## 1 Introduction  
Soil moisture content (SMC) plays a crucial role in hydrological and ecological processes that affect energy, water, and carbon cycles, including evaporation [1], transpiration, diversity [2], and rainfall-runoff in various ecosystems [3]. Understanding the spatiotemporal patterns of SMC is essential for water resources management, mitigating the effects of agricultural drought [4], addressing water scarcity, and improving crop production [5, 6]. Moreover, the collection of high-quality soil moisture data is significant for scientific research purposes, such as weather forecasting, flood prediction, drought monitoring [7, 8], precision agriculture [9], and irrigation management [10]. As the demand for high-precision soil moisture data increases, there is a growing need to develop reliable in situ soil moisture sensor networks that balance cost, complexity, and low power consumption for effective soil moisture monitoring [11]. The advancement of sensing technologies, including Internet of Things (IoT), has enabled real-time data exchange between connected devices using a wireless sensor network, allowing for constant monitoring of various environmental parameters in real time [12]. Recent studies have demonstrated the applicability of IoT-based approaches in agriculture for environmental monitoring [13], controlling greenhouse gas emissions [14], irrigation optimization, weather monitoring, product traceability, and intelligent equipment management [15]. Notably, IoT sensors address the limitations of traditional soil moisture measurement techniques, which are often time consuming and lack real-time monitoring capabilities, by providing real-time, low-maintenance, and scalable solutions [16]. In addition to in situ measurements, remote sensing approaches utilizing satellite images have been widely used for SMC monitoring and predicting model development [17]. The integration of meteorological data and soil properties monitoring in SMC prediction provides a better understanding of its variability and the types of interactions between SMC and these parameters. Accurate SMC prediction is important for enhancing irrigation management and optimizing crop production [18]. Traditional statistical methods often struggle to capture the complex, nonlinear, and multivariate interactions among soil, environmental, and meteorological factors that influence SMC [19]. In contrast, machine learning (ML) algorithms can handle these complexities and offer high potential for accurate SMC prediction [20,21]. ML models have the capability to utilize the high-resolution dataset provided by IoT-based sensors to enhance prediction accuracy of SMC. Among these, the Gradient Boosting Regressor (GBR) algorithm has demonstrated strong performance in SMC prediction due to its ability to combine multiple weak learners (typically decision trees) to iteratively minimize loss functions, thereby improving both accuracy and interpretability [22]. For instance, [PERSON] et al. [23] utilized two years of data to train a GBR model and artificial neural network (ANN) to predict SMC in top surface layers. The results showed that the GBR model performed effectively in predicting SMC with a 0.01 learning rate, 5 max depth, and 350 estimators, achieving a mean square error (MSE) of 0.027 and a 20% difference in maximum between measured and predicted values. Similarly, [PERSON] et al. [24] utilized XGBoost to predict soil moisture, highlighting its high performance in comparison with other models, with a correlation coefficient of 0.69 and an accuracy of 88%. The primary predictors were air relative humidity, maximum air temperature, and total precipitation. Consideration of soil characteristics, particularly during the 2022 drought, led to improved prediction accuracy. However, existing research had a lack of soil texture-specific and depth-specific modeling, which considers significant factors regarding their strong impact on SMC.