Forest duff (fermentation and humus) water content is an important parameter for fire risk prediction and water resource management. However, accurate determination of forest duff water content is difficult due to its loose structure. This study evaluates the feasibility of a standing wave ratio (SWR) sensor to accurately determine the forest duff water content. The performance of this sensor was tested on fermentation and humus with eight different compaction levels. Meanwhile, a commercialized time domain reflectometry (TDR) was employed for comparison. Calibration results showed that there were strong linear relationships between the volumetric water content (θ_V) and the SWR sensor readings (V_SWR) at different compaction classes for both fermentation and humus samples. The sensor readings of both SWR and TDR underestimated the forest duff water content at low compacted levels, proving that the compaction of forest duff could significantly affect the measurement accuracy of both sensors. Experimental data also showed that the accuracy of the SWR sensor was higher than that of TDR according to the root mean square error (RMSE). Furthermore, low cost is another important advantage of the SWR sensor in comparison with TDR. This low-cost SWR sensor performs well in loose materials and is feasible for evaluating the water content of forest duff. In addition, the results indicate that decomposition of the forest duff should be taken into account for continuous and long-term water content measurement.

The gradient method used to estimate soil CO₂ flux is distinctive because it can provide additional information about CO₂ production and consumption of soil profile. However, choosing an appropriate gas diffusion model with confidence with the gradient method is a big challenge. There is no universal optimal diffusion model but only the most suitable model in specific soils. This paper evaluates the applicability of five commonly used diffusion models in laboratory with changing soil properties and in a forest farm, respectively. When soil moisture, bulk density and fertility status were changed in the laboratory, the applicability of the five diffusion models was discussed. Moreover, this paper shows diurnal variation of soil CO₂ flux estimated by the gradient method under four different climatic conditions in the forest farm, and the applicability of the five models was also analyzed. Both laboratory and forest experimental results confirm that the estimating accuracy of the Moldrup model is the highest, followed by the Millington–Quirk model, while those of the Penman, Marshall and Penman–Millington–Quirk models are poor. Furthermore, the results indicate that soil CO₂ flux estimated by the gradient method is highly sensitive to the diffusion model and insensitive to the changes of soil properties. In general, the gradient method can be used as a practical, cost-effective tool to study soil respiration only when the appropriate diffusion model is first determined.

Soil matric potential is an important parameter for agricultural and environmental research and applications. In this study, we developed a novel sensor to determine fast and in-situ the soil matric potential. The probe of the soil matric potential sensor comprises a perforated coaxial stainless steel cylinder filled with a porous material (gypsum). With a pre-determined gypsum water retention curve, the probe can determine the gypsum matric potential through measuring its water content. The matric potential of soil surrounding the probe is inferred by the reading of the sensor after the soil reaches a hydraulic equilibrium with the gypsum. The sensor was calibrated by determining the gypsum water retention curve using a pressure plate method and tested in three soil samples with different textures. The results showed that the novel sensor can determine the water retention curves of the three soil samples from saturated to dry when combined with a soil water content sensor. The novel sensor can respond fast to the changes of the soil matric potential due to its small volume. Future research could explore the application for agriculture field crop irrigation.

In this study, we developed a novel dielectric tomography system for in-situ tracking three-dimensional (3D) soil water dynamics. The system was designed to control a single dielectric tube sensor that automatically lowered in a PVC tube array installed in-situ to determine the water content of a soil profile, which eliminated probe-to-probe uncertainties and labor costs. Two tests for evaluating the novel system were conducted (i) to analyze and correct the positional error of the probe due to out-of-step errors of stepper motors, and (ii) to track and visualize 3D soil water temporal variations in a soil tank with heterogenetic bulk densities and initial water contents under drip irrigation. The results show that the positioning correcting algorithm combined with starting point alignment can minimize the positioning error of the probe during the 3D tomography. The single drip emitter test illustrated spatial and temporal variations of soil water content due to heterogeneous soil properties in vertical and horizontal directions around the access tube array. Based on these data, 3D distributions of soil water dynamics were visualized. The developed tomography system has potential application to be extended to the local scale in a greenhouse or the large scale in an agricultural field. Future research should explore the performance for agricultural crop irrigation or for modeling and validating soil water flow or hydrological process under either steady state or non-steady state condition.

Near-surface soil hydraulic properties (SHPs) are fundamental for describing and predicting water and energy exchange processes, particularly at the soil–atmosphere interface, and regulating evapotranspiration, infiltration, and runoff in different ecosystems. In this study, a new method was proposed to estimate near-surface SHPs by combining sensor-based soil infiltrability measurements with inverse modeling using HYDRUS-2D. The infiltration rate (IR) was estimated by combining the linear source inflow method with image processing, and K_s was estimated from the near-surface steady-state IR (NSIRM). The SWRC parameters described by the van Genuchten model were estimated using the inverse modeling method of HYDRUS-2D for the fitting of sensor-measured infiltration data. Subsequently, the parameters of the van Genuchten model, including $\alpha$, $n$, and $l$, were inversely estimated. Three undisturbed soils, including two stand humus samples from cork oak (Quercus suber L.) and oleander (Pinus tabuliformis L.) stands and one sandy loam from a farmland, were sampled near the soil surface to validate the proposed method. The estimated K_s was evaluated by the constant head method (CHM). The estimated parameters of the SWRC were validated by those determined through the simultaneous measurement of the soil moisture content and water potential using sensor techniques. The results showed that the K_s estimated from the NSIRM for each soil sample were 23.40 ± 1.21, 23.86 ± 1.83, and 22.99 ± 2.26 mm h⁻¹, respectively. In comparison, the K_s determined by the CHM were 24.41 ± 1.53, 24.26 ± 0.37, and 23.81 ± 0.10 mm/h, respectively. The relative errors of the proposed method were 4.14%, 1.64%, and 3.42%, respectively. For the SWRC estimation, the normalized root mean square errors (NRMSEs) between the measurements and the estimates for each soil sample were 0.1724, 0.1454, and 0.0606, respectively. Based on this, the AWC was obtained, and K_u was deduced from the estimated K_s and SWRC parameters for each soil sample. In general, the proposed method successfully estimates near-surface SHPs, simplifies the measurement device, and provides a new perspective for the in situ determination of near-surface SHPs under field conditions in the near future.