{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T23:40:54Z","timestamp":1783381254741,"version":"3.54.6"},"reference-count":98,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["NRF- 2022R1I1A1A01073185"],"award-info":[{"award-number":["NRF- 2022R1I1A1A01073185"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["PJ014787042023"],"award-info":[{"award-number":["PJ014787042023"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003627","name":"Rural Development Administration","doi-asserted-by":"publisher","award":["NRF- 2022R1I1A1A01073185"],"award-info":[{"award-number":["NRF- 2022R1I1A1A01073185"]}],"id":[{"id":"10.13039\/501100003627","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003627","name":"Rural Development Administration","doi-asserted-by":"publisher","award":["PJ014787042023"],"award-info":[{"award-number":["PJ014787042023"]}],"id":[{"id":"10.13039\/501100003627","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)\u2019s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)\u2019s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m3\/m3 and a correlation coefficient (CC) of 0.9226. This model\u2019s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.<\/jats:p>","DOI":"10.3390\/rs15164063","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T10:42:29Z","timestamp":1692268949000},"page":"4063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-6146","authenticated-orcid":false,"given":"Soo-Jin","family":"Lee","sequence":"first","affiliation":[{"name":"Geomatics Research Institute, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuluong","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinsoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minha","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3375-4357","authenticated-orcid":false,"given":"Jaeil","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-6100","authenticated-orcid":false,"given":"Yangwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"ref_1","unstructured":"Robock, A. (2003). Encyclopedia of Atmospheric Sciences, Academic Press."},{"key":"ref_2","unstructured":"Engman, E.T. (1997). Soil Moisture: The Hydrologic Interface between Surface and Ground Waters, Laboratory for Hydrospheric Processes Research Publications."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/j.rse.2014.07.013","article-title":"Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)","volume":"152","author":"Wigneron","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1175\/JCLI3926.1","article-title":"The Temporal Variability of Soil Moisture and Surface Hydrological Quantities in a Climate Model","volume":"19","author":"Arora","year":"2006","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6495","DOI":"10.1029\/2018GL078131","article-title":"Soil Moisture Stress as a Major Driver of Carbon Cycle Uncertainty","volume":"45","author":"Trugman","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4213","DOI":"10.3390\/s8074213","article-title":"On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar","volume":"8","author":"Verhoest","year":"2008","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","article-title":"Investigating Soil Moisture\u2014Climate Interactions in a Changing Climate: A Review","volume":"99","author":"Seneviratne","year":"2010","journal-title":"Earth-Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2017.12.0214","article-title":"Field and Laboratory Evaluation of the CS655 Soil Water Content Sensor","volume":"17","author":"Caldwell","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"293","DOI":"10.21273\/HORTTECH.21.3.293","article-title":"Measuring Soil Water Content: A Review","volume":"21","author":"Bittelli","year":"2011","journal-title":"HortTechnology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/15481603.2018.1489943","article-title":"Estimation of Soil Moisture Using Deep Learning Based on Satellite Data: A Case Study of South Korea","volume":"56","author":"Lee","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2428","DOI":"10.1002\/2014WR016534","article-title":"Global Sensitivity Analysis of the Radiative Transfer Model","volume":"51","author":"Neelam","year":"2015","journal-title":"Water Resour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bhatta, B. (2013). Research Methods in Remote Sensing, Springer. SpringerBriefs in Earth Sciences.","DOI":"10.1007\/978-94-007-6594-8"},{"key":"ref_13","first-page":"1902","article-title":"North American Land Data Assimilation System: A Framework for Merging Model and Satellite Data for Improved Drought Monitoring","volume":"4","author":"Sheffield","year":"2012","journal-title":"Remote Sens."},{"key":"ref_14","unstructured":"European Space Agency (ESA) (2023, May 01). Sentinel-2 MSI User Guide, Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/sentinel-2-msi."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1084669","DOI":"10.1080\/23312041.2015.1084669","article-title":"Present Status of Soil Moisture Estimation by Microwave Remote Sensing","volume":"1","author":"Das","year":"2015","journal-title":"Cogent Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4966","DOI":"10.1109\/TGRS.2013.2286203","article-title":"Evaluation of IEM, Dubois, and Oh Radar Backscatter Models Using Airborne L-Band SAR","volume":"52","author":"Panciera","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1080\/01431160600658123","article-title":"Evaluation of Radar Backscatter Models IEM, OH and Dubois Using Experimental Observations","volume":"27","author":"Baghdadi","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1080\/07038992.2015.1104629","article-title":"A Hybrid (Multi-Angle and Multipolarization) Approach to Soil Moisture Retrieval Using the Integral Equation Model: Preparing for the RADARSAT Constellation Mission","volume":"41","author":"Merzouki","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/TGRS.2002.800232","article-title":"Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces","volume":"40","author":"Oh","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1109\/36.406677","article-title":"Measuring soil moisture with imaging radars","volume":"33","author":"Dubois","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1002\/hyp.6609","article-title":"Operational Performance of Current Synthetic Aperture Radar Sensors in Mapping Soil Surface Characteristics in Agricultural Environments: Application to Hydrological and Erosion Modelling","volume":"22","author":"Baghdadi","year":"2007","journal-title":"Hydrol. Process. Int. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.7745\/KJSSF.2012.45.2.127","article-title":"Estimation of Soil Moisture Content from Backscattering Coefficients Using a Radar Scatterometer","volume":"45","author":"Kim","year":"2012","journal-title":"Korean J. Soil Sci. Fertil."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TGRS.1985.289498","article-title":"Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models","volume":"GE-23","author":"Dobson","year":"1985","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A Simple Interpretation of the Surface Temperature\/Vegetation Index Space for Assessment of Surface Moisture Status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/S1002-0160(14)60031-X","article-title":"Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data","volume":"24","author":"Zhang","year":"2014","journal-title":"Pedosphere"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M., 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_28","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., El Hajj, M., Baghdadi, N., Lili-Chabaane, Z., Gao, Q., and Fanise, P. (2018). Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10121953"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1080\/10106049.2020.1815865","article-title":"Synergetic Utilization of Sentinel-1 SAR and Sentinel-2 Optical Remote Sensing Data for Surface Soil Moisture Estimation for Rupnagar, Punjab, India","volume":"37","author":"Tripathi","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/JSTARS.2020.3043628","article-title":"Combined Sentinel-1A with Sentinel-2A to Estimate Soil Moisture in Farmland","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","unstructured":"Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (2018). Advances in Neural Information Processing Systems, NeurIPS."},{"key":"ref_33","unstructured":"Li, Z., Gong, B., and Yang, T. (2016, January 9). Improved dropout for shallow and deep learning. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_34","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_35","unstructured":"Zhang, W., Du, T., and Wang, J. (2016). Advances in Information Retrieval, Proceedings of the 38th European Conference on IR Research, ECIR 2016, Padua, Italy, 20\u201323 March 2016, Springer."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1007\/s10462-019-09784-7","article-title":"A Survey of Regularization Strategies for Deep Models","volume":"53","author":"Moradi","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, W., Huang, W., Hong, Z., and Meng, L. (2017). Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050130"},{"key":"ref_38","unstructured":"Government of Canada (2023, May 01). Canadian Climate Normals, Available online: https:\/\/climate.weather.gc.ca\/climate_normals\/index_e.html."},{"key":"ref_39","unstructured":"Pacheco, A., L\u2019Heureux, J., McNairn, H., Powers, J., Howard, A., Geng, X., Rollin, P., Gottfried, K., Freeman, J., and Ojo, R. (2014). Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) Network Metadata, Science and Technology Branch Agriculture and Agri-Food Canada."},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2014.08.0114","article-title":"Calibration and evaluation of a frequency domain reflectometry sensor for real-time soil moisture monitoring","volume":"14","author":"Ojo","year":"2015","journal-title":"Vadose Zone J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Beale, J., Snapir, B., Waine, T., Evans, J., and Corstanje, R. (2019). The Significance of Soil Properties to the Estimation of Soil Moisture from C-Band Synthetic Aperture Radar. Hydrol. Earth Syst. Sci. Discuss., 1\u201328.","DOI":"10.5194\/hess-2019-294"},{"key":"ref_43","unstructured":"Rycroft, D.W., and Amer, M.H. (1995). Prospects for the Drainage of the Clay Soils, FAO. Irrigation and Drainage Paper 51."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1109\/LSP.2017.2758203","article-title":"SAR Image Despeckling Using a Convolutional Neural Network","volume":"24","author":"Wang","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Shahrezaei, I.H., and Kim, H.C. (2019). Resolutional Analysis of Multiplicative High-Frequency Speckle Noise Based on SAR Spatial De-Speckling Filter Implementation and Selection. Remote Sens., 11.","DOI":"10.3390\/rs11091041"},{"key":"ref_46","first-page":"947","article-title":"Experimental Retrieval of Soil Moisture for Cropland in South Korea Using Sentinel-1 SAR Data","volume":"33","author":"Lee","year":"2017","journal-title":"Korean J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.3390\/s7102458","article-title":"Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to Touch Basin (France)","volume":"7","author":"Baghdadi","year":"2007","journal-title":"Sens. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.procs.2015.07.415","article-title":"Ndvi: Vegetation Change Detection Using Remote Sensing and Gis\u2014A Case Study of Vellore District","volume":"57","author":"Gandhi","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Cruz-Sanabria, H., S\u00e1nchez, M.G., Rivera-Caicedo, J.P., and Avila-George, H. (2020, January 21\u201323). Identification of phenological stages of sugarcane cultivation using Sentinel-2 images. Proceedings of the 2020 9th International Conference on Software Process Improvement (CIMPS), Mazatlan, Sinaloa, Mexico.","DOI":"10.1109\/CIMPS52057.2020.9390095"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of Changes in Leaf Water Content Using Near-and Middle-Infrared Reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1175\/JAMC-D-12-0164.1","article-title":"Modified Shortwave Infrared Perpendicular Water Stress Index: A Farmland Water Stress Monitoring Method","volume":"52","author":"Feng","year":"2013","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S0034-4257(01)00191-2","article-title":"Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain","volume":"77","author":"Ceccato","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI-A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2005.01.008","article-title":"Potential of ASAR\/ENVISAT for the Characterisation of Soil Surface Parameters over Bare Agricultural Fields","volume":"96","author":"Holah","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"National Aeronautics and Space Administration (NASA) (2023, April 02). Soil Moisture Dirt to Dinner, Available online: https:\/\/smap.jpl.nasa.gov\/system\/internal_resources\/details\/original\/250_Soil_Moisture_Dirt_to_Dinner_3.5.14.pdf."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.catena.2014.04.003","article-title":"Soil Moisture Response to Environmental Factors Following Precipitation Events in a Small Catchment","volume":"120","author":"Zhu","year":"2014","journal-title":"Catena"},{"key":"ref_59","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_60","unstructured":"Jensen, J.R. (2015). Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice Hall. [4th ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"15919","DOI":"10.1038\/srep15919","article-title":"Highly Sensitive Image-Derived Indices of Water-Stressed Plants Using Hyperspectral Imaging in SWIR and Histogram Analysis","volume":"5","author":"Kim","year":"2015","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1080\/02626667.2012.665608","article-title":"Vegetation Effects on Soil Moisture Estimation from ERS-2 SAR Images","volume":"57","author":"Said","year":"2012","journal-title":"Hydrol. Sci. J."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1002\/rds.20048","article-title":"Multitemporal Radar Backscattering Measurement of Wheat Fields Using Multifrequency (L, S, C, and X) and Full-Polarization","volume":"48","author":"Jia","year":"2013","journal-title":"Radio Sci."},{"key":"ref_65","unstructured":"Kaur, P., Bala, A., Singh, H., and Sandhu, S.S. (2013). Guidelines to Prepare Crop Weather Calendar, AICRPAM, School of Climate Change and Agricultural Meteorology, PAU."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Tan, H.H., and Lim, K.H. (2019, January 28\u201330). Vanishing Gradient Mitigation with Deep Learning Neural Network Optimization. Proceedings of the 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia.","DOI":"10.1109\/ICSCC.2019.8843652"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","first-page":"022022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Yaqub, M., Feng, J., Zia, M., Arshid, K., Jia, K., Rehman, Z., and Mehmood, A. (2020). State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images. Brain Sci., 10.","DOI":"10.3390\/brainsci10070427"},{"key":"ref_70","unstructured":"Greenwell, B.M., Boehmke, B.C., and McCarthy, A.J. (2018). A Simple and Effective Model-Based Variable Importance Measure. arXiv."},{"key":"ref_71","unstructured":"Candel, A., Parmar, V., LeDell, E., and Arora, A. (2016). Deep Learning with H2O, H2O. ai Inc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0167-1987(87)90047-X","article-title":"Tillage and rainfall effects on random roughness: A review","volume":"9","author":"Zobeck","year":"1987","journal-title":"Soil Tillage Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/5712046","article-title":"Spatiotemporal Variability of Arctic Soil Moisture Detected from High-Resolution RADARSAT-2 SAR Data","volume":"2018","author":"Collingwood","year":"2018","journal-title":"Adv. Meteorol."},{"key":"ref_74","unstructured":"Hobbs, S., Ang, W., and Seynat, C. (1998, January 21\u201323). Wind and Rain Effects on SAR Backscatter from Crops. Proceedings of the 2nd International Workshop on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications, ETEC, Noordwijk, The Netherlands."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wang, R., Cherkauer, K., and Bowling, L. (2016). Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8040269"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.gloplacha.2011.07.001","article-title":"Effects of Variations in Climatic Parameters on Evapotranspiration in the Arid and Semi-Arid Regions","volume":"78","author":"Eslamian","year":"2011","journal-title":"Glob. Planet. Chang."},{"key":"ref_77","unstructured":"Government of Canada (2023, May 15). Departure from Average Precipitation (mm), Available online: https:\/\/open.canada.ca\/data\/en\/dataset\/7b817d93-f34d-4aa8-8658-d9abe9d84a8f."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Bhuiyan, H.A.K.M., McNairn, H., Powers, J., and Merzouki, A. (2017). Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model. Hydrology, 4.","DOI":"10.3390\/hydrology4010009"},{"key":"ref_79","unstructured":"Manitoba Agriculture (2023, April 02). AgriMaps, Available online: https:\/\/agrimaps.gov.mb.ca\/agrimaps\/#."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/S1671-2927(08)60269-2","article-title":"Effects of Soil Water Content on Cotton Root Growth and Distribution under Mulched Drip Irrigation","volume":"8","author":"Hu","year":"2009","journal-title":"Agric. Sci. China"},{"key":"ref_81","first-page":"752","article-title":"How Much Water Is Used for Irrigation? A New Approach Exploiting Coarse Resolution Satellite Soil Moisture Products","volume":"73C","author":"Brocca","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"103982","DOI":"10.1016\/j.advwatres.2021.103982","article-title":"The Effect of Soil-Moisture Uncertainty on Irrigation Water Use and Farm Profits","volume":"154","author":"Kelly","year":"2021","journal-title":"Adv. Water Resour."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s12230-018-9656-y","article-title":"Yield and nitrogen use of irrigated processing potato in response to placement, timing and source of nitrogen fertilizer in Manitoba","volume":"95","author":"Gao","year":"2018","journal-title":"Am. J. Potatato Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1002\/joc.5973","article-title":"Global observed and modelled impacts of irrigation on surface temperature","volume":"39","author":"Chen","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_85","unstructured":"Scherer, T.F., Franzen, D., and Cihacek, L. (2013). AE1675 (Revised) Soil, Water and Plant Characteristics Important to Irrigation, NDSU Extension Service; North Dakota State University. Available online: https:\/\/www.researchgate.net\/profile\/Larry-Cihacek\/publication\/281845779_Soil_water_and_plant_characteristics_important_to_irrigation\/links\/55fb01d208aec948c4afa85d\/Soil-water-and-plant-characteristics-important-to-irrigation.pdf."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"106859","DOI":"10.1016\/j.agwat.2021.106859","article-title":"Yield-compatible salinity level for growing cotton (Gossypium hirsutum L.) under mulched drip irrigation using saline water","volume":"250","author":"Ren","year":"2021","journal-title":"Agric. Water Manag."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"104592","DOI":"10.1016\/j.still.2020.104592","article-title":"Soil Physicochemical Properties and Cotton (Gossypium hirsutum L.) Yield under Brackish Water Mulched Drip Irrigation","volume":"199","author":"Yang","year":"2020","journal-title":"Soil Tillage Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"8017","DOI":"10.1029\/2017JD027784","article-title":"Simulating the Impacts of Irrigation and Dynamic Vegetation over the North China Plain on Regional Climate","volume":"123","author":"Wu","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"827","DOI":"10.5194\/hess-26-827-2022","article-title":"Untangling Irrigation Effects on Maize Water and Heat Stress Alleviation Using Satellite Data","volume":"26","author":"Zhu","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_90","first-page":"21","article-title":"Factors Affecting the Infiltration of Agricultural Soils","volume":"6","author":"Haghnazari","year":"2015","journal-title":"Int. J. Agron. Agric. Res."},{"key":"ref_91","unstructured":"Sharma, V. (2018). Irrigation Management: Basics of Soil Water Bulletin. B-1331, University of Wyoming. Available online: http:\/\/wyoextension.org\/publications\/html\/B1331."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.13031\/2013.27689","article-title":"Furrow Irrigation Erosion and Sedimentation: On-field Distribution","volume":"39","author":"Trout","year":"1996","journal-title":"Trans. ASAE"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1002\/hyp.9939","article-title":"Changes of Soil Surface Roughness under Water Erosion Process","volume":"28","author":"Zheng","year":"2014","journal-title":"Hydrol. Process."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"L24408","DOI":"10.1029\/2008GL035296","article-title":"Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets","volume":"35","author":"Wisser","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Fan, L. (2022, January 15\u201318). Comparisons of Five Indices for Estimating Local Terrain Surface Roughness using LiDAR Point Clouds. Proceedings of the 2022 29th International Conference on Geoinformatics (IEEE), Beijing, China.","DOI":"10.1109\/Geoinformatics57846.2022.9963877"},{"key":"ref_96","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1982). Microwave Remote Sensing: Active and Passive, Addison-Wesley."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1002\/esp.4758","article-title":"Evaluation of Terrestrial Laser Scanner and Structure from Motion photogrammetry techniques for quantifying soil surface roughness parameters over agricultural soils","volume":"45","author":"Pfeifer","year":"2020","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_98","unstructured":"Smith, A.C., Zarnetske, P., Dahlin, K., Wilson, A., and Latimer, A. (2023, July 22). Package \u2018geodiv\u2019\u2014Methods for Calculating Gradient Surface Metrics. Available online: https:\/\/cran.r-project.org\/web\/packages\/geodiv\/geodiv.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4063\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:35:37Z","timestamp":1760128537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/4063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":98,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15164063"],"URL":"https:\/\/doi.org\/10.3390\/rs15164063","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]}}}