Estimation Of Water Content Research Articles

Innovative technology for estimating water content of water-holding construction in coal mine tunnels

Innovative technology for estimating water content of water-holding construction in coal mine tunnels

Spatiotemporal deep learning approach for estimating water content profiles in soil layers

Land subsidence associated with using natural groundwater resources for serving the growing population needs has been receiving extensive research attention in the literature over the past few decades. The water content fluctuation in the of subsurface soil layers significantly impacts the land subsidence. The key objective of this study is to predict changes in water content profiles in soil layers over a long period of time using a deep learning-based approach. A convolution neural network algorithm that is commonly used in Artificial Intelligence (AI) applications is modified in the present study for processing in-situ measurement water content profiles. The approach used in the proposed AI method has a distinct advantage for generating dynamic predictions based on the extracted spatiotemporal characteristics of the data. In addition, three different algorithms are compared with respect to time series prediction, including long-short-term memory (LSTM), multiple-layer perceptron (MLP) networks and autoregressive integrated moving average (ARIMA).

Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland

Satellite-based soil moisture (SM) retrieval within agricultural regions is challenging due to pronounced vegetation and land cover variations during the growing season. Vegetation water content (VWC)–used to describe vegetation opacity in both the Soil Moisture Active Passive (SMAP) Dual Channel Algorithm (DCA) and V-polarization Single Channel Algorithm (SCA-V)–is approximated using a climatological Normalized Difference Vegetation Index (NDVI) time series. To understand the impact of VWC algorithms on SMAP SM retrievals, we investigated optimal vegetation indices for VWC estimation, and further evaluated the impact of VWC uncertainty on DCA and SCA-V SM retrievals. Besides NDVI, we applied four dynamic vegetation indices (VIs) acquired from Sentinel-2–including two red-edge VIs (Modified Chlorophyll Absorption Ratio Index (MCARI) and Inverted Red-edge Chlorophyll Index (IRECI)) and two non-red-edge VIs (Normalized Difference Water Index (NDWI1 and NDWI2)) to estimate growing-season corn VWC and the impact of different empirical relationships on VWC estimates. We find that climatological NDVI from Moderate Resolution Imaging Spectroradiometer (MODIS) significantly underestimates VWC, and therefore underestimates SM over croplands in Northwest China during the growing season, whereas dynamic VIs can improve the accuracy of VWC and SM retrievals. With respect to the choice of VI for VWC estimation, MCARI is more accurate during the peak growing season compared to its counterpart non-red-edge VIs; however, it also overestimates VWC during the early growing stage. In addition, the influence of VWC uncertainty on SM accuracy is shown to be higher in SCA-V than in DCA. The results of this study provide insight into preferred approaches for the accurate estimation of VWC and associated improvement in passive microwave algorithms for SM retrieval.

A Stacked Machine Learning Algorithm for Multi-Step Ahead Prediction of Soil Moisture

A trustworthy assessment of soil moisture content plays a significant role in irrigation planning and in controlling various natural disasters such as floods, landslides, and droughts. Various machine learning models (MLMs) have been used to increase the accuracy of soil moisture content prediction. The present investigation aims to apply MLMs with novel structures for the estimation of daily volumetric soil water content, based on the stacking of the multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR). Two groups of input variables were considered: the first (Model A) consisted of various meteorological variables (i.e., daily precipitation, air temperature, humidity, and wind speed), and the second (Model B) included only daily precipitation. The stacked model (SM) had the best performance (R2 = 0.962) in the prediction of daily volumetric soil water content for both categories of input variables when compared with the MLP (R2 = 0.957), RF (R2 = 0.956) and SVR (R2 = 0.951) models. Overall, the SM, which, in general, allows the weaknesses of the individual basic algorithms to be overcome while still maintaining a limited number of parameters and short calculation times, can lead to more accurate predictions of soil water content than those provided by more commonly employed MLMs.

Thermal-physical modeling and parameter identification method for dynamic model with unmeasurable state in 10-kW scale proton exchange membrane fuel cell system

For proton exchange membrane fuel cell, the accurate physical modeling, involving dynamic modeling and parameter identification, plays a significant role in simulation and control. The literature presents many optimization algorithms, for instance, gradient-based and metaheuristic method, to identify the parameters of the steady-state model, whereas lack of an effective way to carry out the identification for the dynamic model with unmeasurable states, which is very typical for the control issue. This paper handles these issues for the thermal dynamic model at a 10 kW fuel cell system. It is the first time that the identification, for the dynamic temperature model with two states and one of it unmeasurable, is proposed using step integration with continued fractions to obtain the theoretical value and using the p-dimensional extremum seeking via simplex tuning to optimize the mean square error between the theoretical and sampled value. The fitness between the theoretical and real value reaches 0.89 for stack inlet temperature and 0.90 for outlet temperature. It makes more sense that the identified parameters are within their typical value range. In addition, the method-empowered model obtains good validation in the full power range, from 1.2 kW to 7.2 kW. Through the comparison with traditional gradient-based optimization method, genetic algorithm, differential evolution method, and particle swarm optimization method, the method proposed in this paper features much lower calculation cost and higher fit accuracy. The accuracy of the newly proposed dynamic thermal model, considering the real fuel cell thermal transfer behavior, is improved from 0.54 to 0.89. The method is a promising way to the simulation of inner mass transfer, water content estimation, and especially for the controller design.

High abundance of solar wind-derived water in lunar soils from the middle latitude

Remote sensing data revealed that the presence of water (OH/H2O) on the Moon is latitude-dependent and probably time-of-day variation, suggesting a solar wind (SW)-originated water with a high degassing loss rate on the lunar surface. However, it is unknown whether or not the SW-derived water in lunar soil grains can be preserved beneath the surface. We report ion microprobe analyses of hydrogen abundances, and deuterium/hydrogen ratios of the lunar soil grains returned by the Chang'e-5 mission from a higher latitude than previous missions. Most of the grain rims (topmost ~100 nm) show high abundances of hydrogen (1,116 to 2,516 ppm) with extremely low δD values (-908 to -992‰), implying nearly exclusively a SW origin. The hydrogen-content depth distribution in the grain rims is phase-dependent, either bell-shaped for glass or monotonic decrease for mineral grains. This reveals the dynamic equilibrium between implantation and outgassing of SW-hydrogen in soil grains on the lunar surface. Heating experiments on a subset of the grains further demonstrate that the SW-implanted hydrogen could be preserved after burial. By comparing with the Apollo data, both observations and simulations provide constraints on the governing role of temperature (latitude) on hydrogen implantation/migration in lunar soils. We predict an even higher abundance of hydrogen in the grain rims in the lunar polar regions (average ~9,500 ppm), which corresponds to an estimation of the bulk water content of ~560 ppm in the polar soils assuming the same grain size distribution as Apollo soils, consistent with the orbit remote sensing result.

Review of Ground Penetrating Radar Applications for Water Dynamics Studies in Unsaturated Zone

For water dynamics investigation in unsaturated (vadose) zones, ground penetrating radar is a popular hydro-geophysical method because it is non-invasive for soil, has high resolution and the results have a direct link with water content. Soil water content and soil hydraulic properties are two key factors for describing the water dynamics in vadose zones. There has been tremendous progress in soil water content and soil hydraulic properties estimation with ground penetrating radar. The purpose of this paper is to provide an overview of the application of ground penetrating radar for soil water dynamics studies. This paper first summarizes various methods for the determination of soil water content. including traditional methods in the surveys of surface ground penetrating radar, borehole ground penetrating radar, and off-ground ground penetrating radar, as well as relatively new methods, such as full waveform inversion, the average envelope amplitude method, and the frequency shift method. This paper further provides a review for estimating soil hydraulic properties with GPR according to the types of ground penetrating radar data. We hope that this review can provide a reference for the application of ground penetrating radar in soil water dynamics studies in the future.

Estimation of Residual Pore Water Content in Hydrate-Bearing Sediments at Temperatures below and above 0 °C by NMR

Estimation of Residual Pore Water Content in Hydrate-Bearing Sediments at Temperatures below and above 0 °C by NMR

Retrieval of High-Resolution Vegetation Optical Depth from Sentinel-1 Data over a Grassland Region in the Heihe River Basin

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland.

Estimation of Evapotranspiration and Soil Water Content at a Regional Scale Using Remote Sensing Data

The timely and accurate estimation of soil water content (SWC) and evapotranspiration (ET) is of great significance in drought estimation, irrigation management, and water resources comprehensive utilization. The unsupervised classification was used to identify the crops in the region. Based on MOD16A2 and the meteorological data, a SEBS model was used to estimate the ET in the Jiefangzha Irrigation Field from 2011 to 2015. Based on the crop water stress index (CWSI), the SWC in 2014 was retrieved and verified with the measured SWC on different underlying surfaces (sunflower, corn, wheat, and pepper). The results showed that: (1) The positional accuracy of maize, sunflower, wheat, and pepper are 0.81, 0.80, 0.90, and 0.82, respectively; (2) The annual ET from 2011 to 2015 presented well the spatial distribution of the ET within the field; (3) The validation results of the estimated SWC on the underlying surface of wheat and sunflower showed a good robustness, the R2 was 0.748 and 0.357, respectively, the RMSE was 2.61% and 2.309%, respectively, and the MAE was 2.249% and 1.975%, respectively. However, for maize and pepper with more irrigation times, the SWC estimation results, based on the CWSI were poor, indicating that the method was more sensitive to soil drought and suitable for the crop SWC estimation with less irrigation and drought tolerance. The results can provide a reference for the agricultural water resources management and the irrigation forecast at a regional scale.

ATR-FTIR, SAXS and UV-Vis Studies of Silicone Hydrogel and Bio-Hydrogel Soft Contact Lenses

Aims: This study aimed to evaluate the effect of contact lens materials on the structural properties and to examine ultraviolet (UVA part) and visible (Vis) transmittance with and without UV filters of the commercially available silicone hydrogel (SiHy) and bio-hydrogel (bio-Hy) soft contact lenses (CLs) in vitro.
 Place and Duration of Study: Hacettepe University, Department of Physics, Ankara, Turkey, between May 2018 and May 2021.
 Methodology:Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectra of CLs were recorded (at removing from its package, after 10 min, 1 h and 1 day at room temperature) in the 4000-650 cm-1region to estimate water contents of CLs. Hierarchical Cluster Analysis (HCA) was performed to differentiate chemical structure of CLs based on the spectral differences. Ultraviolet (UVA) and visible light transmittance of (CLs) was measured in the 315 -800 nm region. Small Angle X-ray Scattering (SAXS) analyses were performed to obtain further structural information on nano-scale. 
 Results: One of the key observations in this study is the large influence of lens water content. The HCA analysis grouped all the CLs of same brand in same cluster based on their chemical similarity. The UVA transmittance results showed that CLs with UV blockers almost met ClassI and ClassII standards. The size (11.8-39.9 nm) and differences in morphologies of the nano globules were determined and correlated with equilibrium water content (EWC).
 Conclusion: This work was designed to explain important characteristics of commercial CLs and results will have implications for future experimental and clinical research regarding hydration/ dehydration experiments with CL polymers.

Heterogeneity of nitrate reduction indicators across a tile drained agricultural catchment in East Jutland, Denmark

Detailed knowledge of the nitrate (NO3−) attenuation capacity of different landscapes is essential to support current aspirations of locally differentiated fertilizer nitrogen (N) regulation for protection of aquatic ecosystems. In this context, we studied the spatial heterogeneity of NO3−, ammonium (NH4+), and total N concentrations in pore water collected in an artificially drained sub-catchment and complemented this with measurements of apparent electrical conductivity (ECa), potential denitrification rates, and basic soil physico-chemical properties. Piezometer nests were installed in transects to represent a range of ECa and slopes in the sub-catchment and were then categorized according to predetermined classifications to delineate the different redox environments. Water samples were collected (February to August 2017) from piezometers that were installed in nests with screen depths at 25–75 cm, 85–115 cm and 125–165 cm. For transects with an average ECa higher than 20 mS m−1, NO3− concentrations were <5 mg N L−1 at the depth slightly above the drainage depth, whereas transects with an average ECa of <20 mS m−1 had NO3− concentrations ranging from 0.2 to 15 mg N L−1. Transects with ECa higher than 20 mS m−1 had the highest average clay content and estimated water contents and, possibly, higher macropore connectivity across the soil profile, which could enhance NO3− transport and the potential for development of anaerobic microsites facilitating microbial denitrification. Potential denitrification rates correlated with the presence of amorphous and poorly crystalline iron (Fe) oxides, yet the results were inconclusive regarding the possible role of Fe(II) mediated NO3− reduction. By categorizing the piezometer transects according to their average ECa values, patterns of N concentrations were found that reflected differences in nitrate attenuation capacity, thereby supporting the further pursuit of ECa as a simple and operational tool for mapping the variability in nitrate reduction patterns in the unsaturated zone on a sub-catchment scale.

Temperature Effect on Electrical Resistivity Measurement Using an Embedded Sensor to Estimate Concrete Water Content

Concrete resistivity measurements strongly depend on the temperature and the water content of the structure. In this paper, a study of the effect of the temperature and saturation degree on electrical resistivity measurement is carried out using an embedded printed circuit board sensor to estimate water content profiles in concrete structures. Resistivity measurements are performed at temperatures between 20 and 60 °C. Experimental results are presented and analyzed in light of well-established empirical models. Calibration curves that link the electrical resistivity to the degree of saturation at a given temperature are discussed. Arrhenius laws that depend on the degree of saturation can be used to fit our data. In the perspective of the instrumentation and monitoring of concrete structure in real conditions, it is important to master the temperature correction laws of resistivity measurement to evaluate the gradients of water saturation degree.

Constraining Magma Reservoir Conditions by Integrating Thermodynamic Petrological Models and Bulk Resistivity From Magnetotellurics

AbstractMagnetotelluric (MT) data image the bulk resistivity of the subsurface which can be used to infer magma reservoir conditions beneath volcanoes. The bulk resistivity of magma depends primarily on the melt volume fraction, temperature, and water content. These variables are coupled thermodynamically, yet mixing relations for bulk resistivity implicitly treat them as independent. Here, we use a parameterization of the rhyolite‐MELTS thermodynamic model to constrain relationships between melt fraction, temperature, dissolved water content and bulk resistivity for rhyolitic magmas. This method results in MT interpretations which are (a) thermodynamically consistent at near‐equilibrium conditions, (b) independent of temperature and water content estimates derived from erupted products, and (c) able to consider saturated melts containing a volatile (i.e., aqueous fluid) phase. The utility of the method is demonstrated with three case studies of silicic systems: Mono Basin, Newberry volcano and the Laguna del Maule Volcanic Field (LdMVF). The moderately conductive feature at Mono Basin can be explained by under‐saturated partial melt (6–15 vol%) at &lt;775°C, indicating relatively stable magma storage conditions since the last eruption. However, a relatively resistive feature at Newberry Volcano requires lower temperatures (&lt;750°C) than previous estimates, suggesting that the system has cooled since the last eruption. A conductive feature at the LdMVF cannot be explained by saturated or under‐saturated rhyolitic melt and requires additional conductive phases. These results demonstrate the potential of this new method to reduce uncertainty in MT interpretations and highlight the need for additional coupling strategies between petrology, geophysics, and thermo‐mechanical models to better understand magmatic systems.

Estimation of chlorophyll, macronutrients and water content in maize from hyperspectral data using machine learning and explainable artificial intelligence techniques

ABSTRACT We used the secondary hyperspectral data set (leaf reflectance of maize) taken in the spectral range from 350 to 2500 nm in a field (under low and high nitrogen conditions) and glasshouse experiment. Water, chlorophyll, nitrogen (N), phosphorus (P) and potassium (K) contents of maize leaves taken from these experiments were measured using the standard methods. Six machine learning regression algorithms (Random Forest, Support Vector Regression, k-Nearest Neighbours, Multilayer Perceptron, Gradient Boosting Regression and Partial Least Square Regression) were used for development of the models to predict these parameters from leaf reflectance data. Each plant parameter was estimated with a different machine learning algorithm. Explainable artificial intelligence methods were used to identify the optimum wavelengths for each parameter. The wavelengths in the short-wave infrared (SWIR) region were found optimum for estimating the water content across the three N regimes, and the red-edge band for chlorophyll. The optimum wavelengths for estimating N content in leaves were in the green spectral region under low N status, near-infrared (NIR) and SWIR regions under greenhouse and SWIR region under high N status. The important wavelengths for estimating P in maize leaves were 1088, 1262 and 1263 nm under low N status and the SWIR range under high N status and greenhouse conditions. The SWIR band was useful for estimating the K content in leaves.

Evaluation of low-cost capacitive moisture sensors in three types of soils in the Cerrado, Brazil

Soil moisture sensors enabled new automation and computerization solutions in agriculture. However, its use still requires prior calibrations to attest its accuracy to estimate soil moisture. Thus, in this study a prototype for soil moisture monitoring was developed using the capacitive sensor SKU:SEN0193 integrated with the Arduino microcontroller. The prototype was calibrated based on the relationship between volumetric moisture and sensor output voltage in deformed samples of Red-yellow Latosol (RYL), Regolitic Neosol (RN), and Red Latosol (RL) of the Cerrado region, Federal District, Brazil. The calibration of sensors resulted in a second-degree polynomial model with R² between 0.93 and 0.96 for RYL, 0.89 and 0.92 for RN, and 0.86 and 0.88 for RL. Root mean square errors (RMSE) of volumetric moisture were 0.08, 0.12, and 0.15 cm3.cm-3 for RYL, RN, and RL, respectively. Sensors at operating voltage of 5.00 V showed less variability of readings, with coefficient of variation (CV) between 0.21 and 1.54%, while sensors at 3.30 V showed CVs between 3.27 and 17.84%. It is recommended to calibrate the SKU:SEN0193 for each soil type in order to obtain reliable estimates of water content for RYL, RN, and RL.

Monitoring unsaturated water flow using magnetic resonance soundings

We present an innovative hydrogeophysical approach for non-invasive quantification of the unsaturated water flow. For the water content measurements, we apply the Magnetic Resonance Sounding (MRS) method in the time-lapse mode. For inversion of MRS measurements, we approximate the subsurface by a horizontally stratified media. Laterally, the MRS estimated water content is averaged over the area below the measuring setup. During this study, we used a square loop of 75 m side-length. The time-lapse inversion allows visualizing variations in the water content in the vadose zone down to twenty meters. We tested our approach at the Villamblain test site (France). For that, we carried out the MRS monitoring in 1999–2000 and 2020–2021. During these two observation windows, MRS shows seasonal variations of the water content but also a strong dependence of the water content on the rainfall that was different between two data sets. Comparison of MRS results with the rainfall records shows a good correspondence in-between. For measuring and processing MRS data, we used commercially available MRS instrument and interpretation software.

Comparison of various approaches for estimating leaf water content and stomatal conductance in different plant species using hyperspectral data

Water deficit stress is a frequent phenomenon that inhibits plant growth. This study explores the performance of hyperspectral data for estimating the leaf water content of ten tree species under different water conditions. The three most commonly used leaf water content indicators (relative water content, equivalent water thickness, and fuel moisture content) and stomatal conductance were assessed using narrow-band indices (single band, band ratio, band subtraction, and band difference) and multivariate analyses (partial least squares regression (PLSR), support vector regression, artificial neural network, and random forest) within the 350–2500 nm spectral reflectance range. The results indicated that the best bands and band combinations were mainly concentrated in the short-wavelength infrared region, which is sensitive regarding plant water content. Compared to a single band, dual-band indices exhibited better overall performance among the four kinds of indices. Multivariate analyses are more accurate than narrow-band indices. Among these, PLSR is the most robust and can be considered the optimal technique for predicting the water content of all tree species, except for conifer species. However, accurately predicting stomatal conductance is difficult to predict using these methods. This study shows that the PLSR model can accurately estimate leaf water content in multiple tree species, and hyperspectral technology, such as hyperspectral remote sensing, has potential regarding the estimation of leaf water content.

UAV-based multispectral and thermal cameras to predict soil water content – A machine learning approach

Soil water content (SWC) estimation is a crucial issue of agricultural production, and its mapping is an important task. We aimed to study the efficacy of UAV-based thermal (TH) and multispectral (MS) cameras in SWC mapping. Soil samples were collected and the SWC content was determined in a laboratory as reference data and four machine learning regression algorithms (Random Forest [RF], Elastic Net [ENR], General Linear Model [GLM], Robust Linear Model [RLM]) were tested for the prediction efficacy, combined with three pixel value extraction methods (single pixel, mean of 20 and 30 cm radius buffer). We found that MS cameras ensured better input data than TH cameras: R2s were 0.97 vs 0.71, mean-normalized root mean square errors (nRMSE) were 10 vs 25 %, respectively. Best models were obtained by the RF (0.97 R2) and ENR (0.88 R2) in case of MS camera. Relationship between SWC and thermal data was exponential, which was incorrectly handled by the GLM (>40 % nRMSE; furthermore, RLM and ENR was not working with only one variable), thus, TH data was acceptable only with the RF (24.4 % nRMSE). Single pixel extraction provided the best input for the estimations, mean of buffered areas did not perform better in the models. Maps provided appropriate SWC estimations according to the nRMSEs, with high spatial resolution. In spite of potential inaccuracies, visualizing the spatial heterogeneities can be a great help to farmers to increase the efficacy of planning irrigation in precision agriculture.

Impact of an Uncertain Structural Constraint on Electrical Resistivity Tomography for Water Content Estimation in Landslides

Geoelectrical methods can be part of early warning systems for landslide-prone hillslopes by giving estimates of the water content distribution. Structurally constrained inversions of geoelectrical data can improve the water content estimation by reducing the smoothness constraint along known lithological boundaries, which is especially important for landslides, as often layers with strongly divergent hydrological parameters and varying electrical signatures are present in landslides. However, any a priori information about those boundaries has an intrinsic uncertainty. A detailed synthetic study and a field investigation are combined to study the influence of misplaced structural constraints and the strength of the smoothness reduction via a coupling coefficient on inversion results of electrical resistivity data. While a well-known lithological boundary with a substantial reduction of the smoothness constraint can significantly improve the inversion result, a flawed constraint can cause strong divergences from the synthetic model. The divergence can even grow above the divergence of a fully smoothed inversion result. For correctly placed structural constraints, a coupling coefficient smaller than 10−4 uncovers previously unseen dynamics in the resistivity distribution compared to smoothed inversion results. Uncertain layer boundaries can be included in the inversion process with a larger coupling coefficient to avoid flawed results as long as the uncertainty of the layer thickness is below 20%. The application to field data confirms these findings but is less sensitive to a further reduction of the coupling coefficient, probably due to uncertainties in the structural information.