Estimation Of Water Content Research Articles

Estimation of soil water and organic matter content in medium and low yield fields of Ningxia Yellow River Irrigation area based on hyperspectral information.

Accurately obtaining soil water and organic matter content is of great significance for improving soil qua-lity in croplands with medium to low yield. We explored the estimation effect of fractional order differentiation (FOD) combined with different spectral indices on soil water and organic matter content in medium and low yield croplands of Ningxia Yellow River Irrigation Area. After root mean square transformation of field measured hyperspectral reflectance, we used 0-2 FOD (with a step length of 0.25) to construct difference index (DI), ratio index (RI), product index (PI), sum index (SI), generalized difference index (GDI), and nitrogen planar domain index (NPDI) and to select the optimal spectral index based on the correlation coefficients between six spectral indices with soil water and organic matter contents. We constructed a model for estimating soil water and organic matter content based on partial least squares regression (PLSR) and support vector machine (SVM). The results showed that the correlation between soil water and organic matter content and spectral information was effectively improved after FOD transformation compared with the original spectrum, with maximum increases of 0.1785 and 0.1713, respectively. The soil water content sensitive bands were mainly in the range of 400-630 and 1350-1940 nm, while the sensitive bands of organic matter content were mainly at 460-850, 1530-1910, and 2060-2310 nm. The accuracy of SVM model was significantly higher than that of PLSR, and the soil water content estimation model based on 1.75-order NPDI-SVM reached the highest precision, with a validation determination coefficient (Rp2) of 0.970, root mean square error (RMSE) of 1.615, and relative percent deviation (RPD) of 4.211. The organic matter content estimation model based on 0.5 order DI-SVM had the best performance, with Rp2, RMSE and RPD of 0.983, 0.701 and 5.307, respectively. Our results could provide data and technological support for soil water and nutrient monitoring, quality improvement, and graphics creating in similar area with medium to low yield fields.

Anomalous Sound Velocities of Talc at High Pressure and Implications for Estimating Water Content in Mantle Wedge

AbstractTalc is widely distributed in metamorphic and hydrothermally altered rocks, and its velocity is useful for constraining the velocity of mantle wedge. In this study, we used ultrasonic interferometry to study the elastic properties of talc polycrystalline samples at high pressure (up to 5.6 GPa). VP increases monotonically with increasing pressure, and VS increases with pressure up to ∼3.2 GPa, but negative pressure dependence occurs above ∼3.2 GPa. Talc has a VP/VS ratio of 1.63–2.14 and Poisson's ratio of 0.20–0.36 at the pressure of 1.2–5.6 GPa, which can be used to explain some anomalous seismic observations in subduction zone. Combining the elastic properties of talc with those of olivine, orthopyroxene, and antigorite, the wave velocity of mantle wedge was calculated as a function of water content. The results suggest that lower water content is needed to match the observed values of seismic tomography when talc is present, compared with the antigorite scenario.

Towards practical semi-empirical models for the estimation of leaf and canopy water contents from hyperspectral reflectance

The knowledge of crop water status provides valuable information for agricultural water management. Most remote sensing methods for estimating water contents are based on statistical models and spectral features in the shortwave infrared region where water absorption dominates. However, the effects of leaf structure (Ns) and dry matter (expressed as leaf mass per area, LMA) on the spectral features in this region are not fully considered when estimating leaf water content (LWC) and canopy water content (CWC). This issue would lead to the data-specific empirical model, which lacks generality and is not transferable among different conditions. To fill this gap, this study for the first time evaluated the Ns and LMA effects on the spectral features, including the widely used normalized difference (ND)-type vegetation index (VI) and wavelet feature (WF) from wavelet transform. The leaf and canopy radiative transfer model simulations showed that the ND-type VI was sensitive to Ns at both leaf and canopy levels, whereas WF was sensitive to LMA at the canopy level. For example, the relationships between WF and CWC varied clearly with the level of LMA, especially for high values of CWC. Next, the optimal water index (OWI = ND1200,1500) and optimal WF (OWF = WF1600,8) were determined by minimizing their correlations with the Ns and LMA based on the leaf and canopy simulations. Unlike the existing spectral features applicable to only leaf or canopy level alone, OWI and OWF showed more consistent performance on estimating LWC and CWC. OWI- and OWF-based semi-empirical models were calibrated using model simulations and then evaluated using the independent measured datasets. The semi-empirical model based on OWI exhibited moderate estimation accuracy of LWC (R2 = 0.62, RMSE = 24.33 g/m2) and CWC (R2 = 0.69, RMSE = 0.15 kg/m2), outperforming previous ND-type VIs. In comparison, the best estimation accuracies for LWC (R2 = 0.80, RMSE = 17.47 g/m2) and CWC (R2 = 0.85, RMSE = 0.11 kg/m2) were obtained by the OWF-based semi-empirical model when the prior information of LMA was incorporated at the canopy level. These findings have great potential for mapping crop water status over large regions with universal semi-empirical models and hyperspectral satellite imagery.

A deep neural network with electronic nose for water stress prediction in Khasi Mandarin Orange plants

Water stress is a significant environmental factor that hampers plant productivity and leads to various physiological and biological changes in plants. These include modifications in stomatal conductance and distribution, alteration of leaf water potential & turgor loss, altered chlorophyll content, and reduced cell expansion and growth. Additionally, water stress induces changes in the emission of volatile organic compounds across different parts of the plants. This study presents the development of an electronic nose (E-nose) system integrated with a deep neural network (DNN) to detect the presence and levels of water stress induced in Khasi Mandarin Orange plants. The proposed approach offers an alternative to conventional analytical methods that demand expensive and complex laboratory facilities. The investigation employs the leaf relative water content (RWC) estimation, a conventional technique, to evaluate water stress induction in the leaves of 20 plants collected over a span of 9 days after stopping irrigation. Supervised pattern recognition algorithms are trained using the results of RWC measurement, categorising leaves into non-stressed or one of four stress levels based on their water content. The dataset used for training and optimising the DNN model consists of 27 940 samples. The performance of the DNN model is compared to traditional machine learning methods, including linear and radial basis function support vector machines, k-nearest neighbours, decision tree, and random forest. From the results, it is seen that the optimised DNN model achieves the highest accuracy of 97.59% in comparison to other methods. Furthermore, the model is validated on an unseen dataset, exhibiting an accuracy of 97.32%. The proposed model holds the potential to enhance agricultural practices by enabling the detection and classification of water stress in crops, thereby aiding in water management improvements and increased productivity.

Estimation of plant water content in cut chrysanthemum using leaf-based hyperspectral reflectance

Water plays an important role in the growth process of cut chrysanthemum (Chrysanthemum morifolium Ramat.). Accurate monitoring of plant water content (PWC) is a vital guarantee for the high-quality production of cut chrysanthemums. Hyperspectral remote sensing technology has been widely used in precision agriculture due to its rapid, convenient, and nondestructive advantages, but relatively little is known about its use for predicting the PWC of cut chrysanthemums. Therefore, this study aimed to evaluate the performance of hyperspectral reflectance from different leaf layers for estimating the PWC of cut chrysanthemums. A hyperspectral spectroradiometer was used to collect hyperspectral reflectance data (350-2500 nm) from three leaf layers at different critical growth periods. Immediately following the spectra measurements, cut chrysanthemum canopies were sampled for PWC. Spectral index and partial least square regression (PLSR) were then used to establish PWC estimation models of cut chrysanthemums. The results showed that the first leaf layer (LL1) was the optimal leaf layer for estimating the PWC of cut chrysanthemum. The new proposed two-band spectral index, NDVI-LL1 (R2280, R1885), exhibited moderate prediction capability for PWC cut chrysanthemum (R2=0.54658, RMSE=0.02352). Moreover, compared with the spectral index model, the model using the PLSR-LL1 showed the best performance for estimating the cut chrysanthemum PWC (R2=0.93510, RMSE=0.00887). Our results can provide technical support for spectral monitoring of PWC and precise irrigation in cut chrysanthemums.

Utilizing Redox Transformations of Quinones to Modulate Weak Donor and Acceptor Bonding Interactions with H2o and CO2 in Organic Solvents

Redox chemical transformations can be used to modulate supramolecular interactions between reduced quinones and acceptor molecules, with the application of an electric potential enabling the interactions to be switched on and off. The supramolecular interactions targeted include weak solution phase processes involving H-bonding with water molecules, and interactions between dissolved molecular gases such as CO2 and solution phase electroactive species. The advantage of this approach is that it enables the supramolecular complexing abilities of one molecule to be improved and tuned simply by changing its redox state. For instance, in one redox state the compound may undergo no supramolecular interactions, but once it is oxidized or reduced it undergoes very strong interactions. This enables the development of potential sensing devices as well as the ability to gain fundamental information regarding interactions that are difficult to quantify, including H-bonding.In aprotic organic solvents, neutral quinones (Q) undergo an EE reduction mechanism (E signifies an electron transfer) to first give a radical anion or semiquinone (Q•–) at potential E Q1, followed by an aromatic dianion (Q2–) at a more negative potential, E Q2. When water is progressively added to the solvent, the second reduction wave (E Q2) progressively moves towards more positive potentials until it eventually merges with the first reduction wave (E Q1), so the two electrons transfer at close to one potential. The reason for the shift in potential is because the quinone anions, especially the dianions, undergo very strong H-bonding interactions through the highly electronegative oxygen atoms in the reduced quinones with the hydrogen atoms from the water molecules. In order to be able to quantitatively study the reactions with water, it is essential that the exact water content of the solvent is known, and the water content can be precisely controlled. By performing careful calibration experiments using Karl Fischer coulometric titrations, it was possible to establish working curves of water content versus E Q2 – E Q1 (= ΔE) that enabled the accurate calculation of the water content of an organic solvent simply by recording one voltammogram in the presence of a quinone. Therefore, using this procedure, a voltammetric method for quickly estimating water contents of organic solvents down to the low ppm levels was established.In addition to H-bonding interactions, recent experiments have demonstrated that reduced quinones undergo interactions with CO2 and this has been proposed as a method for removing CO2 from industrial gas streams.The technique involves purging the gas through an ionic liquid containing a quinone and applying a reducing potential to produce the quinone dianion which interacts strongly with CO2 in a similar supramolecular mechanism as for H2O [Figure 1(a)]. Once the CO2 is “trapped”, the [quinone–(CO2)2]2– molecules can be separated, and an oxidizing potential applied which results in the [quinone–(CO2)2]2– complex being oxidized back to the starting material and the CO2 being released.Figure 1(b) shows the results from cyclic voltammetry and electrolysis experiments for the reduction of a quinone in the presence and absence of CO2. Under an argon atmosphere, two one-electron reduction processes are evident due to the reduction to the anion radical then at more negative potentials the dianion. However, when the solution is purged with CO2 gas, the voltametric waves immediately merge into one two-electron process due to the formation of the [quinone–(CO2)2]2– supramolecular complex This species is stable under electrolysis conditions and can exist in the bulk solution for long periods (hours). The mechanism can be varied by purging the solution containing the [quinone–(CO2)2]2– complex with an inert gas such as Ar or N2, resulting in two major pathways.In one pathway, the [quinone–(CO2)2]2– complex can be made to release the CO2 •– and leave the neutral quinone, and in the other pathway the charge remains on the quinone and the neutral CO2 is released as shown in Figure 1(c). Figure 1. (a) Reduction mechanism of 1,4-napthoquinone in the presence of CO2. The counterion for the anionic species is the electrolyte cation, n-Bu4N+. (b) Cyclic voltammograms for 1,4-napthoquinone in the presence and absence of CO2. (c) Proposed pathways for the reaction of NQ(CO2)2 2 – with excess argon gas. Figure 1

Estimation of canopy water content for wheat through combining radiative transfer model and machine learning

Canopy water content (CWC) is an important indicator of crop growth. Currently, the hybrid approach, which combines the physical model and machine learning (ML) method, is commonly used for inversing CWC at the regional scale via optical imagery. Although this approach has good inversion accuracy, it suffers from ill-posed issues from the physical model due to the difference between actual and simulated scenes. Meanwhile, the ability of existing ML methods to resolve this difference remains unclear. To fill the above study gaps, we added different degrees of Gaussian noise into the simulated dataset to reduce the differences in spectral reflectance between that simulated by the PROSAIL-5B model and that measured from optimal imagery. Furthermore, this study also compared the performance of different ML approaches (neural network, NN; Gauss process regression, GPR; and kernel ridge regression, KRR) in the retrieval of wheat (Triticum aestivum L.) CWC based on PROSAIL-simulated datasets. Additionally, we also compared the inversion results for the CWC generated by the SNAP Toolbox with those estimated by the hybrid approach. The results showed that the aforementioned hybrid approaches performed well in the retrieval of wheat CWC when Gaussian noise with a mean of zero and a standard deviation of 0.06 (η: 0, 0.06) was added to the simulated dataset. The hybrid model based on GPR had the highest accuracy of estimation for the CWC (RMSE = 0.0096 g/cm2, RRMSE = 22.2%), when compared with NN or KRR (NN: RMSE = 0.0105 g/cm2, RRMSE = 26.9%; KRR: RMSE = 0.0099 g/cm2, RRMSE = 24.3%). It also outperformed the existing CWC-retrieval algorithms among the SNAP Toolbox (RMSE = 0.027 g/cm2, RRMSE=44.1%). Our results demonstrate that the hybrid approach in combination with GPR and the physical model can accurately retrieve the CWC of crops, thus confirming that the approach is suitable for estimating the CWC.

Estimation of soil water content using electromagnetic induction sensors under different land uses

The complex nature of podzolic soils makes investigating their subsurface challenging. Near-surface geophysical techniques, like electromagnetic induction (EMI), offer significant assistance in studying podzolic soils. Multi-coil (MC-EMI) and multi-frequency (MF-EMI) sensors were selected to maximize soil water content (SWC) prediction in this study. The objectives were to (i) compare apparent electrical conductivity (ECa) measurements from the MC and MF-EMI sensors under different land use conditions, (ii) investigate the spatial variation of ECa, SWC, texture, soil organic matter (SOM), and bulk density (BD) under different land use conditions, and (iii) use statistical and geostatistical analysis to evaluate the effectiveness of ECa measurements in characterizing SWC under different land use conditions, considering the texture, SOM, and BD contents. The study found that MC-EMI had statistically significant relations (p-value < 0.05) with SWC relative to the MF-EMI. Multiple linear regression (MLR) models were also shown to be more effective in representing SWC variations (higher coefficient of determination and lower root mean square error) than simple linear regression models. MC-EMI sensor provided better SWC predictions compared to the MF-EMI sensor, possibly due to larger sampling depths differences between time domain reflectometry measured SWC (SWCTDR) and MF-EMI sensor than those between SWCTDR and MC-EMI sensor. Lastly, cokriging of measured SWC was found to offer more accurate maps than cokriging of predicted SWC obtained from MLR across different land use conditions. The study has shown that EMI may not be highly effective for shallow depths, and ECa can be affected by various soil properties, making it difficult to extrapolate other parameters. However, EMI still shows promise as a reliable method for predicting SWC in boreal podzolic soils. Research into EMI’s usefulness for this purpose has yielded promising results, as indicated in this study. Further investigation is needed to fully harness the potential of this promising technique.

Three years of soil moisture observations by a dense cosmic-ray neutron sensing cluster at an agricultural research site in north-east Germany

Abstract. Cosmic-ray neutron sensing (CRNS) allows for the estimation of root-zone soil water content (SWC) at the scale of several hectares. In this paper, we present the data recorded by a dense CRNS network operated from 2019 to 2022 at an agricultural research site in Marquardt, Germany – the first multi-year CRNS cluster. Consisting, at its core, of eight permanently installed CRNS sensors, the cluster was supplemented by a wealth of complementary measurements: data from seven additional temporary CRNS sensors, partly co-located with the permanent ones; 27 SWC profiles (mostly permanent); two groundwater observation wells; meteorological records; and Global Navigation Satellite System reflectometry (GNSS-R). Complementary to these continuous measurements, numerous campaign-based activities provided data by mobile CRNS roving, hyperspectral imagery via UASs, intensive manual sampling of soil properties (SWC, bulk density, organic matter, texture, soil hydraulic properties), and observations of biomass and snow (cover, depth, and density). The unique temporal coverage of 3 years entails a broad spectrum of hydro-meteorological conditions, including exceptional drought periods and extreme rainfall but also episodes of snow coverage, as well as a dedicated irrigation experiment. Apart from serving to advance CRNS-related retrieval methods, this data set is expected to be useful for various disciplines, for example, soil and groundwater hydrology, agriculture, or remote sensing. Hence, we show exemplary features of the data set in order to highlight the potential for such subsequent studies. The data are available at doi.org/10.23728/b2share.551095325d74431881185fba1eb09c95 (Heistermann et al., 2022b).

A small amount of water reduces the friction of the preferentially oriented montmorillonite gouge

The presence of smectite may control the updip seismogenic boundary of a subducting plate. The smectite near the boundary can be dehydrated and highly preferentially oriented in the smectite-to-illite transition zone. Herein, the frictional properties of preferentially oriented (PO) montmorillonite gouge were examined under dry conditions. Highly PO montmorillonites were prepared as self-supporting sheets, which were dried at 70–200 °C for 12 h before conducting shear experiments. The double direct-shear tests on these PO sheets and nominally randomly oriented (NRO) montmorillonite were conducted at room temperature and dry conditions under applied normal stress from 5 to 40 MPa. The friction coefficients of the NRO gouges (0.42–0.45) were not affected by drying temperature, while those of the PO sheets increased with increasing drying temperature from 0.32 to 0.52 at normal stress of 5 MPa. The estimated water contents for both samples depended on the drying temperature, but it was significantly lower (<1 mass%) than the upper limit of the water content of 4.5 mass% for dry friction as estimated from previous studies. A small amount of water (<1 mass%) might become vapor by frictional heating in the low porosity, low permeability PO smectite gouge and increased pore pressure resulting in the decrease of the effective normal stress and shear stress. Although there is much to consider under the natural conditions of smectite-to-illite transition, it was found that preferentially oriented smectite showed a low friction coefficient, even in a dehydrated state.

Surface exposure constraints on the mantle water budget

Abstract Mantle water content estimates range from 0.5 to 15 oceans of water. Its evolution is even more unclear. Rapid degassing during mantle solidification likely released much of the water to the surface, initially flooding Earth. However, evidence for subaerial land from at least 3.5 Ga means that much of this water must have been rapidly cycled back into the mantle. Here, we used a parameterized convection model and hypsometric curve to assess how much water could have been taken into the mantle and still satisfy evidence for subaerial land. Even if only the highest peaks were exposed, the initial ocean must have been less than 1.5 current oceans to explain subaerial exposure throughout most of Earth history. Today, this implies any water in the mantle &amp;gt;0.5 oceans must be primordial and has been isolated from the convecting mantle for most of Earth’s history.

On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

Оценка состава водных масс восточной части Баренцева моря с использованием современных данных о параметрах δ18O и δ2Н

This paper presents the results of measurements of δ18O and δ2H stable isotope values in the waters of the Barents Sea, which were carried out in March–April 2021. Based on the data presented, the ratio of base water content – atlantic (fa), river (fr) and melt water (fi), as well as the volume of water withdrawn for ice formation in the studied water area was assessed. The comparative estimation of base water content, calculated on values “δ18O–salinity” and “δ2H–salinity” is presented and obtained values do not exceed limits of mean value ±sd. However, average values of Atlantic and ice water content calculated using δ2H parameter are higher than those using δ18O, and river water content is lower. The use of δ18O and δ2H parameters in oceanographic studies in the calculation of balance characteristics is preferable to temperature and salinity because of their greater conservativity.

Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data.

This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R2 (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.

Integrated use of georadar, electrical resistivity, and SPT for site characterization and water content estimative

Geophysical methods are potent tools for geotechnical site characterization in a nondestructive way. They improve the extrapolation of punctual data from direct survey methods, allowing a fast and cost-effective evaluation of large areas. Ground Penetrating Radar (GPR) and DC electrical resistivity (ER) are the most requested methods for geotechnical and geoenvironmental applications. Their use, however, is usually uncoupled, with no sharing of information from one method to another to improve data interpretation. This case study illustrates the development of protocols and scripts in R© programming language for ER and GPR data analysis with Standard Penetration Tests (SPT) data to produce more accurate information on subsurface conditions concerning lithology, water content, and groundwater table (GWT) position. The SPT data were used to associate resistivity ranges with different soil lithologies and GPR pulse velocities for estimating the soil water content. Estimated water content values aided in interpreting ER data and locating the groundwater table. The contacts between layers in the radargrams allowed the refinement of the ER model, rendering 3D volumes for each soil layer in situ.

Velocity analysis of a lateral wave

In practice, the reflected EM signal cannot be clearly observed in GPR data due to the high water content and other reasons. However, the antenna coupling signal through the ground interface is dominated in all the GPR measurements. This direct coupling signal that travels through the ground interface is called a lateral wave. The properties of the lateral wave directly depend on the subsurface properties, especially electrical parameters. We have numerically analyzed a lateral wave and its velocity. Subsequently, the relationship between lateral wave and dielectric permittivity was determined by polynomial regression. Analytically, it is challenging to analyze a lateral wave due to the parameters that can influence wave propagation. Antenna characteristics, surface roughness, etc need to be considered. Numerically, we designed a GPR system with a subsurface layer and observed a lateral wave. This numerical analysis can give a chance to use a lateral wave for near-surface soil water content. This analysis gives a more precise estimation of surface water content. Moreover, we analyze the antenna height effect that influences radar signals. We numerically observed that the GPR signal is highly affected by antenna height. The antenna height effect depended on the wavelength of the applied electromagnetic wave. By adjusting the antenna height, the unobservable GPR signal can be clearly detected.

Estimation of the Soil Water Content Using the Early Time Signal of Ground-Penetrating Radar in Heterogeneous Soil

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content.

The estimated water content of the Abaga basalts (Inner Mongolia, China) and compositions of the recycled materials in the source: Implication for magma genesis

The Abaga volcanic rocks are intraplate basalts that formed in the late Cenozoic period and are located in eastern central Inner Mongolia. Previous studies have discussed the genesis of the studied basalts. However, limited information about subducted recycled materials (the water content, sediments, and melts of the subducted plate), which control the generation of basalts, in the studied basalts has been reported. In this study, the major element abundances and crystallization temperatures and pressures of clinopyroxenes have been reported to estimate the water content of the studied magma, which has been applied to the Wulanhada volcanic field. The calculated result displays that the water content in the Abaga melts are between 0.5% and 1.7% (standard error estimate: 0.45%; average value: 1.3%). These estimated values are in range of the Aershan–Chaihe basalts (0.19–2.7%), while it is higher than those of the mid-ocean ridge basalts (MORBs: 0.16–0.47%). The high-water content of the studied basalts may have a close relationship with the mantle transition zone, which has also been reported to contain high concentrations of H2O. Subduction related sediments and slab may contribute to high-water content of magma. In addition, the clinopyroxene-melt thermobarometer shows that clinopyroxenes generally crystallized at 1113–1246 °C at the crustal level (3.8–12.4 kbar), while olivines formed at 1243–1392 °C (assuming pressure is 13 kbar, H2O = 1.3%). We also studied whole-rock major and trace elements and Sr–Nd–Hf–O isotope compositions of the Abaga basalts. High (Ta/U)N and (Nb/Th)N values that are higher than 1, NdHf isotopic values that plot between sediments and oceanic crust, and low δ18O values (4.51–5.59‰) of the alkali basalts illustrate that a subducted oceanic slab may have been added to the studied mantle source. In addition, we decipher that sediments may also exist in the source with Ba/Th, Nb/La, Ce/Pb and Ba/La ratios and NdHf isotopic values locating between sediments and altered oceanic crust. We use sediments, depleted MORB mantle (DMM), and recycled oceanic crust to simulate mixing features among these endmembers, which demonstrates that 80% of depleted MORB mantle, 15% of altered oceanic crust, and 5% of global subducting sediment (GLOSS), with 4–5% partial melting of peridotite or pyroxenite is a proper result with which to interpret the genesis of the studied alkali basalts.

Broadband Measurements of Soil Complex Permittivity.

Agriculture is a major consumer of freshwater and is often associated with low water productivity. To prevent drought, farmers tend to over-irrigate, putting a strain on the ever-depleting groundwater resources. To improve modern agricultural techniques and conserve water, quick and accurate estimates of soil water content (SWC) should be made, and irrigation timed correctly in order to optimize crop yield and water use. In this study, soil samples common to the Maltese Islands having different clay, sand, and silt contents were, primarily, investigated to: (a) deduce whether the dielectric constant can be considered as a viable indicator of the SWC for the soils of Malta; (b) determine how soil compaction affects the dielectric constant measurements; and (c) to create calibration curves to directly relate the dielectric constant and the SWC for two different soil types of low and high density. The measurements, which were carried out in the X-band, were facilitated by an experimental setup comprising a two-port Vector Network Analyzer (VNA) connected to a rectangular waveguide system. From data analysis, it was found that for each soil investigated, the dielectric constant increases notably with an increase in both density and SWC. Our findings are expected to aid in future numerical analysis and simulations aimed at developing low-cost, minimally invasive Microwave (MW) systems for localized SWC sensing, and hence, in agricultural water conservation. However, it should be noted that a statistically significant relationship between soil texture and the dielectric constant could not be determined at this stage.

State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network

Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC.