{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T08:43:05Z","timestamp":1769503385980,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French Space Study Center"},{"name":"National Research Institute for Agriculture, Food and the Environment"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Comprehensive knowledge about irrigation timing is crucial for water resource management. This paper presents a comparative analysis between C- and L-band Synthetic Aperture Radar (SAR) data for the detection of irrigation events. The analysis was performed using C-band time series data derived from the Sentinel-1 (S1) satellite and two L-band images from the PALSAR-2 (ALOS-2) sensor acquired over irrigated grassland plots in the Crau plain of southeast France. The S1 C-band time series was first analyzed as a function of rainfall and irrigation events. The backscattering coefficients in both the L and C bands were then compared to the time difference between the date of the acquired SAR image and the date of the last irrigation event occurring before the SAR acquisition (\u0394t). Sensitivity analysis was performed for 2 classes of the Normalized Difference Vegetation Index (NDVI \u22640.7 and NDVI &gt;0.7). The main results showed that when the vegetation is moderately developed (NDVI \u22640.7), the C-band temporal variation remains sensitive to the soil moisture dynamics and the irrigation events could be detected. The C-VV signal decreases due to the drying out of the soil when the time difference between the S1 image and irrigation event increases. For well-developed vegetation cover (NDVI &gt;0.7), the C-band sensitivity to irrigation events becomes dependent on the crop type. For well-developed Gramineae grass with longs stalks and seedheads, the C band shows no correlation with \u0394t due to the absence of the soil contribution in the backscattered signal, contrary to the legume grass type, where the C band shows a good correspondence between C-VV and \u0394t for NDVI &gt; 0.7. In contrast, analysis of the L-band backscattering coefficient shows that the L band remains sensitive to the soil moisture regardless of the vegetation cover development and the vegetation characteristics, thus being more suitable for irrigation detection than the C band. The L-HH signal over Gramineae grass or legume grass types shows the same decreasing pattern with the increase in \u0394t, regardless of the NDVI-values, presenting a decrease in soil moisture with time and thus high sensitivity of the radar signal to soil parameters. Finally, the co-polarizations for both the C and L bands (L-HH and C-VV) tend to be more adequate for irrigation detection than the HV cross-polarization, as they show higher sensitivity to soil moisture values.<\/jats:p>","DOI":"10.3390\/rs14102312","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T23:08:36Z","timestamp":1652396916000},"page":"2312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Comparative Analysis of the Sensitivity of SAR Data in C and L Bands for the Detection of Irrigation Events"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5510-1832","authenticated-orcid":false,"given":"Hassan","family":"Bazzi","sequence":"first","affiliation":[{"name":"INRAE, UMR TETIS, University of Montpellier, AgroParisTech, 500 Rue Fran\u00e7ois Breton, CEDEX 5, 34093 Montpellier, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9461-4120","authenticated-orcid":false,"given":"Nicolas","family":"Baghdadi","sequence":"additional","affiliation":[{"name":"INRAE, UMR TETIS, University of Montpellier, AgroParisTech, 500 Rue Fran\u00e7ois Breton, CEDEX 5, 34093 Montpellier, France"}]},{"given":"Fran\u00e7ois","family":"Charron","sequence":"additional","affiliation":[{"name":"University of Montpellier, G-EAU Unit, AgroParisTech, CIRAD, INRAE, Institut Agro, IRD, Domaine du Merle, 13300 Salon de Provence, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6141-8222","authenticated-orcid":false,"given":"Mehrez","family":"Zribi","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES\/CNRS\/INRAE\/IRD\/UPS, 18 Av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"ref_1","unstructured":"Food and Agriculture Organization (FAO) (2017). Water for Sustainable Food and Agriculture, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7791","DOI":"10.1029\/2018WR022792","article-title":"The Effect of Global Warming on Future Water Availability: CMIP5 Synthesis","volume":"54","author":"Ferguson","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Tejero, I.F., Dur\u00e1n-Zuazo, V.H., Muriel-Fern\u00e1ndez, J.L., and Rodr\u00edguez-Pleguezuelo, C.R. (2011). Water and Sustainable Agriculture. Water and Sustainable Agriculture, Springer.","DOI":"10.1007\/978-94-007-2091-6"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.gloplacha.2012.06.004","article-title":"Current and Future Irrigation Water Requirements in Pan-Europe: An Integrated Analysis of Socio-Economic and Climate Scenarios","volume":"94\u201395","author":"Schaldach","year":"2012","journal-title":"Glob. Planet. Chang."},{"key":"ref_6","first-page":"102216","article-title":"Use of Sentinel-2 MSI Data to Monitor Crop Irrigation in Mediterranean Areas","volume":"93","author":"Maselli","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5194\/hess-9-535-2005","article-title":"Development and Validation of the Global Map of Irrigation Areas","volume":"9","author":"Siebert","year":"2005","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3520","DOI":"10.1016\/j.rse.2008.04.010","article-title":"A New Methodology to Map Irrigated Areas Using Multi-Temporal MODIS and Ancillary Data: An Application Example in the Continental US","volume":"112","author":"Ozdogan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_9","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_10","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_11","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M., Baghdadi, N., and Segui, P. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pageot, Y., Baup, F., Inglada, J., Baghdadi, N., and Demarez, V. (2020). Detection of Irrigated and Rainfed Crops in Temperate Areas Using Sentinel-1 and Sentinel-2 Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12183044"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Le Page, M., Jarlan, L., El Hajj, M.M., Zribi, M., Baghdadi, N., and Boone, A. (2020). Potential for the Detection of Irrigation Events on Maize Plots Using Sentinel-1 Soil Moisture Products. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-8588"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., Fayad, I., Charron, F., Zribi, M., and Belhouchette, H. (2020). Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data. Remote Sens., 12.","DOI":"10.3390\/rs12244058"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4370","DOI":"10.1016\/j.rse.2008.08.004","article-title":"Analysis of TerraSAR-X Data and Their Sensitivity to Soil Surface Parameters over Bare Agricultural Fields","volume":"112","author":"Baghdadi","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","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_18","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_19","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_20","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_21","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., Ienco, D., El Hajj, M., Zribi, M., Belhouchette, H., Escorihuela, M.J., and Demarez, V. (2019). Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain. Remote Sens., 11.","DOI":"10.3390\/rs11151836"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/LGRS.2019.2960625","article-title":"Distilling Before Refine: Spatio-Temporal Transfer Learning for Mapping Irrigated Areas Using Sentinel-1 Time Series","volume":"17","author":"Bazzi","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bazzi, H., Baghdadi, N., El Hajj, M., and Zribi, M. (2019). Potential of Sentinel-1 Surface Soil Moisture Product for Detecting Heavy Rainfall in the South of France. Sensors, 19.","DOI":"10.3390\/s19040802"},{"key":"ref_24","unstructured":"Ulaby, F.T. (1982). Microwave Remote Sensing Active and Passive. Rader Remote Sensing and Surface Scattering and Emission Theory, Artech House Publishers."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"El Hajj, M., Baghdadi, N., Bazzi, H., and Zribi, M. (2018). 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_26","doi-asserted-by":"crossref","unstructured":"Nasrallah, A., Baghdadi, N., El Hajj, M., Darwish, T., Belhouchette, H., Faour, G., Darwich, S., and Mhawej, M. (2019). Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping. Remote Sens., 11.","DOI":"10.3390\/rs11192228"},{"key":"ref_27","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_28","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_29","first-page":"1871","article-title":"A Semi-Empirical Modelling Approach to Calculate Two-Way Attenuation in Radar Backscatter from Soil Due to Crop Cover","volume":"100","author":"Srivastava","year":"2011","journal-title":"Curr. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wei\u00df, T., Ramsauer, T., Jagdhuber, T., L\u00f6w, A., and Marzahn, P. (2021). Sentinel-1 Backscatter Analysis and Radiative Transfer Modeling of Dense Winter Wheat Time Series. Remote Sens., 13.","DOI":"10.3390\/rs13122320"},{"key":"ref_31","unstructured":"M\u00e9rot, A. (2007). Analyse et Mod\u00e9lisation Du Fonctionnement Biophysique et D\u00e9cisionnel d\u2019un Syst\u00e8me Prairial Irrigu\u00e9-Application Aux Prairies Plurisp\u00e9cifiques de Crau En Vue de l\u2019\u00e9laboration d\u2019un Outil d\u2019Aide \u00e0 La D\u00e9cision. [Ph.D. Thesis, Ecole Nationale Superieure Agronomique de Montpelli]."},{"key":"ref_32","first-page":"101888","article-title":"Comparative Analysis of the Accuracy of Surface Soil Moisture Estimation from the C- and L-Bands","volume":"82","author":"Baghdadi","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/TGRS.2003.813531","article-title":"Multitemporal C-Band Radar Measurements on Wheat Fields","volume":"41","author":"Mattia","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1109\/TGRS.2003.813353","article-title":"Understanding C-Band Radar Backscatter from Wheat Canopy Using a Multiple-Scattering Coherent Model","volume":"41","author":"Picard","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TGRS.2003.817200","article-title":"Wheat Cycle Monitoring Using Radar Data and a Neural Network Trained by a Model","volume":"42","author":"Ferrazzoli","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Baghdadi, N., El Hajj, M., Zribi, M., and Bousbih, S. (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_38","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2003.814132","article-title":"High-Resolution Measurements of Scattering in Wheat Canopies-Implications for Crop Parameter Retrieval","volume":"41","author":"Brown","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/36.841996","article-title":"Modeling Microwave Interactions with Crops and Comparison with ERS-2 SAR Observations","volume":"38","author":"Cookmartin","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","unstructured":"(2021, November 18). ALOS-2\/PALSAR-2. Available online: https:\/\/www.eorc.jaxa.jp\/ALOS-2\/en\/about\/palsar2.htm."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2312\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:03Z","timestamp":1760137743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/10\/2312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":40,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14102312"],"URL":"https:\/\/doi.org\/10.3390\/rs14102312","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,11]]}}}