, M.R., Amiri, S., and Tabatabaei, M.S. (Inf. Process. Agric., 2021). An Attempt to Find a Suitable Place for Soil Moisture Sensor in a Drip Irrigation System, Inf. Process. Agric., in press.","DOI":"10.1016\/j.inpa.2021.04.010"},{"key":"ref_72","unstructured":"Amri, R. (2013). Estimation R\u00e9gionale de L\u2019\u00e9vapotranspiration sur la Plaine de Kairouan (Tunisie) \u00e0 Partir de Donn\u00e9es Satellites Multi-capteurs \u00c9cole. [Ph.D. Thesis, Universit\u00e9 Paul Sabatier]."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1623\/hysj.52.6.1162","article-title":"Impacts of hydrological changes in the Mediterranean zone: Environmental modifications and rural development in the Merguellil catchment, central Tunisia","volume":"52","author":"Leduc","year":"2007","journal-title":"Hydrol. Sci. J.\/J. Des Sci. Hydrol."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., Pelletier, C., Gorrab, A., Lili-Chabaane, Z., Baghdadi, N., Aissa, N.B., and Mougenot, B. (2019). Soil texture estimation using radar and optical data from Sentinel-1 and Sentinel-2. Remote Sens., 11.","DOI":"10.3390\/rs11131520"},{"key":"ref_76","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Texas A & M University, Remote Sensing Center. Progress Report RSC 1978-1."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1080\/014311698215333","article-title":"The derivation of the green vegetation fraction from NOAA\/AVHRR data for use in numerical weather prediction models","volume":"19","author":"Gutman","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TAP.1975.1140999","article-title":"Radar Response to Vegetation","volume":"23","author":"Ulaby","year":"1975","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_79","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 Trans. Geosci. Remote Sens."},{"key":"ref_80","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_81","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1080\/01431160500239032","article-title":"Soil moisture estimation using multi-incidence and multi-polarization ASAR data","volume":"27","author":"Baghdadi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/LGRS.2010.2050054","article-title":"Semiempirical calibration of the integral equation model for SAR data in C-Band and cross polarization using radar images and field measurements","volume":"8","author":"Baghdadi","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"13626","DOI":"10.3390\/rs71013626","article-title":"Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering","volume":"7","author":"Baghdadi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/S0034-4257(01)00343-1","article-title":"Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables","volume":"81","author":"Inoue","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1109\/36.917914","article-title":"The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops","volume":"39","author":"Macelloni","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_86","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:28:44Z","timestamp":1760365724000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":86,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020580"],"URL":"https:\/\/doi.org\/10.3390\/s22020580","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]}}}