{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T08:38:56Z","timestamp":1771922336535,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,6]],"date-time":"2022-08-06T00:00:00Z","timestamp":1659744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"North Dakota Agricultural Experiment Station","doi-asserted-by":"publisher","award":["USDA-NRCS-CIG 69-3A75-17-282"],"award-info":[{"award-number":["USDA-NRCS-CIG 69-3A75-17-282"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000199","name":"North Dakota Agricultural Experiment Station","doi-asserted-by":"publisher","award":["1005366"],"award-info":[{"award-number":["1005366"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"name":"North Dakota Water Resources Research Institute","award":["USDA-NRCS-CIG 69-3A75-17-282"],"award-info":[{"award-number":["USDA-NRCS-CIG 69-3A75-17-282"]}]},{"name":"North Dakota Water Resources Research Institute","award":["1005366"],"award-info":[{"award-number":["1005366"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, soil clay percentage, and the standardized precipitation index play key roles in determining the moisture content of crop fields. The objective of this study was to (i) calculate and determine the effectiveness of moisture-related indices in predicting surface SM, (ii) predict surface SM from satellite images using the Optical Trapezoid Model (OPTRAM), and (iii) evaluate if the OPTRAM predictions can be improved by incorporating weather station, soil, and crop data with a random forest algorithm. The ENVI\u00ae platform was used to create moisture-related indices maps, and the Google Earth Engine (GEE) was used to prepare OPTRAM maps. The results showed a very weak relationship between the moisture-related indices and surface SM content where r2 and slopes were \u02c20.10 and \u02c20.20, respectively. OPTRAM SM, when compared with in situ surface moisture, showed weak relationship with regression values \u02c20.2. Surface SM was then predicted using random forest regression using OPTRAM moisture values, rainfall, and the standardized precipitation index (SPI), and percent clay showed high goodness of fit (r2 = 0.69) and low root mean square error (RMSE = 0.053 m3 m\u22123).<\/jats:p>","DOI":"10.3390\/rs14153801","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"3801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Umesh","family":"Acharya","sequence":"first","affiliation":[{"name":"CFAES Rattan Lal Center for Carbon Management and Sequestration, The Ohio State University, Columbus, OH 43210, USA"}]},{"given":"Aaron L. M.","family":"Daigh","sequence":"additional","affiliation":[{"name":"Department of Soil Science, School of Natural Resources Sciences, North Dakota State University, Fargo, ND 58102, USA"}]},{"given":"Peter G.","family":"Oduor","sequence":"additional","affiliation":[{"name":"Department of Geoscience, North Dakota State University, Fargo, ND 58102, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,6]]},"reference":[{"key":"ref_1","unstructured":"Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2008). Remote Sensing and Image Interpretation, John Wiley & Sons."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/S0378-3774(00)00080-9","article-title":"Remote sensing for irrigated agriculture: Examples from research and possible applications","volume":"46","author":"Bastiaanssen","year":"2000","journal-title":"Agric. Water Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1080\/01431169308954010","article-title":"The reflectance at the 950\u2013970 mm region as an indicator of plant water status","volume":"14","author":"Penuelas","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0034-4257(80)90096-6","article-title":"Remote sensing of leaf water content in the near infrared","volume":"10","author":"Tucker","year":"1980","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.2136\/sssaj2016.06.0188","article-title":"Predicting near-surface SM content of saline soils from NIR reflectance spectra with a Modified Gaussian model","volume":"80","author":"Zeng","year":"2016","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Zhou, G. (2016). Estimation of SM from optical and thermal remote sensing: A review. Sensors, 16.","DOI":"10.3390\/s16081308"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.3390\/rs6043170","article-title":"Surface soil water content estimation from thermal remote sensing based on the temporal variation of land surface temperature","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1084669","DOI":"10.1080\/23312041.2015.1084669","article-title":"Present status of SM estimation by microwave remote sensing","volume":"1","author":"Das","year":"2015","journal-title":"Cogent Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/s8010070","article-title":"Assessment of evapotranspiration and SM content across different scales of observation","volume":"8","author":"Verstraeten","year":"2008","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11707-009-0023-7","article-title":"Satellite remote sensing applications for surface SM monitoring: A review","volume":"3","author":"Wang","year":"2009","journal-title":"Front. Earth Sci.-Prc"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/2016RG000543","article-title":"A review of spatial downscaling of satellite remotely sensed SM","volume":"55","author":"Peng","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/S0034-4257(01)00293-0","article-title":"The potential of directional radiometric temperatures for monitoring soil and leaf temperature and SM status","volume":"80","author":"Francois","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1080\/014311600210876","article-title":"Toward remote sensing methods for land cover dynamics monitoring, application to Morocco","volume":"20","author":"Sobrino","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/BF00865985","article-title":"Estimates of surface soil water content using linear combinations of spectral wavebands","volume":"42","author":"Levit","year":"1990","journal-title":"Theor. Appl. Climatol."},{"key":"ref_15","first-page":"937","article-title":"A moisture index for surface characterization over a semiarid area","volume":"65","author":"Lewis","year":"1999","journal-title":"Photogramm. Eng. Rem. Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0034-4257(01)00274-7","article-title":"A simple interpretation of the surface temperature\/vegetation index space for assessment of SM status","volume":"79","author":"Sandholt","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.isprsjprs.2005.02.003","article-title":"A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains","volume":"59","author":"Tadesse","year":"2005","journal-title":"ISPRS J. Photogramm."},{"key":"ref_18","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1973). Monitoring Vegetation Systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium, Technical Presentations, Section A."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/S0034-4257(01)00191-2","article-title":"Detecting vegetation water content using reflectance in the optical domain","volume":"77","author":"Ceccato","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.rse.2003.10.021","article-title":"Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans","volume":"92","author":"Jackson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/0034-4257(88)90033-8","article-title":"Estimating surface soil moisture from satellite microwave measurements and a satellite derived vegetation index","volume":"24","author":"Owe","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.13031\/2013.18520","article-title":"Estimation of long-term soil moisture using a distributed parameter hydrologic model and verification using remotely sensed data","volume":"48","author":"Narasimhan","year":"2005","journal-title":"Trans. ASAE"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liang, S.L. (2004). Quantitative Remote Sensing of land Surface, John Wiley & Sons.","DOI":"10.1002\/047172372X"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2013.06.004","article-title":"Estimation of SM using optical\/thermal infrared remote sensing in the Canadian Prairies","volume":"83","author":"Berg","year":"2013","journal-title":"ISPRS J. Photogramm."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.3390\/rs70302352","article-title":"Index of SM using raw Landsat image digital count data in Texas high plains","volume":"7","author":"Shafian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/JSTARS.2015.2500605","article-title":"Two-stage trapezoid: A new interpretation of the land surface temperature and fractional vegetation coverage space","volume":"9","author":"Sun","year":"2016","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.rse.2017.05.041","article-title":"The optical trapezoid model: A novel approach to remote sensing of SM applied to Sentinel-2 and Landsat-8 observations","volume":"198","author":"Sadeghi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_29","first-page":"338","article-title":"A practical algorithm for estimating surface SM using combined optical and thermal infrared data","volume":"52","author":"Leng","year":"2016","journal-title":"Int. J. Appl. Earth. Obs."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pan, F., Peters-Lidard, C.D., and Sale, M.J. (2003). An analytical method for predicting surface soil moisture from rainfall observations. Water Resour. Res., 39.","DOI":"10.1029\/2003WR002142"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4421","DOI":"10.1007\/s12665-013-2837-6","article-title":"Evaluation of TRMM rainfall for soil moisture prediction in a subtropical climate","volume":"71","author":"Gupta","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.isprsjprs.2017.07.013","article-title":"A practical approach for deriving all-weather SM content using combined satellite and meteorological data","volume":"131","author":"Leng","year":"2017","journal-title":"ISPRS J. Photogramm."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1002\/grl.50173","article-title":"A new method for rainfall estimation through SM observations","volume":"40","author":"Brocca","year":"2013","journal-title":"Geophys. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, W., Ma, M., Li, Z., Tan, J., and Wu, A. (2017). New Scheme for validating remote-sensing land surface temperature products with station observations. Remote Sens., 9.","DOI":"10.3390\/rs9121210"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.rse.2018.04.029","article-title":"Mapping SM with the OPtical TRApezoid Model (OPTRAM) based on long-term MODIS observations","volume":"211","author":"Babaeian","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yadav, S.K., Singh, P., Jadaun, S.P.S., Kumar, N., and Upadhyay, R.K. (2019, January 18\u201320). SM analysis of Lalitpur district Uttar Pradesh India using Landsat and sentinel data. Proceedings of the International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, ISPRS-GEOGLAM-ISRS Joint Int. Workshop on \u201cEarth Observations for Agricultural Monitoring\u201d, New Delhi, India.","DOI":"10.5194\/isprs-archives-XLII-3-W6-351-2019"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2020EA001108","DOI":"10.1029\/2020EA001108","article-title":"Evaluation of the OPTRAM Model to retrieve soil moisture in the Sanjiang Plain of Northeast China","volume":"7","author":"Chen","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1080\/01431160410001712990","article-title":"Mapping SM in the central Ebro river valley (northeast Spain) with Landsat and NOAA satellite imagery: A comparison with meteorological data","volume":"25","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1175\/2007JHM863.1","article-title":"An analysis of the SM feedback on convective and stratiform precipitation","volume":"9","author":"Alfieri","year":"2008","journal-title":"J. Hydrometeorol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1002\/2017JD027035","article-title":"The importance of soil-type contrast in modulating August precipitation distribution new the Edwards Plateau and Balcones Escarpment in Texas","volume":"122","author":"Hu","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"16398","DOI":"10.3390\/rs71215841","article-title":"Review of machine learning approaches for biomass and SM retrievals from remote sensing data","volume":"7","author":"Ali","year":"2015","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3274","DOI":"10.1109\/TGRS.2008.920370","article-title":"A comparison of algorithms for retrieving SM from ENVISAT\/ASAR images","volume":"46","author":"Paloscia","year":"2008","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/0022-1694(70)90066-1","article-title":"The gravimetric method of SM determination Part I: A study of equipment, and methodological problems","volume":"11","author":"Reynolds","year":"1970","journal-title":"J. Hydrol."},{"key":"ref_44","unstructured":"USDA. United States Department of Agriculture, International Production Assessment Division (2020, August 05). Metadata for Crops at Different Growth Stage, Available online: https:\/\/ipad.fas.usda.gov\/cropexplorer\/description.aspx?legendid=312."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsat 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shi, L., Mao, Z., Chen, P., Gong, F., and Zhu, Q. (2016). Comparison and evaluation of atmospheric correction algorithms of QUAC, DOS, and FLAASH for HICO hyperspectral imagery. Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions, International Society for Optics and Photonics.","DOI":"10.1117\/12.2241368"},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"Mcfeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/S0034-4257(02)00151-7","article-title":"Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: A comparison of indices based on liquid water and chlorophyll absorption features","volume":"84","author":"Sims","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1109\/TGRS.1995.8746027","article-title":"A feedback-based modification of the NDVI to minimize canopy background and atmospheric noise","volume":"33","author":"Liu","year":"1995","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_52","first-page":"221","article-title":"Semi-empirical indices to assess carotenoids\/chlorophyll a ratio from leaf spectral reflectance","volume":"31","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2015.04.007","article-title":"A linear physically-based model for remote sensing of SM using short wave infrared bands","volume":"164","author":"Sadeghi","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"(2020, June 01). Google Earth Engine. Google Earth Engine: A Planetary-Scale Platform for Earth Science Data & Analysis. Available online: https:\/\/earthengine.google.com."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Mckee, T.B.N., Doesken, J., and Kleist, J. (1993, January 17\u201322). The relationship of drought frequency and duration to time scales. Proceedings of the Eighth Conference on Applied Climatology, Anaheim, CA, USA."},{"key":"ref_58","unstructured":"Abramowitz, M., and Stegun, I.A. (1948). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables."},{"key":"ref_59","unstructured":"NDMC. National Drought Mitigation Center (2020, July 15). Explanation of the US Drought Monitor. Available online: http:\/\/droughtmonitor.unl.edu\/classify.htm."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Acharya, U., Daigh, A.L., and Oduor, P.G. (2021). Machine learning for predicting field soil moisture using soil, crop, and nearby weather station data in the Red River Valley of the North. Soil Syst., 5.","DOI":"10.3390\/soilsystems5040057"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.jhydrol.2004.10.019","article-title":"Microwave remote sensing of SM: Evaluation of the TRMM microwave imager (TMI) satellite for the Little River Watershed Tifton, Georgia","volume":"307","author":"Cashion","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.asr.2006.02.040","article-title":"Validation of satellite-derived soil-vegetation indices for prognosis of spring cereals yield reduction under drought conditions\u2013Case study from central-western Poland","volume":"39","author":"Martyniak","year":"2007","journal-title":"Adv. Space Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jhydrol.2007.03.022","article-title":"Different responses of MODIS-derived NDVI to root-zone SM in semi-arid and humid regions","volume":"340","author":"Wang","year":"2007","journal-title":"J. Hydrol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1175\/1525-7541(2002)003<0395:RBSMAS>2.0.CO;2","article-title":"Relations between SM and satellite vegetation indices in the US Corn Belt","volume":"3","author":"Adegoke","year":"2002","journal-title":"J. Hydrometeorol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1175\/1520-0477-79.5s.S1","article-title":"Climate assessment for 1997","volume":"79","author":"Bell","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1126\/science.266.5193.1975","article-title":"Isotopic composition of old ground water from Lake Agassiz: Implications for late Pleistocene climate","volume":"266","author":"Remenda","year":"1994","journal-title":"Science"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Adab, H., Morbidelli, R., Saltalippi, C., Moradian, M., and Ghalhari, G.A.F. (2020). Machine learning to estimate surface SM from remote sensing data. Water, 12.","DOI":"10.3390\/w12113223"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.5194\/hess-25-2739-2021","article-title":"Advances in SM retrieval from multispectral remote sensing using unmanned aircraft systems and machine learning techniques","volume":"25","author":"Araya","year":"2021","journal-title":"Hydrol. Earth Syst. Sc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/TGRS.2002.803790","article-title":"On current limits of SM retrieval from ERS-SAR data","volume":"40","author":"Satalino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2013.02.027","article-title":"SM mapping using Sentinel-1 images: Algorithm and preliminary validation","volume":"134","author":"Paloscia","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"106823","DOI":"10.1016\/j.ecolind.2020.106823","article-title":"Modification of the Land Surface Temperature\u2013Vegetation Index Triangle Method for soil moisture condition estimation by using SYNOP reports","volume":"119","author":"Zawadzki","year":"2020","journal-title":"Ecol. Indic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3801\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:17Z","timestamp":1760141117000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,6]]},"references-count":73,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153801"],"URL":"https:\/\/doi.org\/10.3390\/rs14153801","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,6]]}}}