{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T07:59:38Z","timestamp":1762761578945,"version":"build-2065373602"},"reference-count":72,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T00:00:00Z","timestamp":1565913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"JSPS KAKENHI","award":["19K06313"],"award-info":[{"award-number":["19K06313"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to supplement data provided by larger satellites. Land cover classification is one of the most common applications of remote sensing, and the results provide a reliable resource for agricultural field management and estimating potential harvests. This paper describes the results of the first experiments in which ASNARO-2 XSAR data were applied for agricultural crop classification. In previous studies, Sentinel-1 C-SAR data have been widely utilized to identify crop types. Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the combination of these data was also tested. To assess the potential for accurate crop classification, some radar vegetation indices were calculated from the backscattering coefficients for two dates. In addition, the potential of each type of SAR data was evaluated using four popular supervised learning models: Support vector machine (SVM), random forest (RF), multilayer feedforward neural network (FNN), and kernel-based extreme learning machine (KELM). The combination of ASNARO-2 XSAR and Sentinel-1 C-SAR data was effective, and overall classification accuracies of 85.4 \u00b1 1.8% were achieved using SVM.<\/jats:p>","DOI":"10.3390\/rs11161920","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T06:10:14Z","timestamp":1566195014000},"page":"1920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH\/VV Polarization Data for Improved Crop Mapping"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8330-3730","authenticated-orcid":false,"given":"Rei","family":"Sonobe","sequence":"first","affiliation":[{"name":"Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1038\/nature01014","article-title":"Agricultural sustainability and intensive production practices","volume":"418","author":"Tilman","year":"2002","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_3","unstructured":"(2019, April 01). Ministry of Agriculture, Forestry and Fisheries, Available online: http:\/\/www8.cao.go.jp\/space\/comittee\/dai36\/siryou3-5.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.rse.2010.11.010","article-title":"Improved forest biomass estimates using ALOS AVNIR-2 texture indices","volume":"115","author":"Sarker","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"58","article-title":"Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model","volume":"79","author":"Darvishzadeh","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Darvishzadeh, R., Wang, T., Skidmore, A., Vrieling, A., O\u2019Connor, B., Gara, T.W., Ens, B.J., and Paganini, M. (2019). Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model. Remote Sens., 11.","DOI":"10.3390\/rs11060671"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/JSTARS.2018.2870650","article-title":"Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification","volume":"11","author":"Useya","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/10106049.2018.1425739","article-title":"Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover","volume":"34","author":"Sonobe","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4183","DOI":"10.1080\/01431160701422213","article-title":"Using in-situ measurements to evaluate the new RapidEye (TM) satellite series for prediction of wheat nitrogen status","volume":"28","author":"Eitel","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wu, B., Ponce-Campos, G.E., Zhang, M., Chang, S., and Tian, F. (2018). Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images. Remote Sens., 10.","DOI":"10.3390\/rs10081200"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6069","DOI":"10.1080\/01431160902980316","article-title":"Application of the Sahebi model using ALOS\/PALSAR and 66.3 cm long surface profile data","volume":"30","author":"Sonobe","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1080\/01431161.2014.919679","article-title":"Polarimetric analysis of multi-temporal RADARSAT-2 SAR images for wheat monitoring and mapping","volume":"35","author":"Xu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"471","DOI":"10.6090\/jarq.48.471","article-title":"Winter Wheat Growth Monitoring Using Multi-temporal TerraSAR-X Dual-polarimetric Data","volume":"48","author":"Sonobe","year":"2014","journal-title":"Jpn. Agric. Res. Q. JARQ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1109\/JSTARS.2018.2845127","article-title":"Sensitivity of SAR Tomography to the Phenological Cycle of Agricultural Crops at X-, C-, and L-band","volume":"11","author":"Joerg","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1016\/j.rse.2010.12.014","article-title":"Use of ENVISAT\/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta","volume":"115","author":"Bouvet","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_17","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_18","unstructured":"Zonno, M., Bordoni, F., Matar, J., de Almeida, F.Q., Sanjuan-Ferrer, M.J., Younis, M., Rodriguez-Cassola, M., and Krieger, G. (2019, January 13\u201317). Sentinel-1 Next Generation: Trade-offs and Assessment of Mission Performance. Proceedings of the ESA Living Planet Symposium, Milan, Italy."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sun, C., Bian, Y., Zhou, T., and Pan, J. (2019). Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sensors, 19.","DOI":"10.3390\/s19102401"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mercier, A., Betbeder, J., Rumiano, F., Baudry, J., Gond, V., Blanc, L., Bourgoin, C., Cornu, G., Ciudad, C., and Marchamalo, M. (2019). Evaluation of Sentinel-1 and 2 Time Series for Land Cover Classification of Forest\u2013Agriculture Mosaics in Temperate and Tropical Landscapes. Remote Sens., 11.","DOI":"10.3390\/rs11080979"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1080\/15481603.2017.1351149","article-title":"Assessing the suitability of data from Sentinel-1A and 2A for crop classification","volume":"54","author":"Sonobe","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Stendardi, L., Karlsen, S.R., Niedrist, G., Gerdol, R., Zebisch, M., Rossi, M., and Notarnicola, C. (2019). Exploiting Time Series of Sentinel-1 and Sentinel-2 Imagery to Detect Meadow Phenology in Mountain Regions. Remote Sens., 11.","DOI":"10.3390\/rs11050542"},{"key":"ref_23","first-page":"574","article-title":"Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data","volume":"73","author":"Clauss","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Minh, D.H.T., Nguyen, H.D., Baghdadi, N., Courault, D., Hossard, L., and El Moussawi, I. (2018). Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sens., 10.","DOI":"10.3390\/rs10091394"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1080\/2150704X.2013.842285","article-title":"Sensitivity analysis of X-band SAR to wheat and barley leaf area index in the Merguellil Basin","volume":"4","author":"Fontanelli","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.rse.2018.10.012","article-title":"Estimating canola phenology using synthetic aperture radar","volume":"219","author":"McNairn","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1080\/0143116031000116985","article-title":"Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain","volume":"25","author":"Costa","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","unstructured":"National Research and Development Agency and Japan Aerospace Exploration Agency (JAXA) (2019, June 05). Launch Result, Epsilon-3 with ASNARO-2 Aboard. Available online: https:\/\/global.jaxa.jp\/press\/2018\/01\/20180118_epsilon3.html."},{"key":"ref_29","unstructured":"Japan EO Satellite Service, Ltd. (JEOSS) (2019, June 05). Japan EO Satellite Service, Ltd. (JEOSS) Announces the Start of Commercial Operation. Available online: https:\/\/jeoss.co.jp\/press\/japan-eo-satellite-service-ltd-jeoss-announces-the-start-of-commercial-operation\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sonobe, R. (2019). Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data. Remote Sens., 11.","DOI":"10.3390\/rs11101148"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial on Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","article-title":"A relative evaluation of multiclass image classification by support vector machines","volume":"42","author":"Foody","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sonobe, R., Tani, H., Wang, X., Kobayashi, N., and Shimamura, H. (2015). Discrimination of crop types with TerraSAR-X-derived information. Phys. Chem. Earth Parts A B C, 2\u201313.","DOI":"10.1016\/j.pce.2014.11.001"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","article-title":"A random forest guided tour","volume":"25","author":"Biau","year":"2016","journal-title":"TEST"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.biosystemseng.2018.09.018","article-title":"Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments","volume":"175","author":"Sonobe","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/2150704X.2013.805279","article-title":"Kernel-based extreme learning machine for remote-sensing image classification","volume":"4","author":"Pal","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_38","first-page":"128","article-title":"An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data","volume":"32","author":"Sonobe","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cooner, A.J., Shao, Y., and Campbell, J.B. (2016). Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake. Remote Sens., 8.","DOI":"10.3390\/rs8100868"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rse.2013.06.003","article-title":"Balancing misclassification errors of land cover classification maps using support vector machines and Landsat imagery in the Maipo river basin (Central Chile, 1975\u20132010)","volume":"137","author":"Puertas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"281","article-title":"Random Search for Hyper-Parameter Optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4989","DOI":"10.1109\/TGRS.2018.2803153","article-title":"Phenology-Based Backscattering Model for Corn at L-Band","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1080\/01431160050029620","article-title":"The relationship between ERS-2 SAR backscatter and soil moisture: Generalization from a humid to semi-arid transect","volume":"21","author":"Shoshany","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"026020","DOI":"10.1117\/1.JRS.10.026020","article-title":"Contribution of multitemporal polarimetric synthetic aperture radar data for monitoring winter wheat and rapeseed crops","volume":"10","author":"Betbeder","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Richards, J.A. (2013). Remote Sensing Digital Image Analysis, Springer.","DOI":"10.1007\/978-3-642-30062-2"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_48","unstructured":"R Core Team (2019, June 05). R: A Language and Environment for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_50","first-page":"821","article-title":"Theoretical foundations of the potential function method in pattern recognition learning","volume":"25","author":"Aizerman","year":"1964","journal-title":"Autom. Remote Control"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","first-page":"352","article-title":"A kernel functions analysis for support vector machines for land cover classification","volume":"11","author":"Kavzoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_55","first-page":"18","article-title":"Classification and regression by random Forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_56","first-page":"25","article-title":"Random survival forests for R","volume":"7","author":"Ishwaran","year":"2007","journal-title":"R News"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"Ishwaran","year":"2008","journal-title":"Ann. Appl. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1080\/01431161.2017.1323286","article-title":"Mapping crop cover using multi-temporal Landsat 8 OLI imagery","volume":"38","author":"Sonobe","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","article-title":"Introduction to multi-layer feed-forward neural networks","volume":"39","author":"Svozil","year":"1997","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_60","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_63","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":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1080\/01431161.2012.724540","article-title":"Does spatial resolution matter? A multi-scale comparison of object-based and pixel-based methods for detecting change associated with gas well drilling operations","volume":"34","author":"Baker","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.asr.2009.11.013","article-title":"Study on extraction of crop information using time-series MODIS data in the Chao Phraya Basin of Thailand","volume":"45","author":"Lv","year":"2010","journal-title":"Adv. Space Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1635","DOI":"10.1016\/j.asr.2015.07.021","article-title":"Process-based image analysis for agricultural mapping: A case study in Turkgeldi region, Turkey","volume":"56","author":"Avci","year":"2015","journal-title":"Adv. Space Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Guarini, R., Bruzzone, L., Santoni, M., and Dini, L. (2015, January 21\u201323). Analysis on the Effectiveness of Multi-Temporal COSMO-SkyMed Images for Crop Classification. Proceedings of the Conference on Image and Signal Processing for Remote Sensing XXI, Toulouse, France.","DOI":"10.1117\/12.2193757"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4702","DOI":"10.1080\/01431161.2015.1088674","article-title":"Mapping land cover and land use from object-based classification: An example from a complex agricultural landscape","volume":"36","author":"Goodin","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"361","DOI":"10.5721\/EuJRS20164920","article-title":"Assessing in-season crop classification performance using satellite data: A test case in Northern Italy","volume":"49","author":"Azar","year":"2016","journal-title":"Eur. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1109\/JSTARS.2016.2560141","article-title":"Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data","volume":"9","author":"Kussul","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N., and Segui, P.Q. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.rse.2018.04.013","article-title":"Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil","volume":"211","author":"Amazirh","year":"2018","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1920\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:41Z","timestamp":1760188301000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/16\/1920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,16]]},"references-count":72,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11161920"],"URL":"https:\/\/doi.org\/10.3390\/rs11161920","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,8,16]]}}}