Prediction Of Water Content Research Articles

Prediction of the soil water content in the Luanhe river basin based on CMIP6

A hydrological model (Soil and Water Assessment Tool, SWAT) of the Luanhe River Basin was built based on collected hydrological and meteorological data to better understand how the soil water content varies under climate change. To assess the applicability of the eight selected global climate models (GCMs), four single indicators and one comprehensive indicator were calculated at multiple stations. According to the evaluation results, a weighted multimode ensemble was established. To downscale and correct the deviation from the meteorological data while maintaining the trend signal, the enhanced Delta-DCSI technique was utilized. Eventually, the corrected GCM data were used to drive the SWAT model to simulate the spatial and temporal changes in the soil water content in the Luanhe River Basin under future scenarios. The results indicated that (1) the SWAT model was suitable for and capable of simulating hydrological processes in the basin; (2) the SWAT model can be accurately driven by multimodel ensemble data after model optimization; (3) the soil water content in the basin slowly increased and fluctuated over time; and (4) in the future, lower soil water contents will occur in a few northern subbasins, and consistently higher water contents will occur in the remaining areas. The findings could provide a scientific basis for water resource management and ecological environmental protection in the Luanhe River Basin.

Numerical modeling of laundry drying in a commercial laundry plant: Incorporating water potential as a driving force for heat and mass transfer

Increasing energy efficiency and reducing emissions in industrial processes through waste-heat recovery systems are vital. The energy-intensive nature of professional laundry care generates waste heat from machines such as washers, dryers, and boilers. We focused on a waste-heat recovery system tailored for this sector. An accurate drying model is essential for analyzing its energy-saving potential.This study presents a water-potential-based drying model using equilibrium thermodynamics. Two measurement experiments and numerical simulations were conducted to verify the accuracy of the proposed model, and the experimental results were incorporated into the simulations. The first experiment collected and determined laundry samples' basic information and hygrothermal properties, including the surface area, initial weight, and heat and moisture transfer coefficients. The second experiment attempted to determine each laundry item's equilibrium water content and capacity at various humidity levels. Through simulations, a comparison was made between the surface temperature and water content predictions of the existing and proposed models. Experimental results revealed that the laundry surface temperature remained constant during the constant-rate drying period. However, during the falling rate period, it was influenced by water content. The drying characteristics differ based on the material and quilting method. The proposed model improved the temperature and water content predictions by approximately 80.07% and 63.08%, respectively, compared with the existing model. This model can effectively assess laundry drying processes, including the latent heat flux assessment of the indoor environment.

Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content (SWC) is one of the critical indicators in various fields such as geotechnical engineering and agriculture. To avoid the time-consuming, destructive, and laborious drawbacks of conventional SWC measurements, the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis. In this study, a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images, aiming to alleviate the interference of complex surface conditions with color information extraction. In addition, an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images. Meanwhile, a subset of 10 parameters is identified through three variable analytical methods. Then, four machine learning models for SWC prediction based on partial least squares regression (PLSR), random forest (RF), support vector machines regression (SVMR), and Gaussian process regression (GPR), are established using 32 and 10 characteristic parameters, and their performance is compared. The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions. The RGB, CIEXYZ, and CIELCH color spaces and lightness parameters, as the inputs, are more suitable for the SWC prediction models. Furthermore, it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy, and the GPR model has the best prediction performance (R2 ≥ 0.95, RMSE ≤ 2.01%, RPD ≥ 4.95, and RPIQ ≥ 6.37). The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction, providing valuable insights for mapping soil moisture fields.

Prediction model of water content in surface dead fuel based on convolution neural network and meteoro-logical factors regression.

Water content of surface fuels is an important indicator of forest fire risk level and fire behavior, and the prediction model of which has a significant effect on fire risk prediction and management. Based on field meteorological factors of Quercus mongolica and Pinus sylvestris var. mongolica forests and water content data of dead fuels on the ground, we conducted the relative importance ranking of meteorological factors random forest and Pearson correlation analysis, and predicted water content of fuels using deeply learned convolutional neural network and meteorological factors regression. The results showed that water content of Q. mongolica fuels in the wild was significantly higher than that of P. sylvestris var. mongolica. The results of random forest showed that the factors that had significant effect on water content of fuel were humidity, temperature, rainfall, wind speed, and solar radiation, with the importance ranking from the largest to the smallest. Results of correlation analysis showed that temperature, humidity, and rainfall of current day had a significant impact on water content of fuels, and certain correlations were observed between meteorological factors. The prediction R2 of the convolutional neural network model for the surface fuel water content of Q. mongolica and P. sylvestris var. mongolica forest was 0.928 and 0.905, the mean absolute error (MAE) was 6.1% and 8.1%, and the mean relative error (MRE) was 8.9% and 4.2%, respectively. However, the R2, MAE, MRE of meteorological factors regression were 0.495 and 0.525, 30.5% and 39.5%, 52.1% and 32.6%, respectively. The precision of convolution neural network model was significantly higher than that of meteorological factors regression. Our results showed that the deeply learned convolutional neural network could provide some reference for the prediction of fuel water content in the future, and effectively support higher level forest fire management.

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.

Prediction of Soil Water Content Based on Hyperspectral Reflectance Combined with Competitive Adaptive Reweighted Sampling and Random Frog Feature Extraction and the Back-Propagation Artificial Neural Network Method

The soil water content (SWC) is a critical factor in agricultural production. To achieve real-time and nondestructive monitoring of the SWC, an experiment was conducted to measure the hyperspectral reflectance of soil samples with varying levels of water content. The soil samples were divided into two parts, SWC higher than field capacity (super-θf) and SWC lower than field capacity (sub-θf), and the outliers were detected by Monte Carlo cross-validation (MCCV). The raw spectra were processed using Savitzky–Golay (SG) smoothing and then the spectral feature variable of SWC was extracted by using a combination of competitive adaptive reweighted sampling (CARS) and random frog (Rfrog). Based on the extracted feature variables, an extreme learning machine (ELM), a back-propagation artificial neural network (BPANN), and a support vector machine (SVM) were used to establish the prediction model. The results showed that the accuracy of retrieving the SWC using the same model was poor, under two conditions, i.e., SWC above and below θf, mainly due to the influence of the lower accuracy of the super-θf part. The number of feature variables extracted by the sub-θf and super-θf datasets were 25 and 18, respectively, accounting for 1.85% and 1.33% of the raw spectra, and the variables were widely distributed in the NIR range. Among the models, the best results were achieved by the BPANN model for both the sub-θf and the super-θf datasets; the R2p, RMSEp, and RRMSE of the sub-θf samples were 0.941, 1.570%, and 6.685%, respectively. The R2p, RMSEp, and RRMSE of the super-θf samples were 0.764, 1.479%, and 4.205%, respectively. This study demonstrates that the CARS–Rfrog–BPANN method was reliable for the prediction of SWC.

Variation and prediction of unfrozen water content in different soils at extremely low temperature conditions

The unfrozen water content in frozen soil is the critical factor for liquid water migration, frost heaving and thawing settlement during freeze–thaw process, and its content largely depends on soil texture and temperature. Herein, the unfrozen water content in different soils was measured by low-field nuclear magnetic resonance (NMR) at extremely low temperature ranging from −80 to 0 °C. The effect of soil properties on the variation of pore water in soil during freezing is analysed by combining T2 relaxation curve and freezing characteristic curve. The results indicate that soil properties affect the phase transition process between liquid water and solid ice in frozen soil, and the pore size distribution plays a leading role in capillary water freezing in the severe phase transformation zone. When unfrozen water develops from the transitional phase transformation zone to the frozen stability zone, the freezing of adsorbed water in soil is affected by clay content. In addition, by analogy with the soil freezing characteristic curve, a prediction model of unfrozen water content of soil is established in accordance with the phase transition process of liquid water, and the accuracy of the model is further confirmed by comparing the test results. Sensitivity analysis of the proposed model parameters revealed that the parameters are closely related to the pore size distribution and the clay content. The proposed model expands the response range between the variation of unfrozen water and temperature by experimental means, and realizes the prediction of unfrozen water content in different types of soil at extremely low temperature. This study would provide significant reference for exploring the heat-moisture migration and mechanical strength in frozen soil.

Variability of in situ soil water retention curves under different tillage systems and growing seasons

Soil pore space can change over time due to tillage, frost, wetting and drying cycles, plant roots, and compaction. However, a soil water retention curve (SWRC) is usually considered to be static and is determinedin a laboratory on a limited number of small soil samples. Field SWRCs were determined at a depth of 20 cm on Mollic Gleysol. Measurements were made in two growing seasons at different spatial locations on plots with different tillage systems: no-tillage (NT) and conventional tillage (CT). The unimodal van Genuchten retention function (VG) was fitted to each SWRC and model parameters were estimated. We evaluated the variability of the curves within growing seasons, predicted soilwater contents at field capacity, and evaluated the differences in the estimated parameters of the VG model between different growing seasons and tillage systems considering intraseasonal and spatial variability. The shape of the curves depended more on the environmental conditions during the growing season than on the tillage system. In the wetter and cooler growing season of 2020, when soybeans were grown, the soil retained water in a narrower range. In the drier growing season of 2021, when maize was grown, intraseasonal variability in SWRCs was greater than in 2020. Regardless of the growing season, estimated α values were more intraseasonally variable under the CT than under the NT. Based on the SWRCs, predicted water contents at field capacity varied intraseasonally, seasonally, and spatially. Accounting for spatial and intraseasonal variability, the estimated VG parameter θr differed between years, regardless of tillage system. In 2020, the parameter α was higher under the NT system than under the CT system. Under the CT, α differed between years, while under NT it did not. For the parameter n, there were no significant differences between tillage systems or growing seasons. Seasonal, intraseasonal, and spatial variability in soil hydraulic properties should be considered in soil water modelling studies.

Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Research on soil moisture content combination prediction model based on ARIMA and BP neural networks

AbstractPredicting soil moisture accurately is the precondition of realizing accurate irrigation and improving the utilization rate of water resource and the necessary step of developing water‐saving agriculture, which can alleviate the water shortage in our agricultural effectively. In order to further improve the accuracy of soil water content prediction, a combined soil water content prediction model based on Autoregressive moving average model (ARIMA model) and back propagation neural network (BP neural network) neural network is proposed. The model considers the linear and nonlinear characteristics of soil water content data, combines them according to the characteristics of the model itself, gives full play to the advantages of ARIMA model and BP neural network. At the same time, two data smoothing methods were used to establish the ARIMA model, and the adaptive moment estimation algorithm (Adam algorithm) and mind evolutionary algorithm (MEA) optimization BP neural network model were used to propose an improved combined prediction model to predict soil water content data. The experimental results show that the average relative error of the improved combinatorial prediction model is 1.51%, which is 4.18%, 0.95% and 3.1% lower than the combinatorial prediction model, BP neural network model and ARIMA model, respectively, and the overall prediction effect is better, which can be used to save agricultural water and provide a strong basis for the development of water‐saving agriculture in China. At the same time, it can also ensure that crop production is increased and the purpose of national food security is guaranteed.

3D modelling of surface spreading and underground dam groundwater recharge: Egri Creek Subbasin, Turkey.

This study investigated surface spreading and underground dam recharge methods to replenish groundwater in Turkey's Egri Creek Sub-basin of the Kucuk Menderes River Basin. A three-dimensional numerical model was employed for this purpose. Field and lab data are provided to the model for realistic simulations. Pumping test results were used to determine the aquifer parameters. The laboratory works involved sieve analysis, permeability tests, and porosity and water content prediction. The numerical model's boundary conditions were determined from the geological and hydrogeological characteristics of the study area. Initial conditions were expressed regarding water content and pressure head in the vadose zone. The numerical model was satisfactorily validated by simulating water levels in three different pumping wells in the study area. Seven different scenarios, each having a different pool size, were investigated for the surface spreading recharge method. The results showed that a pool size of 30 × 30m with a 6-m depth basin was the most optimal choice, raising the groundwater level to about 29.3m. On the other hand, it was found that an underground dam could raise the levels by an average of 9.5m, which might not be significant to warrant the construction.

Thermo-hydro-mechanical analysis of the complete lifetime of the bentonite barrier in the FEBEX in-situ test

The FEBEX test was a large-scale demonstration project for the deep geological disposal concept of nuclear waste involving bentonite seals that lasted 18 years. One of the objectives of the test was to evaluate the capabilities of numerical methods to provide reliable predictions of the physical processes in a geological repository. Although previous studies have demonstrated the performance of current models of water, vapour and heat flow to capture the evolution of temperature and relative humidity, some uncertainties remain in the capabilities of constitutive models to predict and interpret the stress–strain behaviour of the bentonite. In this paper a recently developed thermo-hydro-mechanical (THM) elastoplastic constitutive model is used to analyse the bentonite barrier of the FEBEX test by means of the Finite Element method. The model features a two-way hydro-mechanical coupling and includes thermo-plasticity. The associated water retention formulation distinguishes the behaviour of adsorbed water and free water. The predictive capabilities of the model are tested by calibrating the material parameters on the sole basis of laboratory tests. Good predictions of total stress, dry density and water content are obtained and the analysis of the computed THM stress paths provides new insights on the causes of the final heterogeneous state of the bentonite barrier.

Prediction of porosity and water saturation of chalks from combined refraction seismic and reflection ground-penetrating radar measurements

The prediction of porosity and water content of near-surface chalks is important for agricultural, environmental, hydrologic, and geologic engineering investigations. However, current methods based on laboratory measurements or crosshole ground-penetrating radar (GPR) are typically costly and applicable to only a limited area. We have combined the refraction seismic and reflection GPR data sets measured on the ground surface along a 96 m long profile line to predict the porosity and water saturation simultaneously. Using integrated rock-physics templates, we develop and apply a quantitative method that combines acoustic and electromagnetic velocities to estimate the petrophysical parameters of chalks. The seismic and GPR data are processed independently and subsequently combined to invert the porosity and water saturation. Field data tests demonstrate that the predicted results are consistent with laboratory measurements and field observations, demonstrating that the integration of seismic and GPR data facilitates efficient and reliable estimates of the porosity and saturation of chalks on scales relevant to regional field mapping. Hence, this method allows the petrophysical parameters of near-surface chalks to be evaluated efficiently at a regional scale.

Inversion of reclaimed soil water content based on a combination of multi-attributes of ground penetrating radar signals

Rapid, accurate, and non-destructive acquisition of the distribution of reclaimed soil moisture information can provide data for the rapid monitoring of reclaimed soil in areas experiencing coal mining subsidence. However, the water content inversion methods based on ground penetrating radar (GPR) are mostly single-attribute analysis methods, which are easily affected by the soil structure. This paper proposes a multi-attribute joint analysis method, which can reduce the influence of complex soil structure on the prediction results. Surveys and soil sampling using GPR were performed on a subsided reclamation area in Huaibei City, Anhui Province, China. Correlation analysis of GPR attribute information and volumetric water content (VWC) showed that frequency peak (FP), average envelope amplitude (AEA), energy, instantaneous amplitude area, instantaneous frequency area, and average instantaneous frequency values were significantly related to the VWC of reclaimed soil. The applicability of different single-attribute analysis methods under the condition of reclaimed soil was compared. Results showed that the predictive effects of FP and AEA attributes were better than other attributes. Generally, the single-attribute analysis method was greatly affected by the structure of reclaimed soil, so the accuracy and reliability of this method need to be further optimized. The prediction results of the single- and multi-attribute joint analysis methods in the structure of reclaimed soil were compared and analyzed. This showed that the prediction accuracy and model reliability of the multi-attribute method are both higher than those of the single-attribute method. The multi-attribute method can overcome the problem of insufficient accuracy of water content detection of ground-seeking radar in soil with a complex structure. Finally, the multi-attribute method was used to obtain the water distribution information in the reclamation area. Results of this study provide new methods and ideas for the prediction of water content by GPR in soils with complex structure.

Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images

For appropriate managing fields and crops, it is essential to understand soil properties. There are drawbacks to the conventional methods currently used for collecting a large amount of data from agricultural lands. Convolutional neural network is a deep learning algorithm that specializes in image classification, and developing soil property prediction techniques using this algorithm will be extremely beneficial to soil management. We present the convolution neural network models for estimating water content and dry density using soil surface images. Soil surface images were taken with a conventional digital camera. The range of water content and dry density were determined considering general upland soil conditions. Each image was divided into segmented images and used for model training and validation. The developed model confirmed that the model can learn soil features through appropriate image argumentation of few of original soil surface images. Additionally, it was possible to predict the soil water content in a situation where various soil dry density conditions were considered.

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.

Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks

Geotechnical investigation plays an indispensable role in site characterization and provides necessary data for various construction projects. However, geotechnical measurements are time-consuming, point-based, and invasive. Non-destructive geophysical measurements (seismic wave velocity) can complement geotechnical measurements to save project money and time. However, correlations between geotechnical and seismic wave velocity are crucial in order to maximize the benefit of geophysical information. In this work, artificial neural networks (ANNs) models are developed to forecast geotechnical parameters from seismic wave velocity. Specifically, published seismic wave velocity, liquid limit, plastic limit, water content, and dry density from field and laboratory measurements are used to develop ANN models. Due to the small number of data, models are developed with and without the validation step in order to use more data for training. The results indicate that the performance of the models is improved by using more data for training. For example, predicting seismic wave velocity using more data for training improves the R2 value from 0.50 to 0.78 and reduces the ASE from 0.0174 to 0.0075, and MARE from 30.75 to 18.53. The benefit of adding velocity as an input parameter for predicting water content and dry density is assessed by comparing models with and without velocity. Models incorporating the velocity information show better predictability in most cases. For example, predicting water content using field data including the velocity improves the R2 from 0.75 to 0.85 and reduces the ASE from 0.0087 to 0.0051, and MARE from 10.68 to 7.78. A comparison indicates that ANN models outperformed multilinear regression models. For example, predicting seismic wave velocity using field plus lab data has an ANN derived R2 value that is 81.39% higher than regression model.

Prediction of deep soil water content (0–5 m) with in-situ and remote sensing data

Accurate and timely evaluation of soil water content (SWC) in water-limited arid and semiarid regions will provide an essential reference for scientifically based water management strategies and vegetation restoration practices. Theoretical and empirical models based on remote sensing data have been successfully applied to predict large-scale SWC. However, most existing models are limited to the surface soil layer, while the prediction of SWC in the deep soil layer (1–5 m) has been overlooked because of the shortage of long-term and large-scale filed-observed data. In this study, a south-north transect (ca. 860 km) was selected across the Chinese Loess Plateau (CLP). Based on both long-term remote sensing and in-situ data (2013–2016), pedo-transfer functions (PTFs) for the SWC in the 0–5 m profile were developed using multiple regression, random forest, and artificial neural network. The results showed that evapotranspiration, potential evapotranspiration, soil surface temperature, total shortwave broadband albedo, and normalized difference vegetation index were important remote sensing parameters, whereas soil texture and bulk density were important in-situ parameters for SWC PTFs development. Among the three PTF development methods, machine learning (i.e. random forest and artificial neural network) obtained a higher accuracy than multiple regression. For different combinations of input parameters, the introduction of in-situ factors significantly improved the accuracy of PTFs compared with PTFs based on remote sensing data only. The artificial neural network developed a PTF with only five input variables that predicted SWC with reasonable accuracy (root mean square error = 0.039, R2 = 0.697, mean absolute percentage = 24.8) and is thus useful for many applications on the Loess Plateau of China. In the future, more attention should be given to the role of in-situ parameters when developing PTFs for deep SWC prediction.

Deep adversarial domain adaptation for hyperspectral calibration model transfer among plant phenotyping systems

Hyperspectral imaging has widely been used for plant phenotyping applications. Calibration transfer is crucial for eliminating the prediction drift when a model developed in one plant phenotyping system needs to be deployed in another system. In this study two approaches (Domain Adversarial Neural Network - DANN and Adversarial Discriminative Domain Adaptation – ADDA) for transferring a deep learning model across hyperspectral imaging-based plant phenotyping systems were presented. The proposed techniques employ adversarial learning to minimise the discrepancy between the spectral data of two imaging facilities in the feature space. The calibration transfer analysis was examined by collecting the hyperspectral images of maize plants in two phenotyping facilities equipped with different sensors covering the visible near-infrared (VNIR) region under different lighting conditions. The capability of the suggested methods to transfer a relative water content (RWC) prediction model was investigated. The results showed that the prediction R 2 improved from 0.603 to 0.849 and 0.843 and RMSE reduced from 23.52% to 14.15% and 12.73% for DANN and ADDA, respectively. The study showed that both approaches improved the performance of the transferred model compared to a similar deep learning model without adversarial transfer. Visualisation of feature space showed that both methods lead to similar source and target feature distributions, thus indicating that a prediction model developed using these common features can successfully be applied to both imaging facilities. The main benefit of the DANN and ADDA is that they do not need the standard samples to be imaged in two phenotyping systems or the collection of labelled data to carry out the transfer. The adversarial learning-based calibration transfer can promote the sharing of prediction models across different phenotyping systems. • Differences across phenotyping facilities hinders the application of exiting models. • Calibration transfer between different phenotyping facilities is critically important. • Adversarial domain adaptation based methods were proposed for calibration transfer. • Proposed methods resulted in RWC model shared between two facilities. • Proposed methods can help to eliminate the need of full recalibration.

Spectrum Index for Estimating Ground Water Content Using Hyperspectral Information

Quality control considerably affects road stability and operability and is directly linked to the underlying ground compaction. The degree of compaction is largely determined by water content, which is typically measured at the actual construction site. However, conventional methods for measuring water content do not capture entire construction sites efficiently. Therefore, this study aimed to apply remote sensing of hyperspectral information to efficiently measure the groundwater content of large areas. A water content prediction equation was developed through an indoor experiment. The experimental samples comprised 0–40% (10% increase) of fine contents added to standard sand. As high water content is not required in road construction, 0–15% (1% increase) of water content was added. The test results were normalized, the internal and external environments were controlled for precise results, and a wavelength–reflection curve was derived for each test case. Data variability analyses were performed, and the appropriate wavelength for water content reflection, as well as reflectance, was determined and converted into a spectrum index. Finally, various fitting models were applied to the corresponding spectrum index for water content prediction. Reliable results were obtained with the reflectance corresponding to a wavelength of 720 nm applied as the spectrum index.