Exploring vegetation-driven microclimatic effects on soil temperature dynamics in tropical climates through machine learning and explainable AI

Soil temperature (ST) is a crucial parameter in tropical environments, influencing microbial activity, nutrient cycling, and root growth. Accurate and cost-effective prediction of ST is essential for understanding soil health and supporting resilient ecosystems. This study investigates ST dynamics across four different urban tropical microclimates within a 150-square-meter area over a four-month period, utilizing a dataset comprising 5,856 observations collected in a tropical setting. Advanced machine learning modelling, including Random Forest Regressor (RFR), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and Multilayer Perceptron (MLP), coupled with Explainable Artificial Intelligence (XAI) was employed. Results reveal that the interplay of solar azimuth and vegetation cover governs ST variations. The XGBoost outperformed all other machine learning models, exhibiting the most accurate predictions and resulting root mean square error (RMSE) values of 0.298 ± 0.008ºC for modelling ST at 10 cm depth, 0.117 ± 0.006ºC at 30 cm and 0.064 ± 0.002ºC at 50 cm. XAI analysis highlighted air temperature as the dominant predictor of ST at 10 cm, while deeper layers were influenced by temperature of the overlaying soil layer, followed by solar radiation and soil water content. These findings emphasize the potential of integrating machine learning (ML) and XAI for explicit and reliable ST prediction and advancing plant growth.

土壤温度（Soil Temperature，ST）是热带环境中的关键参数，可影响微生物活动、养分循环与根系生长。精准且兼具成本效益的土壤温度预测，对于认识土壤健康状况、支撑韧性生态系统至关重要。本研究针对150平方米区域内的四种不同热带城市微气候，开展了为期四个月的土壤温度动态监测研究，所用数据集包含热带环境下采集的5856条观测记录。研究采用了包括随机森林回归器（Random Forest Regressor，RFR）、极限梯度提升（Extreme Gradient Boosting，XGBoost）、支持向量回归（Support Vector Regression，SVR）以及多层感知器（Multilayer Perceptron，MLP）在内的先进机器学习建模方法，并结合可解释人工智能（Explainable Artificial Intelligence，XAI）技术。结果表明，太阳方位角与植被覆盖的相互作用主导了土壤温度的变化。极限梯度提升模型的表现优于其余所有机器学习模型，预测精度最高：在10厘米深度的土壤温度建模中，其均方根误差（Root Mean Square Error，RMSE）为0.298±0.008℃；30厘米深度为0.117±0.006℃；50厘米深度则为0.064±0.002℃。可解释人工智能分析显示，10厘米深度的土壤温度主要受气温主导，而更深土层的温度则受上覆土层温度影响最大，其次为太阳辐射与土壤含水量。本研究结果证实，将机器学习（Machine Learning，ML）与可解释人工智能相结合，可实现精准可靠的土壤温度预测，助力植物生长研究的发展。