# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## Abbreviations  
* [1] [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] An analytical model for estimating soil temperature profiles on the Qinghai-Tibet Plateau of China. _J. Arid Land_**2016**, \(8\), 232-240. [CrossRef]
* [2] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Grazing modulates soil temperature and moisture in a Eurasian steppe. _Agric. For. Meteorol._**2018**, _262_, 157-165. [CrossRef]
* [3] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A review of machine learning approaches to soil temperature estimation. _Sustainability_**2023**, _15_, 7677. [CrossRef]
* [4] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Predicting soil temperatures under a ridge-furrow system in the US Corn Belt. _Soil Village Res._**1990**, _18_, 145-165. [CrossRef]
* [5] [PERSON]; [PERSON] Modeling plant canopy effects on variability of soil temperature and water. _Agric. For. Meteorol._**1991**, _56_, 227-246. [CrossRef]
* [6] [PERSON]; [PERSON]; [PERSON]; [PERSON] Predicting spatial and temporal patterns of soil temperature based on topography, surface cover and air temperature. _For. Ecol. Manag._**2000**, _136_, 173-184. [CrossRef]
* [7] [PERSON]; [PERSON] Estimation of soil moisture from optical and thermal remote sensing: A review. _Sensors_**2016**, _16_, 1308. [CrossRef]
* [8] [PERSON]; [PERSON] Spatiotemporal variability of soil temperature and moisture across two contrasting timberline ecotones in the Sergyemia Mountains, Southeast Tibet. _Arct. Antract. Alp. Res._**2011**, _43_, 229-238. [CrossRef]
* [9] [PERSON]; [PERSON]; [PERSON] A new embedded estimation model for soil temperature prediction. _Sci. Program._**2021**, _2021_, 5881018. [CrossRef]
* [10] [PERSON]; [PERSON]; [PERSON]; [PERSON] Developing novel hybrid models for estimation of daily soil temperature at various depths. _Soil Village Res._**2020**, _197_, 104513. [CrossRef]
* [11] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Novel machine learning framework for high-resolution sorghum biomass estimation using multi-temporal UAV imagery. _Int. J. Environ. Sci. Technol._**2025**, 1-16. [CrossRef]
* [12] [PERSON]; [PERSON] [PERSON]; [PERSON] Novel Machine Learning Approaches for Accurate Leaf Area Estimation in Apples. _Appl. Fruit Sci._**2025**, _67_, 68. [CrossRef]
* [13] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Comparative analysis of machine learning techniques for estimating groundwater deuterium and oxygen-18 isotopes. _Stoch. Environ. Res. Risk Assess._**2022**, _36_, 4271-4285. [CrossRef]
* [14] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models. _Phys. Chem. Earth Parts A/B/C_**2024**, _133_, 103537. [CrossRef]
* [15] [PERSON]; [PERSON] Modeling daily soil temperature using data-driven models and spatial distribution. _Theor. Appl. Climatol._**2014**, _118_, 465-479. [CrossRef]* _[PERSON] et al. (2010)_ [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A model comparison between predicted soil temperatures using ANFIS model and regression methods in three different climates. _Water Soil_**2010**, _24_, 274-285. * _[PERSON] et al. (1997)_ [PERSON]; [PERSON]; [PERSON] An artificial neural network to estimate soil temperature. _Can. J. Soil Sci._**1997**, _77_, 421-429. [CrossRef]
* [PERSON] (2013) [PERSON]; [PERSON] Artificial neural network approach for modeling of soil temperature: A case study for Bathalagoda area. _Sri Lankan J. Appl. Stat._**2013**, _13_, 39-59. [CrossRef]
* _[PERSON] et al. (2011)_ [PERSON] [PERSON]; [PERSON]; [PERSON] Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. _Meteorol. Atmos. Phys._**2011**, _110_, 135-142. [CrossRef]
* [PERSON] (2010) [PERSON] Prediction of soil temperature using regression and artificial neural network models. _Meteorol. Atmos. Phys._**2010**, _110_, 59-70.