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.

Reliable in situ soil moisture measurements provide essential information for irrigation scheduling and agricultural soil–water management. Among the available measurement techniques, capacitive soil moisture sensors based on frequency domain reflectometry (FDR) are widely used because of their high measurement accuracy and suitability for in situ monitoring. However, the resonant frequency of an FDR capacitive soil moisture sensor is affected simultaneously by temperature, soil electrical conductivity (EC), and water content conditions, and independent fixed coefficient corrections cannot adequately describe this coupled relationship. To address this problem, we developed a time division multiplexed three-parameter FDR sensor that uses a composite electrode to sequentially acquire a water content-related resonant frequency, a conductivity-sensitive voltage, and a temperature-sensitive voltage at the same sensing location. A joint three-parameter calibration model was then established to map the three outputs to the reference volumetric water content determined by oven drying. Comparison of five candidate calibration models showed that the backpropagation neural network (BPNN) provided the best overall performance on the development data. A paired 10-seed analysis showed no stable predictive advantage for WOA initialization over standard BPNN, so WOA was retained only as a secondary robustness analysis. After the WOA-BPNN calibration model was fixed and deployed on the embedded platform, repeat measurements under 72 temperature–salinity–water content conditions within the same prepared sandy loam system yielded an RMSE of 2.086 percentage points. Within the tested calibration domain, the coordinated three-parameter hardware and joint calibration model reduced water content errors associated with temperature and salinity variation. These repeat measurements were conducted in the same prepared sandy loam system and, therefore, do not demonstrate generalization across other soil textures, field sites, independently prepared sample batches, or seasons.