{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:55:00Z","timestamp":1776092100293,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mississippi Department of Transportation (MDOT)","award":["State Study 316"],"award-info":[{"award-number":["State Study 316"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi\u2019s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned\/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (\u03b8) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict \u03b8. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting \u03b8 from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were &gt;0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (\u0394LST) from UAV-captured Thermal Infrared (TIR) images to predict \u03b8. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract \u0394LST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from \u0394LST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted \u03b8 with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.<\/jats:p>","DOI":"10.3390\/rs15071888","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T01:37:05Z","timestamp":1680485825000},"page":"1888","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Near-Surface Soil Moisture Characterization in Mississippi\u2019s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2466-1121","authenticated-orcid":false,"given":"Rakesh","family":"Salunke","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, JSU Box 17068, Jackson, MS 39217, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8457-7234","authenticated-orcid":false,"given":"Masoud","family":"Nobahar","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, JSU Box 17068, Jackson, MS 39217, USA"}]},{"given":"Omer Emad","family":"Alzeghoul","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, JSU Box 17068, Jackson, MS 39217, USA"}]},{"given":"Sadik","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, JSU Box 17068, Jackson, MS 39217, USA"}]},{"given":"Ian","family":"La Cour","sequence":"additional","affiliation":[{"name":"Mississippi Department of Transportation (MDOT), Materials Division, Geotechnical Branch, 412 E. Woodrow Wilson Ave., Jackson, MS 39216, USA"}]},{"given":"Farshad","family":"Amini","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, JSU Box 17068, Jackson, MS 39217, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fusco, F., Mirus, B.B., Baum, R.L., Calcaterra, D., and de Vita, P. (2021). Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments. Water, 13.","DOI":"10.3390\/w13050713"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2004.06.017","article-title":"Influence of rainfall-induced wetting on the stability of slopes in weathered soils","volume":"75","author":"Kim","year":"2004","journal-title":"Eng. 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