{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:44:16Z","timestamp":1776444256063,"version":"3.51.2"},"reference-count":74,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Capturing and identifying field-based agricultural activities, such as the start, duration and end of irrigation, together with crop sowing\/germination, growing period and time of harvest, offer informative metrics that can assist in precision agricultural activities in addition to broader water and food security monitoring efforts. While optically based band-ratios, such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), have been used as descriptors for monitoring crop dynamics, data are not always available due to the influence of clouds and other atmospheric effects on optical sensors. Satellite-based microwave systems, such as the synthetic aperture radar (SAR), offer an all-weather advantage in monitoring soil and crop conditions. In this paper, we leverage the relative strengths of both optical- and microwave-based approaches by combining high resolution Sentinel-1 SAR and Sentinel-2 optical imagery to monitor irrigation events and crop dynamics in a dryland agricultural landscape. A microwave backscatter model was used to analyze the responses of simulated backscatters to soil moisture, NDVI and NDWI (both are correlated with vegetation water content and can be regarded as vegetation descriptors), allowing an empirical relationship between these two platforms. A correlation analysis was also performed using Sentinel-1 SAR and Sentinel-2 optical data over crops of maize, alfalfa, carrot and Rhodes grass in Al Kharj farm of Saudi Arabia to identify an appropriate SAR-based vegetation descriptor. The results illustrate the relationship between SAR and both NDVI and NDWI and demonstrated the relationship between the cross-polarization ratio (VH\/VV) and the two optical indices. We explore the capacity of this multi-platform and multi-sensor approach to inform on the spatio-temporal dynamics of a range of agricultural activities, which can be used to facilitate field-based management decisions.<\/jats:p>","DOI":"10.3390\/rs14051205","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T21:25:14Z","timestamp":1646169914000},"page":"1205","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Monitoring Irrigation Events and Crop Dynamics Using Sentinel-1 and Sentinel-2 Time Series"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2025-6030","authenticated-orcid":false,"given":"Chunfeng","family":"Ma","sequence":"first","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia"},{"name":"Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Heihe Remote Sensing Experimental Research Station of Chinese Academy of Sciences, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1889-9336","authenticated-orcid":false,"given":"Kasper","family":"Johansen","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5272","authenticated-orcid":false,"given":"Matthew F.","family":"McCabe","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0378-3774(82)90013-0","article-title":"Problems of Irrigated Agriculture in Al-Hassa, Saudi-Arabia","volume":"5","author":"Hussain","year":"1982","journal-title":"Agric. Water Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0011-9164(99)00076-4","article-title":"Wastewater quality and its reuse in agriculture in Saudi Arabia","volume":"123","author":"Hussain","year":"1999","journal-title":"Desalination"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17514","DOI":"10.1038\/srep17514","article-title":"Productivity and sustainability of rainfed wheat-soybean system in the North China Plain: Results from a long-term experiment and crop modelling","volume":"5","author":"Qin","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.agwat.2019.03.034","article-title":"Current and potential capabilities of UAS for crop water productivity in precision agriculture","volume":"218","author":"Ezenne","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.apgeog.2016.12.019","article-title":"Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dryland vegetation variability","volume":"79","author":"Zewdie","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1109\/JSTARS.2015.2398034","article-title":"Optical Sensing of Vegetation Water Content: A Synthesis Study","volume":"8","author":"Gao","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jonsson, P., Cai, Z.Z., Melaas, E., Friedl, M.A., and Eklundh, L. (2018). A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10040635"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.rse.2006.01.005","article-title":"Monitoning herbaceous biomass and water content with SPOT VEGETATION time-series to improve fire risk assessment in savanna ecosystems","volume":"101","author":"Verbesselt","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2005.07.008","article-title":"Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.rse.2003.10.021","article-title":"Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans","volume":"92","author":"Jackson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2005.03.008","article-title":"A crop phenology detection method using time-series MODIS data","volume":"96","author":"Sakamoto","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_12","first-page":"102670","article-title":"Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat\u2013MODIS data fusion","volume":"106","author":"Sisheber","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1109\/JSTARS.2021.3139155","article-title":"Incremental Knowledge Extraction from IoT-Based System for Anomaly Detection in Vegetation Crops","volume":"15","author":"Cavaliere","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","first-page":"102376","article-title":"Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine","volume":"102","author":"Pan","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_15","first-page":"602","article-title":"Vegetation water content retrieval using scatterometer data at X-band","volume":"33","author":"Gupta","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, J.H., and Wang, S.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_17","doi-asserted-by":"crossref","first-page":"5356","DOI":"10.1109\/TGRS.2019.2899120","article-title":"The Discrepancy between Backscattering Model Simulations and Radar Observations Caused by Scaling Issues: An Uncertainty Analysis","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1109\/TGRS.2018.2870188","article-title":"A Nonlinear Guided Filter for Polarimetric SAR Image Despeckling","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.1002\/2017WR022240","article-title":"CubeSats in hydrology: Ultrahigh-resolution insights into vegetation dynamics and terrestrial evaporation","volume":"53","author":"McCabe","year":"2017","journal-title":"Water Resour. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Rott","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/JSTARS.2012.2190136","article-title":"Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1","volume":"5","author":"Hornacek","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2011.09.028","article-title":"Development of a Global Backscatter Model in support to the Sentinel-1 mission design","volume":"120","author":"Sabel","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.rse.2018.06.035","article-title":"Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy","volume":"215","author":"Vanino","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","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_27","first-page":"520","article-title":"Toward Global Soil Moisture Monitoring with Sentinel-1: Harnessing Assets and Overcoming Obstacles","volume":"57","author":"Freeman","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","unstructured":"Kolecka, N., Ginzler, C., Pazur, R., Price, B., and Verburg, P.H. (2018). Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series. Remote Sens., 10.","DOI":"10.3390\/rs10081221"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.rse.2018.03.014","article-title":"Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island","volume":"215","author":"Vrieling","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pan, L., Xia, H., Zhao, X., Guo, Y., and Qin, Y. (2021). Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7\/8 Images, and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13132510"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, L.Y., Chen, J.S., Guo, S.X., Deng, X.P., and Han, Y. (2020). Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas. Remote Sens., 12.","DOI":"10.3390\/rs12010158"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Van Tricht, K., Gobin, A., Gilliams, S., and Piccard, I. (2018). Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0066.v1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Orynbaikyzy, A., Gessner, U., Mack, B., and Conrad, C. (2020). Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. Remote Sens., 12.","DOI":"10.3390\/rs12172779"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.3390\/rs5031389","article-title":"Investigating the relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI detection","volume":"5","author":"Capodici","year":"2013","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6749","DOI":"10.1038\/s41598-020-63560-0","article-title":"Could Vegetation index be Derive from Synthetic Aperture Radar?\u2013the Linear Relationship between interferometric coherence and nDVi","volume":"10","author":"Bai","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1023\/A:1015777403153","article-title":"Possible Effects of Global Warming on Agriculture and Water Resources in Saudi Arabia: Impacts and Responses","volume":"54","author":"Alkolibi","year":"2002","journal-title":"Clim. Chang."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.isprsjprs.2017.10.004","article-title":"A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning","volume":"135","author":"Houborg","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rse.2018.02.067","article-title":"A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data","volume":"209","author":"Houborg","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_45","first-page":"19548","article-title":"Sentinel-2 data exploitation with ESA\u2019s Sentinel-2 Toolbox","volume":"19","author":"Gascon","year":"2017","journal-title":"EGU Gen. Assem. Conf. Abstr."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1109\/LGRS.2018.2865816","article-title":"Sen4Rice: A Processing Chain for Differentiating Early and Late Transplanted Rice Using Time-Series Sentinel-1 SAR Data With Google Earth Engine","volume":"15","author":"Mandal","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A Processor for Users. Proceedings of the Living Planet Symposium 2016, Prague, Czech Republic."},{"key":"ref_48","unstructured":"Chen, D.Y., Jackson, T.J., Li, F., Cosh, M.H., Walthall, C., and Anderson, M. (2003, January 21\u201325). Estimation of vegetation water content for corn and Soybeans with a Normalized Difference Water Index (NDWI) using Landsat Thematic Mapper data. Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/S0034-4257(00)00200-5","article-title":"Parameterization of vegetation backscatter in radar-based, soil moisture estimation","volume":"76","author":"Bindlish","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_51","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_52","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_53","first-page":"2027","article-title":"Soil surface roughness observed during SMAPVEX16-IA and its potential consequences for SMOS and SMAP","volume":"2017","author":"Hornbuckle","year":"2017","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1109\/TGRS.2007.903698","article-title":"Inferring vegetation water content from C- and L-band SAR images","volume":"45","author":"Notarnicola","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","first-page":"1013","article-title":"Monitoring of soil moisture and vegetation water content variations in boreal forest from C-band SAR data","volume":"2","author":"Pulliainen","year":"2004","journal-title":"IEEE Int. Geosci. Remote Sens. Symp."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/LGRS.2011.2174772","article-title":"Radar Vegetation Index for Estimating the Vegetation Water Content of Rice and Soybean","volume":"9","author":"Kim","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/TGRS.2015.2471803","article-title":"Estimation of Vegetation Water Content From the Radar Vegetation Index at L-Band","volume":"54","author":"Huang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/01431161.2016.1192304","article-title":"Sentinel-1-based flood mapping: A fully automated processing chain","volume":"37","author":"Twele","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1016\/j.isprsjprs.2008.07.006","article-title":"Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories","volume":"64","author":"McNairn","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.rse.2005.03.010","article-title":"Efficiency of crop identification based on optical and SAR image time series","volume":"96","author":"Blaes","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Irwin, K., Beaulne, D., Braun, A., and Fotopoulos, G. (2017). Fusion of SAR, Optical Imagery and Airborne LiDAR for Surface Water Detection. Remote Sens., 9.","DOI":"10.3390\/rs9090890"},{"key":"ref_63","first-page":"7","article-title":"Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa","volume":"21","author":"Liesenberg","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/014311698216224","article-title":"Soil moisture estimation with ERS-1 SAR data in the East-German loess soil area","volume":"19","author":"Weimann","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1029\/1998WR900120","article-title":"Retrieving near-surface soil moisture from Radarsat SAR data","volume":"35","author":"Biftu","year":"1999","journal-title":"Water Resour. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.asr.2011.03.029","article-title":"Soil moisture estimation by using multipolarization SAR image","volume":"48","author":"Saradjian","year":"2011","journal-title":"Adv. Space Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1002\/2012WR013405","article-title":"Soil moisture mapping in a semiarid region, based on ASAR\/Wide Swath satellite data","volume":"50","author":"Zribi","year":"2014","journal-title":"Water Resour. Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5613","DOI":"10.1109\/TGRS.2015.2426194","article-title":"A Global Sensitivity Analysis of Soil Parameters Associated with Backscattering Using the Advanced Integral Equation Model","volume":"53","author":"Ma","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ma, C., Wang, S., Zhao, Z., and Ma, H. (2021). Global Sensitivity Analysis of a Water Cloud Model toward Soil Moisture Retrieval over Vegetated Agricultural Fields. Remote Sens., 13.","DOI":"10.3390\/rs13193889"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Gao, Q. (2018). Irrigation mapping using Sentinel-1 time series at field scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"12131","DOI":"10.1038\/s41598-021-91646-w","article-title":"CubeSats deliver new insights into agricultural water use at daily and 3 m resolutions","volume":"11","author":"Aragon","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Johansen, K., Ziliani, M.G., Houborg, R., Franz, T.E., and McCabe, M.F. (2022). Cubesat Constellations Provide Enhanced Crop Phenology And Digital Agricultural Insights Using Daily Leaf Area Index Retrievals. Sci. Rep., 1\u201316.","DOI":"10.1038\/s41598-022-09376-6"},{"key":"ref_74","unstructured":"Sebastianelli, A., Nowakowski, A., Puglisi, E., Rosso, M.P.d., Mifdal, J., Pirri, F., Mathieu, P.-P., and Ullo, S.L. (2021). Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:30:01Z","timestamp":1760135401000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/5\/1205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":74,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14051205"],"URL":"https:\/\/doi.org\/10.3390\/rs14051205","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,1]]}}}