{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T18:39:04Z","timestamp":1775673544907,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"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>The volumetric water content (VWC) of soil is a critical parameter in agriculture, as VWC strongly influences crop yield, provides nutrients to plants, and maintains the microbes that are needed for the biological health of the soil. Measuring VWC is difficult, as it is spatially and temporally heterogeneous, and most agricultural producers use point measurements that cannot fully capture this parameter. Electrical conductivity (EC) is another soil parameter that is useful in agriculture, since it can be used to indicate soil salinity, soil texture, and plant nutrient availability. Soil EC is also very heterogeneous; measuring EC using conventional soil sampling techniques is very time consuming and often fails to capture the variability in EC at a site. In contrast to the point-based methods used to measure VWC and EC, multispectral data acquired with unmanned aerial vehicles (UAV) can cover large areas with high resolution. In agriculture, multispectral data are often used to calculate vegetation indices (VIs). In this research UAV-acquired VIs and raw multispectral data were used to predict soil VWC and EC. High-resolution geophysical methods were used to acquire more than 41,000 measurements of VWC and 8000 measurements of EC in 18 traverses across a field that contained 56 experimental plots. The plots varied by crop type (corn, soybeans, and alfalfa) and drainage (no drainage, moderate drainage, high drainage). Machine learning was performed using the random forest method to predict VWC and EC using VIs and multispectral data. Prediction accuracy was determined for several scenarios that assumed different levels of knowledge about crop type or drainage. Results showed that multispectral data improved prediction of VWC and EC, and the best predictions occurred when both the crop type and degree of drainage were known, but drainage was a more important input than crop type. Predictions were most accurate in drier soil, which may be due to the lower overall variability of VWC and EC under these conditions. An analysis of which multispectral data were most important showed that NDRE, VARI, and blue band data improved predictions the most. The final conclusions of this study are that inexpensive UAV-based multispectral data can be used to improve estimation of heterogenous soil properties, such as VWC and EC in active agricultural fields. In this study, the best estimates of these properties were obtained when the agriculture parameters in a field were fairly homogeneous (one crop type and the same type of drainage throughout the field), although improvements were observed even when these conditions were not met. The multispectral data that were most useful for prediction were those that penetrated deeper into the soil canopy or were sensitive to bare soil.<\/jats:p>","DOI":"10.3390\/rs14041023","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:23:29Z","timestamp":1645431809000},"page":"1023","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Prediction of Soil Water Content and Electrical Conductivity Using Random Forest Methods with UAV Multispectral and Ground-Coupled Geophysical Data"],"prefix":"10.3390","volume":"14","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"}]},{"given":"Joel","family":"Schott","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA"}]},{"given":"Kelsi","family":"Leverett","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":[[2022,2,20]]},"reference":[{"key":"ref_1","first-page":"455","article-title":"Application of Soil Electrical Conductivity to Precision Agriculture","volume":"95","author":"Corwin","year":"2003","journal-title":"Agron. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1007\/s11119-012-9277-2","article-title":"Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery","volume":"13","author":"Guo","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_3","first-page":"16","article-title":"Applications of Soil Electrical Conductivity In Production Agriculture","volume":"93","author":"Wiatrak","year":"2009","journal-title":"Better Crops"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Robinson, D., Lebron, I., Kocar, B., Phan, K., Sampson, M., Crook, N., and Fendorf, S. (2009). Time-lapse geophysical imaging of soil moisture dynamics in tropical deltaic soils: An aid to interpreting hydrological and geochemical processes. Water Resour. Res., 45.","DOI":"10.1029\/2008WR006984"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.eja.2014.12.004","article-title":"Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements","volume":"64","author":"Stadler","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_6","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_7","unstructured":"Chan, D., Rajeev, P., Gallage, C., and Kodikara, J. (2010, January 10\u201313). Taiwan Ge-Otechnical Society\/Southeast Asian Geotechnical Society. Proceedings of the Seventeenth Southeast Asian Geotechnical Conference, Taipei, Taiwan."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"986","DOI":"10.2136\/vzj2005.0099","article-title":"Advances in Soil Water Content Sensing: The Continuing Maturation of Technology and Theory","volume":"4","author":"Evett","year":"2005","journal-title":"Vadose Zone J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1006\/jaer.1998.0338","article-title":"Soil Water Content Measurement with a High-Frequency Capacitance Sensor","volume":"71","author":"Gardner","year":"1998","journal-title":"J. Agric. Eng. Res."},{"key":"ref_10","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_11","unstructured":"Piikki, K., S\u00f6derstr\u00f6m, M., Wetterlind, J., and Stenberg, B. (2015). Digital Soil Mapping for Modelling of Transport Pathways for Pesticides to Surface Water, Swedish University of Agricultural Sciences."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0022-1694(99)00121-3","article-title":"Measurement of relative permittivity in sandy soils using TDR, capacitance and theta probes: Comparison, including the effects of bulk soil electrical conductivity","volume":"223","author":"Robinson","year":"1999","journal-title":"J. Hydrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"444","DOI":"10.2136\/vzj2003.4440","article-title":"A review of advances in dielectric and electrical conductivity measurement in soils using time domain reflectometry","volume":"2","author":"Robinson","year":"2003","journal-title":"Vadose Zone J."},{"key":"ref_14","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_15","first-page":"476","article-title":"Measuring Soil Water Content with Ground Penetrating Radar: A Review","volume":"2","author":"Huisman","year":"2003","journal-title":"Vadose Zone J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"180052","DOI":"10.2136\/vzj2018.03.0052","article-title":"Measuring Soil Water Content with Ground Penetrating Radar: A Decade of Progress","volume":"17","author":"Klotzsche","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.geoderma.2019.02.024","article-title":"Application of ground penetrating radar methods in soil studies: A review","volume":"343","author":"Chuman","year":"2019","journal-title":"Geoderma"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"352","DOI":"10.2134\/agronj2003.3520","article-title":"Identifying Soil Properties That Influence Cotton Yield Using Soil Sampling Directed by Apparent Soil Electrical Conductivity","volume":"95","author":"Corwin","year":"2003","journal-title":"Agron. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.compag.2004.10.005","article-title":"Apparent soil electrical conductivity measurements in agriculture","volume":"46","author":"Corwin","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"285","DOI":"10.15666\/aeer\/1404_285295","article-title":"Monitoring of Total Dissolved Solids on Agricultural Lands Using Electrical Conductivity Measurements","volume":"14","author":"Lech","year":"2016","journal-title":"Appl. Ecol. Environ. Res."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2019.01.006","article-title":"Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems","volume":"223","author":"Vaudour","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., and Van Wesemael, B. (2019). Sampling Strategies for Soil Property Mapping Using Multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data. Remote Sens., 11.","DOI":"10.3390\/rs11030309"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Santaga, F.S., Agnelli, A., Leccese, A., and Vizzari, M. (2021). Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy. Remote Sens., 13.","DOI":"10.3390\/rs13173379"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106387","DOI":"10.1016\/j.agwat.2020.106387","article-title":"Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lez\u00edria Grande, Portugal","volume":"241","author":"Ramos","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zribi, M., Nativel, S., and Le Page, M. (2021). Analysis of Agronomic Drought in a Highly Anthropogenic Context Based on Satellite Monitoring of Vegetation and Soil Moisture. Remote Sens., 13.","DOI":"10.3390\/rs13142698"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, Y., Zhang, J., Bao, Z., Ao, T., Wang, G., Wu, H., and Wang, J. (2021). Evaluation of Multi-Source Soil Moisture Datasets over Central and Eastern Agricultural Area of China Using in Situ Monitoring Network. Remote Sens., 13.","DOI":"10.3390\/rs13061175"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Han, L., Wang, C., Yu, T., Gu, X., and Liu, Q. (2020). High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas using Chinese GF-3 and GF-1 Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12132123"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1029\/2018RG000618","article-title":"Ground, Proximal, and Satellite Remote Sensing of Soil Moisture","volume":"57","author":"Babaeian","year":"2019","journal-title":"Rev. Geophys."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17159\/sajs.2020\/6535","article-title":"Estimating Soil Moisture Using Sentinel-1 and Sentinel-2 Sensors for Dryland and Palustrine Wetland Areas","volume":"116","author":"Gangat","year":"2020","journal-title":"S. Afr. J. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7448","DOI":"10.1109\/JSTARS.2021.3098513","article-title":"Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture","volume":"14","author":"Liu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_34","unstructured":"(2021, June 11). Sentinel-2\u2014Missions\u2014Sentinel Online. Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_35","unstructured":"(2021, January 11). Sentinel 2\u2014Bands and Combinations\u2014GIS Geography. Available online: https:\/\/gisgeography.com\/sentinel-2-bands-combinations\/."},{"key":"ref_36","first-page":"216","article-title":"Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery","volume":"Volume 11008","author":"Thomasson","year":"2019","journal-title":"Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.agwat.2017.11.011","article-title":"Effective and efficient agricultural drainage pipe mapping with UAS thermal infrared imagery: A case study","volume":"197","author":"Allred","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106036","DOI":"10.1016\/j.agwat.2020.106036","article-title":"Overall results and key findings on the use of UAV visible-color, multispectral, and thermal infrared imagery to map agricultural drainage pipes","volume":"232","author":"Allred","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104946","DOI":"10.1016\/j.compag.2019.104946","article-title":"Agricultural drainage tile surveying using an unmanned aircraft vehicle paired with Real-Time Kinematic positioning\u2014A case study","volume":"165","author":"Freeland","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","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_41","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_42","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.3390\/rs70302627","article-title":"Assessment of Surface Soil Moisture Using High-Resolution Mul-ti-Spectral Imagery and Artificial Neural Networks","volume":"7","author":"Jensen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sona, G., Passoni, D., Pinto, L., Pagliari, D., Masseroni, D., Ortuani, B., and Facchi, A. (2016). UAV Multispectral Survey to Map Soil and Crop for Precision Farming Applications, International Society for Photogrammetry and Remote Sensing (ISPRS).","DOI":"10.5194\/isprsarchives-XLI-B1-1023-2016"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2489\/jswc.74.1.1","article-title":"Delineation of Tile-Drain Networks Using Thermal and Mul-tispectral Imagery\u2014Implications for Water Quantity and Quality Differences from Paired Edge-of-Field Sites","volume":"74","author":"Williamson","year":"2018","journal-title":"J. Soil Water Conserv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., and Landivar, J. (2019). Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sens., 11.","DOI":"10.3390\/rs11131548"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The application of small unmanned aerial systems for precision agriculture: A review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"012022","DOI":"10.1088\/1755-1315\/275\/1\/012022","article-title":"A Review on the Use of Drones for Precision Agriculture","volume":"275","author":"Daponte","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Delgado Vera, C., Aguirre Munizaga, M., Jim\u00e9nez, M., Manobanda, N., and Rodr\u00edguez-M\u00e9ndez, A. (2017). A Photogrammetry Software as a Tool for Precision Agriculture: A Case Study, Springer.","DOI":"10.1007\/978-3-319-67283-0_21"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Govorcin, M., Pribicevic, B., and \u0110apo, A. (2014, January 17\u201326). Comparison and Analysis of Software Solutions for Creation of a Digital Terrain Model Using Unmanned Aerial Vehicles. Proceedings of the 14th International Multidisciplinary Scientific Geo-Conference and Expo 2014 (SGEM 2014), Albena, Bulgaria.","DOI":"10.5593\/SGEM2014\/B23\/S10.013"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ziliani, M., Parkes, S., Hoteit, I., and McCabe, M. (2018). Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV. Remote Sens., 10.","DOI":"10.3390\/rs10122007"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Li, S., Yuan, F., Ata-Ui-Karim, S., Zheng, H., Cheng, T., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q. (2019). Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11151763"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Zheng, H., Cheng, T., Li, D., Zhou, X., Yao, X., Tian, Y., Cao, W., and Zhu, Y. (2018). Evaluation of RGB, Color-Infrared and Multispectral Images Acquired from Unmanned Aerial Systems for the Estimation of Nitrogen Accumulation in Rice. Remote Sens., 10.","DOI":"10.3390\/rs10060824"},{"key":"ref_55","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":"Ramos","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_56","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_57","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_58","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_59","doi-asserted-by":"crossref","unstructured":"Easterday, K., Kislik, C., Dawson, T., Hogan, S., and Kelly, M. (2019). Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs). Remote Sens., 11.","DOI":"10.20944\/preprints201907.0083.v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.2135\/cropsci2013.02.0126","article-title":"Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield","volume":"53","author":"Fritschi","year":"2013","journal-title":"Crop Sci."},{"key":"ref_61","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_62","unstructured":"(2021, January 11). Band Multispectral Band Math. Sentera. Retrieved 31 October 2019. Available online: https:\/\/sentera.com\/."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_65","unstructured":"Barnes, E., Clarke, T., Richards, S., Colaizzi, P., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., and Thompson, T. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the 5th International Conference on Precision Agriculture and Other Resource Management, Bloomington, MN, USA."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships Between Leaf Chlorophyll Content and Spectral Reflectance and Algo-rithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_68","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_69","unstructured":"Jol, H. (2009). Ground Penetrating Radar Theory and Applications, Elsevier."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.2136\/vzj2004.0143","article-title":"Numerical Modeling of GPR to Determine the Direct Ground Wave Sampling Depth","volume":"4","author":"Galagedara","year":"2005","journal-title":"Vadose Zone J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"SBH5-1\u2013SBH5-13","DOI":"10.1029\/2003WR002045","article-title":"Field-scale estimation of volumetric water content using ground-penetrating radar ground wave techniques","volume":"39","author":"Grote","year":"2003","journal-title":"Water Resour. Res."},{"key":"ref_72","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_73","first-page":"78","article-title":"Determination of the Volumetric Soil Water Content of Two Soil Types Using Ground Penetrating Radar: A Case Study in Thailand","volume":"12","author":"Kummode","year":"2020","journal-title":"Environ. Asia"},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0926-9851(99)00058-0","article-title":"Dielectric Constant Determination Using Ground-Penetrating Radar Reflection Coefficients","volume":"43","author":"Reppert","year":"2000","journal-title":"J. Appl. Geophys."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"705","DOI":"10.2136\/vzj2004.0705","article-title":"Measurement of the Solid Dielectric Permittivity of Clay Minerals And Granular Samples Using A Time Domain Reflectometry Immersion Method","volume":"3","author":"Robinson","year":"2004","journal-title":"Vadose Zone J."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/0022-1694(93)90234-Z","article-title":"Field evaluation of time domain reflectometry for soil water measurements","volume":"151","author":"Jacobsen","year":"1993","journal-title":"J. Hydrol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1365-2389.1992.tb00115.x","article-title":"Empirical evaluation of the relationship between soil dielectric constant and volumetric water content as the basis for calibrating soil moisture measurements by TDR","volume":"43","author":"Roth","year":"1992","journal-title":"Eur. J. Soil Sci."},{"key":"ref_79","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_80","doi-asserted-by":"crossref","unstructured":"Grote, K., Crist, T., and Nickel, C. (2010). Experimental estimation of the GPR groundwave sampling depth. Water Resour. Res., 46.","DOI":"10.1029\/2009WR008403"},{"key":"ref_81","unstructured":"Lund, E., Christy, C., and Drummond, P. (2000, January 16\u201319). Using Yield and Soil Electrical Conductivity (EC) Maps to Derive Crop Production Performance Information. Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/S0103-90162013000100001","article-title":"Spatial and temporal variability of soil electrical conductivity related to soil moisture","volume":"70","author":"Molin","year":"2013","journal-title":"Sci. Agricola"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_84","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_85","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1016\/j.ecolind.2015.08.036","article-title":"Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem","volume":"60","author":"Yang","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_86","unstructured":"(2021, September 15). Reflected Near-Infrared Waves|Science Mission Directorate, Available online: https:\/\/science.nasa.gov\/ems\/08_nearinfraredwaves."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/1023\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:28Z","timestamp":1760135008000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/1023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,20]]},"references-count":86,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14041023"],"URL":"https:\/\/doi.org\/10.3390\/rs14041023","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,20]]}}}