{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:13:55Z","timestamp":1772554435479,"version":"3.50.1"},"reference-count":102,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,22]],"date-time":"2023-12-22T00:00:00Z","timestamp":1703203200000},"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>Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a field. While useful, these methods are limited by the relatively small number of SWC measurements that can be acquired with conventional measurement techniques. This study uses UAV-based data and thousands of SWC measurements acquired using geophysical methods at two different depths and before and after precipitation to predict the SWC using the random forest method across a vineyard in the central United States. Both multispectral data (five reflectance bands and eleven vegetation indices calculated from these bands) and thermal UAV-based data were acquired, and the importance of different reflectance data and vegetation indices in the prediction of SWC was analyzed. Results showed that when both thermal and multispectral data were used to estimate SWC, the thermal data contributed the most to prediction accuracy, although multispectral data were also important. Reflectance data contributed as much or more to prediction accuracy than most vegetation indices. SWC measurements that had a larger sample size and greater penetration depth (~30 cm sampling depth) were more accurately predicted than smaller and shallower SWC estimates (~18 cm sampling depth). The timing of SWC estimation was also important; higher accuracy predictions were achieved in wetter soils than in drier soils, and a light precipitation event also improved prediction accuracy.<\/jats:p>","DOI":"10.3390\/rs16010061","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:48:37Z","timestamp":1703450917000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Assessing the Potential of UAV-Based Multispectral and Thermal Data to Estimate Soil Water Content Using Geophysical Methods"],"prefix":"10.3390","volume":"16","author":[{"given":"Yunyi","family":"Guan","sequence":"first","affiliation":[{"name":"Department of Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA"}]},{"given":"Katherine","family":"Grote","sequence":"additional","affiliation":[{"name":"Department of Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.2136\/sssaj2008.0016br","article-title":"Field Estimation of Soil Water Content: A Practical Guide to Methods, Instrumentation and Sensor Technology","volume":"73","author":"Robinson","year":"2009","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"358","DOI":"10.2136\/vzj2007.0143","article-title":"Soil Moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review","volume":"7","author":"Robinson","year":"2008","journal-title":"Vadose Zone J."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.20546\/ijcmas.2019.801.238","article-title":"Applications of Remote Sensing in Agriculture\u2014A Review","volume":"8","author":"Palanisamy","year":"2019","journal-title":"Int. J. Curr. Microbiol. Appl. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A Review of Spatial Downscaling of Satellite Remotely Sensed Soil Moisture","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Lu, Z., Li, S., Lei, Y., Chu, Q., Yin, X., and Chen, F. (2020). Large-Scale and High-Resolution Crop Mapping in China Using Sentinel-2 Satellite Imagery. Agriculture, 10.","DOI":"10.3390\/agriculture10100433"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fenvs.2020.00085","article-title":"The Potential of Sentinel-2 for Crop Production Estimation in a Smallholder Agroforestry Landscape, Burkina Faso","volume":"8","author":"Karlson","year":"2020","journal-title":"Front. Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Andreu, F.J., Erena, M., Dominguez-G\u00f3mez, J.A., and L\u00f3pez-Morales, J.A. (2021). Sentinel-2 Images and Machine Learning as Tool for Monitoring of the Common Agricultural Policy: Calasparra Rice as a Case Study. Agronomy, 11.","DOI":"10.3390\/agronomy11040621"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Segarra, J., Buchaillot, M.L., Araus, J.L., and Kefauver, S.C. (2020). Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy, 10.","DOI":"10.3390\/agronomy10050641"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"112150","DOI":"10.1016\/j.rse.2020.112150","article-title":"Modeling the Directional Anisotropy of Fine-Scale TIR Emissions over Tree and Crop Canopies Based on UAV Measurements","volume":"252","author":"Bian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"159595","DOI":"10.1109\/ACCESS.2020.3020325","article-title":"Spectral Index Fusion for Salinized Soil Salinity Inversion Using Sentinel-2A and UAV Images in a Coastal Area","volume":"8","author":"Ma","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6401","DOI":"10.1109\/JSTARS.2020.3034193","article-title":"Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images","volume":"13","author":"Diotto","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112223","DOI":"10.1016\/j.rse.2020.112223","article-title":"Quantifying Plant-Soil-Nutrient Dynamics in Rangelands: Fusion of UAV Hyperspectral-LiDAR, UAV Multispectral-Photogrammetry, and Ground-Based LiDAR-Digital Photography in a Shrub-Encroached Desert Grassland","volume":"253","author":"Sankey","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, Temperature and Narrow-Band Indices Acquired from a UAV Platform for Water Stress Detection Using a Micro-Hyperspectral Imager and a Thermal Camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1029\/2019MS001729","article-title":"Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product","volume":"11","author":"Reichle","year":"2019","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112583","DOI":"10.1016\/j.rse.2021.112583","article-title":"Assessing the Potential of Different Satellite Soil Moisture Products in Landslide Hazard Assessment","volume":"264","author":"Zhao","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Crow, W.T., Berg, A.A., Cosh, M.H., Loew, A., Mohanty, B.P., Panciera, R., de Rosnay, P., Ryu, D., and Walker, J.P. (2012). Upscaling Sparse Ground-Based Soil Moisture Observations for the Validation of Coarse-Resolution Satellite Soil Moisture Products. Rev. Geophys., 50.","DOI":"10.1029\/2011RG000372"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1080\/00380768.2020.1738899","article-title":"Satellite- and Drone-Based Remote Sensing of Crops and Soils for Smart Farming\u2014A Review","volume":"66","author":"Inoue","year":"2020","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"112434","DOI":"10.1016\/j.rse.2021.112434","article-title":"Estimation of Root Zone Soil Moisture from Ground and Remotely Sensed Soil Information with Multisensor Data Fusion and Automated Machine Learning","volume":"260","author":"Babaeian","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8403","DOI":"10.1029\/2009WR008403","article-title":"Experimental Estimation of the GPR Groundwave Sampling Depth","volume":"46","author":"Grote","year":"2010","journal-title":"Water Resour. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.jhydrol.2011.11.034","article-title":"Imaging of Hill-Slope Soil Moisture Wetting Patterns in a Semi-Arid Oak Savanna Catchment Using Time-Lapse Electromagnetic Induction","volume":"416\u2013417","author":"Robinson","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"459","DOI":"10.2136\/sssaj2014.09.0360","article-title":"The Use of Electromagnetic Induction to Monitor Changes in Soil Moisture Profiles beneath Different Wheat Genotypes","volume":"79","author":"Shanahan","year":"2015","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/LGRS.2013.2257673","article-title":"Effect of Aluminum Neutron Probe Access Tubes on the Apparent Electrical Conductivity Recorded by an Electromagnetic Soil Survey Sensor","volume":"11","author":"Stanley","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Toy, C.W., Steelman, C.M., and Endres, A.L. (2010, January 21\u201325). Comparing Electromagnetic Induction and Ground Penetrating Radar Techniques for Estimating Soil Moisture Content. Proceedings of the XIII Internarional Conference on Ground Penetrating Radar, Lecce, Italy.","DOI":"10.1109\/ICGPR.2010.5550068"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1109\/JSTARS.2022.3156878","article-title":"Inter-Comparison of Proximal Near-Surface Soil Moisture Measurement Techniques","volume":"15","author":"Wu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","first-page":"1","article-title":"A Robust Deep Learning Approach for the Quantitative Characterization and Clustering of Peach Tree Crowns Based on UAV Images","volume":"60","author":"Hu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5908","DOI":"10.1109\/ACCESS.2023.3235912","article-title":"Prediction Dynamics in Cotton Aphid Using Unmanned Aerial Vehicle Multispectral Images and Vegetation Indices","volume":"11","author":"Jiang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean Yield Prediction from UAV Using Multimodal Data Fusion and Deep Learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6253","DOI":"10.1109\/JSTARS.2021.3089203","article-title":"Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","first-page":"121","article-title":"Crop monitoring using unmanned aerial vehicles: A review","volume":"42","author":"Cuaran","year":"2021","journal-title":"Agric. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Matese, A., and Di Gennaro, S.F. (2018). Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture. Agriculture, 8.","DOI":"10.3390\/agriculture8070116"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.biosystemseng.2017.08.013","article-title":"Linking Thermal Imaging and Soil Remote Sensing to Enhance Irrigation Management of Sugar Beet","volume":"165","author":"Quebrajo","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_36","first-page":"715","article-title":"UAV\/Satellite Multiscale Data Fusion for Crop Monitoring and Early Stress Detection","volume":"XLII-2\/W13","author":"Sagan","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.agwat.2016.08.026","article-title":"High-Resolution UAV-Based Thermal Imaging to Estimate the Instantaneous and Seasonal Variability of Plant Water Status within a Vineyard","volume":"183","author":"Santesteban","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s00271-012-0382-9","article-title":"Assessment of Vineyard Water Status Variability by Thermal and Multispectral Imagery Using an Unmanned Aerial Vehicle (UAV)","volume":"30","author":"Baluja","year":"2012","journal-title":"Irrig. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11119-013-9334-5","article-title":"Mapping Crop Water Stress Index in a \u2018Pinot-Noir\u2019 Vineyard: Comparing Ground Measurements with Thermal Remote Sensing Imagery from an Unmanned Aerial Vehicle","volume":"15","author":"Bellvert","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Ad\u00e3o, T., Guimar\u00e3es, N., Sousa, A., Peres, E., and Sousa, J.J. (2019). Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts. Agronomy, 9.","DOI":"10.3390\/agronomy9100581"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1080\/10106049.2018.1508312","article-title":"Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas","volume":"35","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/03071375.2013.783746","article-title":"The measurement of plant vitality in landscape trees","volume":"35","author":"Johnstone","year":"2013","journal-title":"Arboric. J."},{"key":"ref_43","unstructured":"Turner, D., Lucieer, A., and Watson, C. (2011, January 10\u201315). Development of an Unmanned Aerial Vehicle (UAV) for Hyper-Resolution Vineyard Mapping Based on Visible, Multispectral and Thermal Imagery. Proceedings of the 34th International Symposium on Remote Sensing of Environment, Sydney, Australia."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105791","DOI":"10.1016\/j.compag.2020.105791","article-title":"A Random Forest Ranking Approach to Predict Yield in Maize with Uav-Based Vegetation Spectral Indices","volume":"178","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.2134\/agronj2019.04.0260","article-title":"Relationship of Drone-Based Vegetation Indices with Corn and Sugarbeet Yields","volume":"111","author":"Olson","year":"2019","journal-title":"Agron. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108096","DOI":"10.1016\/j.agrformet.2020.108096","article-title":"Grain Yield Prediction of Rice Using Multi-Temporal UAV-Based RGB and Multispectral Images and Model Transfer\u2014A Case Study of Small Farmlands in the South of China","volume":"291","author":"Wan","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting Grain Yield in Rice Using Multi-Temporal Vegetation Indices from UAV-Based Multispectral and Digital Imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"126329","DOI":"10.1016\/j.eja.2021.126329","article-title":"Impact Assessment of Soybean Yield and Water Productivity in Brazil Due to Climate Change","volume":"129","author":"Zanon","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2016.10.005","article-title":"Development of Methods to Improve Soybean Yield Estimation and Predict Plant Maturity with an Unmanned Aerial Vehicle Based Platform","volume":"187","author":"Yu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bian, C., Shi, H., Wu, S., Zhang, K., Wei, M., Zhao, Y., Sun, Y., Zhuang, H., Zhang, X., and Chen, S. (2022). Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data. Remote Sens., 14.","DOI":"10.3390\/rs14061474"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fei, S., Hassan, M.A., He, Z., Chen, Z., Shu, M., Wang, J., Li, C., and Xiao, Y. (2021). Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance. Remote Sens., 13.","DOI":"10.3390\/rs13122338"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.plantsci.2018.10.022","article-title":"A Rapid Monitoring of NDVI across the Wheat Growth Cycle for Grain Yield Prediction Using a Multi-Spectral UAV Platform","volume":"282","author":"Hassan","year":"2019","journal-title":"Plant Sci."},{"key":"ref_53","unstructured":"Thomasson, J.A., McKee, M., and Moorhead, R.J. (2019). Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, SPIE."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"107530","DOI":"10.1016\/j.agwat.2022.107530","article-title":"Estimation of Soil Moisture Content under High Maize Canopy Coverage from UAV Multimodal Data and Machine Learning","volume":"264","author":"Cheng","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"9587","DOI":"10.1007\/s13762-022-03958-7","article-title":"A UAV-Aided Prediction System of Soil Moisture Content Relying on Thermal Infrared Remote Sensing","volume":"19","author":"Li","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.geoderma.2018.09.046","article-title":"UAV Based Soil Salinity Assessment of Cropland","volume":"338","author":"Ivushkin","year":"2019","journal-title":"Geoderma"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"e6926","DOI":"10.7717\/peerj.6926","article-title":"Combining UAV-Based Hyperspectral Imagery and Machine Learning Algorithms for Soil Moisture Content Monitoring","volume":"7","author":"Ge","year":"2019","journal-title":"PeerJ"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gu, H., Lin, Z., Guo, W., and Deb, S. (2021). Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images. Remote Sens., 13.","DOI":"10.3390\/rs13010145"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.3390\/rs70302627","article-title":"Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks","volume":"7","author":"Jensen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Gitelson, A.A., Vi\u00f1a, A., Ciganda, V., Rundquist, D.C., and Arkebauer, T.J. (2005). Remote Estimation of Canopy Chlorophyll Content in Crops. Geophys. Res. Lett., 32.","DOI":"10.1029\/2005GL022688"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10106040108542184","article-title":"Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat","volume":"16","author":"Louhaichi","year":"2001","journal-title":"Geocarto Int."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and Photographic Infrared Linear Combinations for Monitoring Vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_65","first-page":"79","article-title":"Combining UAV-Based Plant Height from Crop Surface Models, Visible, and near Infrared Vegetation Indices for Biomass Monitoring in Barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1080\/23754931.2020.1798808","article-title":"UAV-Based High Spatial and Temporal Resolution Monitoring and Mapping of Surface Moisture Status in a Vineyard","volume":"6","author":"Tang","year":"2020","journal-title":"Pap. Appl. Geogr."},{"key":"ref_67","unstructured":"Barnes, E., Clarke, T.R., Richards, S.E., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T.L. (2000, January 16\u201319). Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground Based Multispectral Data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1080\/01431160110107806","article-title":"Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction","volume":"23","author":"Gitelson","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.fcr.2015.03.010","article-title":"Comparing the Performance of Active and Passive Reflectance Sensors to Assess the Normalized Relative Canopy Temperature and Grain Yield of Drought-Stressed Barley Cultivars","volume":"177","author":"Elsayed","year":"2015","journal-title":"Field Crops Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.jhydrol.2004.06.031","article-title":"Field Studies of the GPR Ground Wave Method for Estimating Soil Water Content during Irrigation and Drainage","volume":"301","author":"Galagedara","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1002\/hyp.1351","article-title":"An Analysis of the Ground-Penetrating Radar Direct Ground Wave Method for Soil Water Content Measurement","volume":"17","author":"Galagedara","year":"2003","journal-title":"Hydrol. Process."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Grote, K., Hubbard, S., and Rubin, Y. (2003). Field-Scale Estimation of Volumetric Water Content Using Ground-Penetrating Radar Ground Wave Techniques. Water Resour. Res., 39.","DOI":"10.1029\/2003WR002045"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/S0022-1694(01)00336-5","article-title":"Soil Water Content Measurements at Different Scales: Accuracy of Time Domain Reflectometry and Ground-Penetrating Radar","volume":"245","author":"Huisman","year":"2001","journal-title":"J. Hydrol."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Lu, Y., Song, W., Lu, J., Wang, X., and Tan, Y. (2017). An Examination of Soil Moisture Estimation Using Ground Penetrating Radar in Desert Steppe. Water, 9.","DOI":"10.3390\/w9070521"},{"key":"ref_76","first-page":"55","article-title":"Off- and on-Ground GPR Techniques for Field-Scale Soil Moisture Mapping","volume":"200\u2013201","year":"2013","journal-title":"Geoderma"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1109\/JSTARS.2011.2165939","article-title":"On Mapping Surface Moisture Content of Japanese Andisol Using GPR","volume":"4","author":"Pallavi","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Steelman, C.M., and Endres, A.L. (2010). An Examination of Direct Ground Wave Soil Moisture Monitoring over an Annual Cycle of Soil Conditions. Water Resour. Res., 46.","DOI":"10.1029\/2009WR008815"},{"key":"ref_79","first-page":"7887","article-title":"Determination of the Volumetric Soil Water Content of Two Soil Types Using Ground Penetrating Radar: A Case Study in Thailand","volume":"13","author":"Kummode","year":"2020","journal-title":"EnvironmentAsia"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.jhydrol.2007.04.013","article-title":"Mapping the Spatial Variation of Soil Water Content at the Field Scale with Different Ground Penetrating Radar Techniques","volume":"340","author":"Huisman","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1029\/WR016i003p00574","article-title":"Electromagnetic Determination of Soil Water Content: Measurements in Coaxial Transmission Lines","volume":"16","author":"Topp","year":"1980","journal-title":"Water Resour. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for Land Cover Classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest Classification of Mediterranean Land Cover Using Multi-Seasonal Imagery and Multi-Seasonal Texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.3390\/rs70708368","article-title":"The Improvement of Land Cover Classification by Thermal Remote Sensing","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geoderma.2011.10.010","article-title":"Uncertainty in the Spatial Prediction of Soil Texture: Comparison of Regression Tree and Random Forest Models","volume":"170","author":"Glaser","year":"2012","journal-title":"Geoderma"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"107262","DOI":"10.1016\/j.compag.2022.107262","article-title":"UAV-Based Multispectral and Thermal Cameras to Predict Soil Water Content\u2014A Machine Learning Approach","volume":"200","author":"Bertalan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Lendzioch, T., Langhammer, J., Vl\u010dek, L., and Mina\u0159\u00edk, R. (2021). Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning. Remote Sens., 13.","DOI":"10.5194\/egusphere-egu21-6687"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Guan, Y., Grote, K., Schott, J., and Leverett, K. (2022). Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data. Remote Sens., 14.","DOI":"10.3390\/rs14041023"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Zhu, C., Ding, J., Zhang, Z., and Wang, Z. (2022). Exploring the Potential of UAV Hyperspectral Image for Estimating Soil Salinity: Effects of Optimal Band Combination Algorithm and Random Forest. Spectrochim. Acta Part A Mol. Biomol. Spectrosc., 279.","DOI":"10.1016\/j.saa.2022.121416"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Hu, J., Peng, J., Zhou, Y., Xu, D., Zhao, R., Jiang, Q., Fu, T., Wang, F., and Shi, Z. (2019). Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images. Remote Sens., 11.","DOI":"10.3390\/rs11070736"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Yuzugullu, O., Lorenz, F., Fr\u00f6hlich, P., and Liebisch, F. (2020). Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12071116"},{"key":"ref_92","unstructured":"Gholamy, A., Kreinovich, V., and Kosheleva, O. (2023, November 05). Why 70\/30 or 80\/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. Departmental Technical Reports (CS) 2018. Available online: https:\/\/scholarworks.utep.edu\/cs_techrep\/1209\/."},{"key":"ref_93","first-page":"54","article-title":"Food-Finding Capability of Grape Root Borer (Lepidoptera: Sesiidae) Neonates in Soil Column Bioassays","volume":"51","author":"Rijal","year":"2016","journal-title":"J. Entomol. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5344\/ajev.2006.57.1.89","article-title":"Grapevine Rooting Patterns: A Comprehensive Analysis and a Review","volume":"57","author":"Smart","year":"2006","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_95","unstructured":"U.S. Geological Survey (2022, March 15). What Are the Best Landsat Spectral Bands for Use in My Research?|U.S. Geological Survey, Available online: https:\/\/www.usgs.gov\/faqs\/what-are-best-landsat-spectral-bands-use-my-research."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"133390","DOI":"10.1016\/j.scitotenv.2019.07.196","article-title":"Effects of Vineyard Soil Management on the Characteristics of Soils and Roots in the Lower Oltrep\u00f2 Apennines (Lombardy, Italy)","volume":"693","author":"Bordoni","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5344\/ajev.2003.54.1.1","article-title":"Grapevine Root System and Soil Characteristics in a Vineyard Maintained Long-Term with or without Interrow Sward","volume":"54","author":"Morlat","year":"2003","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"393","DOI":"10.21273\/HORTSCI.45.3.393","article-title":"Relative Rooting Depths of Native Grasses and Amenity Grasses with Potential for Use on Roadsides in New England","volume":"45","author":"Brown","year":"2010","journal-title":"HortScience"},{"key":"ref_99","unstructured":"(2022, March 15). Spectral Reflectance. Available online: http:\/\/gsp.humboldt.edu\/olm\/Courses\/GSP_216\/lessons\/reflectance.html."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s42834-019-0016-5","article-title":"Near Infrared Band of Landsat 8 as Water Index: A Case Study around Cordova and Lapu-Lapu City, Cebu, Philippines","volume":"29","author":"Mondejar","year":"2019","journal-title":"Sustain. Environ. Res."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.eja.2008.04.007","article-title":"Spatial and Temporal Changes to the Water Regime of a Mediterranean Vineyard Due to the Adoption of Cover Cropping","volume":"29","author":"Celette","year":"2008","journal-title":"Eur. J. Agron."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e00631","DOI":"10.1016\/j.geodrs.2023.e00631","article-title":"Role of Cultivars and Grass in the Stability of Soil Moisture and Temperature in an Organic Vineyard","volume":"33","author":"Wu","year":"2023","journal-title":"Geoderma Reg."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/61\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:40:39Z","timestamp":1760132439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/1\/61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,22]]},"references-count":102,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16010061"],"URL":"https:\/\/doi.org\/10.3390\/rs16010061","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1007\/s12145-024-01662-3","asserted-by":"object"}]},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,22]]}}}