Soil moisture content plays a crucial role in precision agriculture and geological hazard monitoring, driving the need for stable, reliable, and high-precision sensors. Capacitive soil moisture sensors based on Frequency Domain Reflectometry (FDR) are widely adopted due to their favorable measurement performance, yet their accuracy is highly susceptible to environmental interferences such as temperature, salinity (electrical conductivity), and soil type. This paper systematically reviews current calibration strategies addressing these three factors, classifying them into hardware-based compensation and software-based calibration (including conventional mathematical and machine learning models). Furthermore, it critically analyzes the trade-offs of these approaches in terms of robustness, scalability, and field applicability. To break through current technical limitations, this review argues that future research must prioritize the physical decoupling of multi-parameter interferences under extreme conditions. Additionally, to overcome the generalization crisis of current data-driven models, adaptive strategies utilizing techniques like transfer learning are essential. Finally, implementing Edge-AI on resource-constrained hardware is crucial for achieving calibration-free or real-time online calibration strategies, ensuring long-term measurement accuracy.

A novel capacitive interdigital electrode (IDE) sensor for the in-situ measurement of soil moisture is presented. Two planar electrode configurations, spiral and embracing, were designed and evaluated through modeling, simulation, fabrication, and experimental validation. Compared with conventional circular and square electrodes, the proposed structures exhibited higher sensitivity and greater electric field penetration, with the spiral configuration offering the advantage of easier fabrication. The experimental results demonstrated that the calibrated spiral IDE sensor achieved a coefficient of determination (R²) of 0.9976 and a mean squared error (MSE) of 0.859, indicating good stability and repeatability over the tested period. Furthermore, comparison with a commercial moisture sensor showed that the proposed sensor reached a higher R² value of 0.9995, exhibiting closer agreement with gravimetric measurements. These findings confirm that the developed sensor holds strong potential for in situ monitoring of soil moisture and can provide valuable technical support for landslide monitoring and prevention.