{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:45:39Z","timestamp":1783640739524,"version":"3.55.0"},"reference-count":73,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Research Council (ERC)","award":["75561"],"award-info":[{"award-number":["75561"]}]},{"name":"ERC-2017-STG SENTIFLEX project","award":["75561"],"award-info":[{"award-number":["75561"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil moisture content (SMC) plays a critical role in soil science via its influences on agriculture, water resources management, and climate conditions. There is broad interest in finding relationships between groundwater recharge, soil characteristics, and plant properties for the quantification of SMC. The objective of this study was to assess the potential of optical satellite imagery for estimating the SMC over cropland areas. For this purpose, we collected 394 soil samples as targets in Gonbad-e Kavus in the Golestan province in the north of Iran, where a variety of crop types are cultivated. As input data, we first computed several spectral indices from Sentinel 2 (S2) and Landsat 8 (L8) images, such as the Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Salinity Index (NDSI), and then analyzed their relationships with surveyed SMC using four machine learning regression algorithms: random forests (RFs), XGBoost, extra tree decision (EDT), and support vector machine (SVM). Results revealed a high and rather similar correlation between the spectral indices and measured SMC values for both S2 and L8 data. The EDT regression algorithm yielded the highest accuracy, with an R2 = 0.82, MAE = 3.74, and RMSE = 1.08 for S2 and R2 = 0.88, RMSE = 2.42, and MAE = 1.08 for L8 images. Results also revealed that MNDWI, NDWI, and NDSI responded most sensitively to SMC estimation.<\/jats:p>","DOI":"10.3390\/rs15082155","type":"journal-article","created":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T01:42:39Z","timestamp":1681954959000},"page":"2155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Satellite-Based Estimation of Soil Moisture Content in Croplands: A Case Study in Golestan Province, North of Iran"],"prefix":"10.3390","volume":"15","author":[{"given":"Soraya","family":"Bandak","sequence":"first","affiliation":[{"name":"Department of Soil Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan P.O. Box 386, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Ali Reza","family":"Movahedi Naeini","sequence":"additional","affiliation":[{"name":"Department of Soil Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan P.O. Box 386, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chooghi Bairam","family":"Komaki","sequence":"additional","affiliation":[{"name":"Department of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan P.O. Box 386, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL)\u2014Laboratory for Earth Observation (LEO), University of Valencia, 46003 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2318-8216","authenticated-orcid":false,"given":"Mohammad","family":"Kakooei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1513-869X","authenticated-orcid":false,"given":"Mohammad Ali","family":"Mahmoodi","sequence":"additional","affiliation":[{"name":"Department of Soil Science, Faculty of Agriculture, University of Kurdistan, Sanandaj P.O. Box 416, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126868","DOI":"10.1016\/j.jhydrol.2021.126868","article-title":"A comprehensive assessment of water storage dynamics and hydroclimatic extremes in the Chao Phraya River Basin during 2002\u20132020","volume":"603","author":"Kinouchi","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_2","first-page":"H51H-1606","article-title":"A temporal correlation based approach for spatial disaggregation of remotely sensed soil moisture","volume":"2016","author":"Kim","year":"2016","journal-title":"AGU Fall Meet. Abstr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1002\/ldr.3243","article-title":"The impact of freeze\u2013thaw cycles and soil moisture content at freezing on runoff and soil loss","volume":"30","author":"Wei","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/15481603.2016.1258971","article-title":"Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces","volume":"54","author":"Jiang","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3839","DOI":"10.1109\/JSTARS.2017.2723923","article-title":"Improvement of AMSR2 Soil Moisture Products over South Korea","volume":"10","author":"Lee","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"S49","article-title":"Assessment of water pollution induced by human activities in Burullus Lake using Landsat 8 operational land imager and GIS","volume":"20","year":"2017","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_7","first-page":"e7","article-title":"Importance of including soil moisture in drought monitoring over the Brazilian semiarid region: An evaluation using the JULES model, in situ observations, and remote sensing","volume":"1","author":"Zeri","year":"2022","journal-title":"Clim. Resil. Sustain."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.rse.2016.02.064","article-title":"Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index","volume":"177","author":"Gumuzzio","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.jhydrol.2006.09.004","article-title":"Soil moisture spatial variability in experimental areas of central Italy","volume":"333","author":"Brocca","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0022-1694(03)00229-4","article-title":"Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall\u2013runoff model","volume":"280","author":"Aubert","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103673","DOI":"10.1016\/j.earscirev.2021.103673","article-title":"Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future","volume":"218","author":"Li","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/JSTARS.2011.2169236","article-title":"A Fusion Approach to Retrieve Soil Moisture with SAR and Optical Data","volume":"5","author":"Prakash","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","unstructured":"Johnson, A. (1962). Methods of Measuring Soil Moisture in the Field, US Department of the Interior, US Geological Survey."},{"key":"ref_15","unstructured":"Mekonnen, D.F. (2009). Satellite Remote Sensing for Soil Moisture Estimation: Gumara Catchment, Ethiopia, University of Twente. Available online: https:\/\/purl.utwente.nl\/essays\/93086."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"722","DOI":"10.2136\/sssaj2002.7220","article-title":"Moisture Effects on Soil Reflectance","volume":"66","author":"Lobell","year":"2002","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/01431161.2018.1512767","article-title":"Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery","volume":"40","author":"Taghadosi","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115280","DOI":"10.1016\/j.geoderma.2021.115280","article-title":"Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape","volume":"404","author":"Larson","year":"2021","journal-title":"Geoderma"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"114901","DOI":"10.1016\/j.geoderma.2020.114901","article-title":"Effect of multi-temporal satellite images on soil moisture prediction using a digital soil mapping approach","volume":"385","author":"Fathololoumi","year":"2021","journal-title":"Geoderma"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.5194\/hess-25-2739-2021","article-title":"Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques","volume":"25","author":"Araya","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.5194\/hess-23-2615-2019","article-title":"Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics","volume":"23","author":"Laborczi","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2022.01.005","article-title":"Soil moisture content retrieval from Landsat 8 data using ensemble learning","volume":"185","author":"Zhang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hssaine, B.A., Chehbouni, A., Er-Raki, S., Khabba, S., Ezzahar, J., Ouaadi, N., Ojha, N., Rivalland, V., and Merlin, O. (2021). On the Utility of High-Resolution Soil Moisture Data for Better Constraining Thermal-Based Energy Balance over Three Semi-Arid Agricultural Areas. Remote Sens., 13.","DOI":"10.3390\/rs13040727"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115691","DOI":"10.1016\/j.geoderma.2021.115691","article-title":"Spatio-temporal mapping of soil water storage in a semi-arid landscape of northern Ghana\u2014A multi-tasked ensemble machine-learning approach","volume":"410","author":"Nketia","year":"2022","journal-title":"Geoderma"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., and Ghalhari, G.A.F. (2020). Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data. Water, 12.","DOI":"10.3390\/w12113223"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jia, Y., Jin, S., Savi, P., Yan, Q., and Li, W. (2020). Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach. Remote Sens., 12.","DOI":"10.3390\/rs12223679"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ecolind.2018.01.049","article-title":"Estimating soil organic carbon stocks using different modelling techniques in the semi-arid rangelands of eastern Australia","volume":"88","author":"Wang","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compag.2019.03.015","article-title":"Mapping stocks of soil total nitrogen using remote sensing data: A comparison of random forest models with different predictors","volume":"160","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-021-06824-x","article-title":"Effects of superabsorbent polymer A200 on soil characteristics and rainfed winter wheat growth (Triticum aestivum L.)","volume":"14","author":"Bandak","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and polygons using simple non-iterative clustering. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_31","first-page":"47","article-title":"Development of land moisture estimation model using modis infrared, thermal, and evi to detect drought at paddy field","volume":"10","author":"Domiri","year":"2013","journal-title":"Int. J. Remote Sens. Earth Sci. (IJReSES)"},{"key":"ref_32","first-page":"379","article-title":"Infiltrabilit\u00e9 et \u00e9rodibilit\u00e9 de sols salinis\u00e9s de la plaine du Bas Ch\u00e9liff (Alg\u00e9rie). Mesures au laboratoire sous simulation de pluie","volume":"11","author":"Douaoui","year":"2004","journal-title":"EGS"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.agwat.2004.09.038","article-title":"Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators","volume":"77","author":"Khan","year":"2005","journal-title":"Agric. Water Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.pce.2010.12.004","article-title":"Characterizing soil salinity in irrigated agriculture using a remote sensing approach","volume":"55\u201357","author":"Abbas","year":"2013","journal-title":"Phys. Chem. Earth, Parts A\/B\/C"},{"key":"ref_35","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1080\/01431160701395195","article-title":"Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI","volume":"29","author":"Alhammadi","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.proeng.2012.01.1193","article-title":"Remote Sensing Techniques for Salt Affected Soil Mapping: Application to the Oran Region of Algeria","volume":"33","author":"Dehni","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/2150704X.2014.998793","article-title":"An improved automated land cover updating approach by integrating with downscaled NDVI time series data","volume":"6","author":"Chen","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jhydrol.2004.01.008","article-title":"In situ measurement of soil moisture: A comparison of techniques","volume":"293","author":"Walker","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"126698","DOI":"10.1016\/j.jhydrol.2021.126698","article-title":"Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning","volume":"600","author":"Li","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_42","unstructured":"Huete, A., Justice, C., and van Leeuwen, W. (2023, March 05). MODIS Vegetation Index (MOD13) Algorithm Theoretical Basis Document, Version. 3., Available online: https:\/\/modis.gsfc.nasa.gov\/data\/atbd\/atbd_mod13.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4621","DOI":"10.5194\/bg-12-4621-2015","article-title":"Deriving seasonal dynamics in ecosystem properties of semi-arid savannas using in situ based hyperspectral reflectance","volume":"12","author":"Tagesson","year":"2015","journal-title":"Biogeosciences Discuss."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"59","DOI":"10.3390\/tomography8010006","article-title":"Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm","volume":"8","author":"Li","year":"2022","journal-title":"Tomography"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"125840","DOI":"10.1016\/j.jhydrol.2020.125840","article-title":"Root zone soil moisture estimation with Random Forest","volume":"593","author":"Carranza","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zheng, H., Yuan, J., and Chen, L. (2017). Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation. Energies, 10.","DOI":"10.3390\/en10081168"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2019.02.022","article-title":"Downscaling SMAP soil moisture estimation with gradient boosting decision tree regression over the Tibetan Plateau","volume":"225","author":"Wei","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107618","DOI":"10.1016\/j.agwat.2022.107618","article-title":"Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm","volume":"267","author":"He","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., and Yang, X. (2017). Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Remote Sens., 9.","DOI":"10.3390\/rs9040309"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ge, X., Ding, J., Jin, X., Wang, J., Chen, X., Li, X., Liu, J., and Xie, B. (2021). Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region. Remote Sens., 13.","DOI":"10.3390\/rs13081562"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., and Hopkinson, C. (2019). Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote Sens., 11.","DOI":"10.3390\/rs11070842"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"053501","DOI":"10.1117\/1.3539767","article-title":"Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine","volume":"5","author":"Sun","year":"2011","journal-title":"J. Appl. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Elgeldawi, E., Sayed, A., Galal, A.R., and Zaki, A.M. (2021). Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis. Informatics, 8.","DOI":"10.3390\/informatics8040079"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geoderma.2018.12.037","article-title":"Digital mapping of soil carbon fractions with machine learning","volume":"339","author":"Keskin","year":"2019","journal-title":"Geoderma"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Nolet, C., Poortinga, A., Roosjen, P., Bartholomeus, H., and Ruessink, G. (2014). Measuring and Modeling the Effect of Surface Moisture on the Spectral Reflectance of Coastal Beach Sand. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0112151"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.mcm.2011.10.054","article-title":"A method of estimating soil moisture based on the linear decomposition of mixture pixels","volume":"58","author":"Gao","year":"2013","journal-title":"Math. Comput. Model."},{"key":"ref_60","unstructured":"Acharya, U., Daigh, A.L.M., and Oduor, P.G. (2021). Factors affecting the use of weather station data in predicting surface soil moisture for agricultural applications. Can. J. Soil Sci., 1\u201313."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008WR006855","article-title":"Using oceanic-atmospheric oscillations for long lead time streamflow forecasting","volume":"45","author":"Kalra","year":"2009","journal-title":"Water Resour. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104320","DOI":"10.1016\/j.cageo.2019.104320","article-title":"Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models","volume":"133","author":"Achieng","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.jhydrol.2013.12.047","article-title":"Combining SMOS with visible and near\/shortwave\/thermal infrared satellite data for high resolution soil moisture estimates","volume":"516","author":"Piles","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Nadeem, A.A., Zha, Y., Shi, L., Ali, S., Wang, X., Zafar, Z., Afzal, Z., and Tariq, M.A.U.R. (2023). Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China. Remote Sens., 15.","DOI":"10.3390\/rs15030812"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Romano, E., Bergonzoli, S., Bisaglia, C., Picchio, R., and Scarfone, A. (2023). The Correlation between Proximal and Remote Sensing Methods for Monitoring Soil Water Content in Agricultural Applications. Electronics, 12.","DOI":"10.3390\/electronics12010127"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.jhydrol.2013.12.008","article-title":"Soil moisture at watershed scale: Remote sensing techniques","volume":"516","author":"Fang","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"012112","DOI":"10.1088\/1755-1315\/126\/1\/012112","article-title":"Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia","volume":"126","author":"Zaitunah","year":"2018","journal-title":"IOP Conf. Series Earth Environ. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation","volume":"161","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1080\/10106049.2014.965757","article-title":"Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level","volume":"30","author":"Singh","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/0034-4257(94)90038-8","article-title":"The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall","volume":"50","author":"Nicholson","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1109\/TGRS.2010.2040830","article-title":"Estimating Soil Moisture Conditions of the Greater Changbai Mountains by Land Surface Temperature and NDVI","volume":"48","author":"Han","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.rse.2013.08.022","article-title":"Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia","volume":"140","author":"Chen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/JSTARS.2014.2378795","article-title":"Estimation of Soil Moisture in Mountain Areas Using SVR Technique Applied to Multiscale Active Radar Images at C-Band","volume":"8","author":"Pasolli","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2155\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:18:59Z","timestamp":1760123939000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":73,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082155"],"URL":"https:\/\/doi.org\/10.3390\/rs15082155","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]}}}