Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Abstract

Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.

Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis

“…9). Overall, Ta showed the highest correlation with Ts, confirming findings by Kisi et al (2015) and Nanda et al (2020).…”

“…values) of all models at all soil depths. Nanda et al (2020) also found that SVM had the lowest precision of all machine learning models tested in hourly and half-hourly Ts prediction. The main reason for the low accuracy of SVM may be non-linearity between several analyzed parameters.…”

A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)

“…In the regression studies, DT (Sattari et al 2020;Sanikhani et al 2018), Support Vector Regression (SVR) (Li et al 2020a;Li et al 2020b;Shamshirband et al 2020;Delbari et al 2019;Mehdizadeh et al 2018;Xing et al 2018), RF (Alizamir et al 2020b;Tsai et al 2020;Feng et al 2019), NN (Abimbola et al 2021;Bayatvarkeshi et al 2021;Wang et al 2021;Hao et al 2020;Penghui et al 2020;Citakoglu 2017;Abyaneh et al 2016;Kisi et al 2015), ELM (Alizamir et al 2020a) algorithms have been preferred for predicting soil temperatures. In addition, some of the time-series studies have also applied NN (Li et al 2020c;Bonakdari et al 2019), ELM (Zeynoddin et al 2020;Mehdizadeh et al 2020), SVR (Nanda et al 2020) algorithms for the prediction performance comparison.…”

“…(Sanikhani et al 2018) applied extreme learning machine (ELM), neural network (NN), and M5 Model Tree (M5 Tree) models on the meteorological data obtained from two stations in Turkey for predicting soil temperatures at 5, 50, and 100 cm depths. The studies also estimate the features that affect the soil temperature prediction (Feng et al 2019;Nanda et al 2020). (Nanda et al 2020) discovered that while rainfall data does not affect the prediction performance, the soil moisture parameter improves the accuracy of the prediction problem.…”

“…The studies also estimate the features that affect the soil temperature prediction (Feng et al 2019;Nanda et al 2020). (Nanda et al 2020) discovered that while rainfall data does not affect the prediction performance, the soil moisture parameter improves the accuracy of the prediction problem.…”