Estimation Of Soil Properties Research Articles

Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices

AbstractKnowledge of soil volumetric water content (VWC) on agricultural soils as influenced by different soil management practices is important, but the measurement outputs of frequently used traditional sampling techniques are restricted to point‐based measurements with limited spatial coverage. Remote sensing (RS) techniques are therefore being explored because of their greater spatial and temporal availability as well as their ability to cover large‐scale areas. But a general limitation for RS data is the presence of vegetation cover, cloud cover and the effect of topography. To minimize the effects of these factors during field sampling, the use of spectral indices and terrain attributes have proven successful in the estimation of several soil properties; however, the impact of these approaches for the estimation and mapping of soil VWC, an important factor in crop growth and development, especially where different soil management practices are employed, remains limited. To contribute to the knowledge base of RS under varying soil systems, this study explores the possibility of combining in‐situ measurements with remotely sensed (explanatory variables) data obtained under four different tillage practices to produce an estimated soil VWC that represents the entire study field. The tillage practices used include reduced till (RT), strategic till (ST), no‐till (NT) and conventional till (CT). From these tillage plots, three explanatory datasets, namely Sentinel‐2 (S2), spectral indices (SI) and terrain attributes (TA), were collected as predictors. In addition, each of the explanatory variables was structured into four groups, representing each of the four tillage methods. The eXtreme Gradient Boosting (XGBoost) model was used, and the best results were selected based on the root mean squared error (RMSE), the coefficient of determination (R2) and the mean absolute error (MAE). Prior to soil VWC prediction, the Pearson correlation matrix was used to determine the linear relationship between each of the selected explanatory variables (S2 bands, SI and TA) and soil VWC. Furthermore, spatial distribution maps of soil VWC were constructed using the inverse distance weighting (IDW) interpolation technique. For soil VWC estimation, the TA outperformed the SI and S2 datasets (R2 = 0.84). Similarly, the spatial distribution maps obtained from the TA data show a study area with a large concentration of VWC compared with the other datasets. The study also identified CT as the tillage approach that most impacts soil VWC estimation because all predicted results were poor without the addition of data from the CT plot. According to our findings, using TA data collected from various tillage management systems to estimate soil VWC is very promising because they can be used as predictors to improve soil VWC estimation and mapping. This study demonstrates conclusively that remote sensing data collected from different tillage management systems can be used as predictors to enhance the estimation and mapping of soil VWC, complementing the basis for the development of reliable and consistent precision irrigation management systems.

Impact of Spectral Resolution and Signal-to-Noise Ratio in Vis–NIR Spectrometry on Soil Organic Matter Estimation

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

Estimation of Soil Characteristics from Multispectral Sentinel-3 Imagery and DEM Derivatives Using Machine Learning.

In this paper, different machine learning methodologies have been evaluated for the estimation of the multiple soil characteristics of a continental-wide area corresponding to the European region, using multispectral Sentinel-3 satellite imagery and digital elevation model (DEM) derivatives. The results confirm the importance of multispectral imagery in the estimation of soil properties and specifically show that the use of DEM derivatives improves the quality of the estimates, in terms of R2, by about 19% on average. In particular, the estimation of soil texture increases by about 43%, and that of cation exchange capacity (CEC) by about 65%. The importance of each input source (multispectral and DEM) in predicting the soil properties using machine learning has been traced back. It has been found that, overall, the use of multispectral features is more important than the use of DEM derivatives with a ration, on average, of 60% versus 40%.

Combining ground penetrating radar methodologies enables large‐scale mapping of soil horizon thickness and bulk density in boreal forests

AbstractForest soil properties must be observed with the appropriate resolution by depth and landscape area to understand biogeomorphological controls on soil carbon (C). These observations, particularly in boreal forests, have been limited because of the poor resolution and unavailability of physical soil sampling results, especially for soil bulk density measurements. Ground penetrating radar (GPR) has been demonstrated to non‐destructively and continuously estimate forest soil properties required in Cstock estimates, such as soil horizon thickness and soil bulk density, across small spatial scales and shallow depths. Yet, successful small‐scale forest GPR approaches represent a potential opportunity to obtain soil property estimates at relevant resolution and depth across forest landscapes, enabling improvement to much needed soil mapping and stock estimates. This review discusses the existing soil property studies that utilize ground penetrating radar (GPR) and explores how the adaptation of GPR methodology can contribute to investigating soils in forest landscapes. We have identified common GPR surveying practices, data processing steps and interpretation methods employed in multiple studies. These approaches have proven effective in obtaining higher‐resolution estimates of important soil properties, such as bulk density and horizon thickness, within small‐scale forest plots. By applying relevant findings in this review to our own boreal forest investigation across an 80 m hillslope transect, we provide recommendations on how to tailor GPR methodology for landscape‐scale estimates of soil horizon thickness and bulk density to examine forest soil property distribution. These findings should enable the future collection of soil datasets informing the distribution of soil C stocks and their relationship to landscape features, and thus their controls and responses to climate and environmental change.

Simultaneous estimation of multiple soil properties under moist conditions using fractional-order derivative of vis-NIR spectra and deep learning

The application of visible and near-infrared (vis-NIR) spectroscopy for predicting soil properties presents a cost-effective and time-efficient approach for evaluating various soil properties. However, the accuracy of these predictions can be adversely affected by soil moisture. Consequently, previous research focused on removing moisture effects from the spectra, resulting in the loss of valuable information on soil moisture. In this study, we propose a novel approach to directly predict both soil moisture and a range of soil properties using vis-NIR spectra. To achieve this, we employed the fractional-order derivative (FOD) technique in combination with a one-dimensional convolutional neural network (1D-CNN) to mitigate the influence of soil moisture and enhance the prediction accuracy of soil properties. We calibrated 1D-CNN, partial least squares regression (PLSR) and support vector machine (SVM) models using FOD spectra with various orders (ranging from 0 to 2 with 0.1 intervals). The results demonstrated that the FOD spectra consistently outperformed the original and integer-order differential spectra. Moreover, the 1D-CNN models exhibited superior performance compared to PLSR and SVM models in predicting soil moisture content (SMC), soil organic matter (SOM), sand, silt, clay, and iron contents. However, it is challenging to predict all six soil properties using a single 1D-CNN model with spectra of a single FOD order. Our findings identified the optimal FOD order for different soil properties. Specifically, a FOD order of 0.5 yielded the best results for predicting SMC and SOM, while an order of 0.7 was optimal for iron oxides, clay, silt, and sand contents. Consequently, we propose the combined use of FOD and 1D-CNN to estimate multiple soil properties simultaneously.

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy

Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.

A deep scalable neural architecture for soil properties estimation from spectral information

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. As an additional feature, our method permits to backtrace the spectral bands that most contribute to the estimation of a given variable. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution. In fact, the proposed method achieves an overall R2 of about 0.75. In detail, pHCaCl2 and CaCO3 are predicted with an R2 above 0.90, pHH2O, OC, N, clay, sand, CEC, silt are predicted with an R2 between 0.89 and 0.70, K and P with an R2 respectively of 0.54 and 0.37. The proposed method overcomes, in terms of R2, the state of the art of about 15% on the LUCAS dataset and of about 24% on the PRISMA simulated dataset.

Different approaches to estimating soil properties for digital soil map integrated with machine learning and remote sensing techniques in a sub-humid ecosystem.

Today, data mining has become a relevant topic in digital soil mapping. In this current study, prediction of some soil properties and their spatial distribution were examined by machine learning algorithms (Support Vector Machine, Artificial Neural Network) using reflectance values of Triplesat satellite image bands in Vezirköprü district of Samsun province. The band data obtained from different wavelengths revealed positive correlations between the electrical conductivity and calcium carbonate equivalent contents of the soils. The support vector machine algorithm was the most successful to estimate the textural fractions, organic matter, electrical conductivity, and calcium carbonate equivalent contents of the soils using the bands obtained from satellite images. The mean absolute error for estimating sand, silt, and clay contents by support vector machine was 4.05%, 3.05%, and 3.66%, respectively. Texture classes were determined with an accuracy of 82% with support vector machine and 60% with artificial neural network. In all estimations, the highest percentage of error was for calcium carbonate equivalent content with very low estimation reliability. The mean absolute percentage of error values for this property are 101.13% and 51.61% for artificial neural network and support vector machine, respectively. Also, in both algorithms, the most successfully estimated soil property was clay fraction of soils. It was also investigated the spatial distribution of actual and estimated values using various interpolation methods (Kriging, inverse distance weighting-, radial basis function). Considering the spatial distributions, it was determined that the most successful method was kriging for sand, silt, and clay contents and inverse distance weighting for electrical conductivity, calcium carbonate equivalent, and organic matter contents. According to our findings, it is concluded that successful estimations and spatial distributions can be made by the support vector machine algorithm using band data from different wavelengths.

Development of Hungarian spectral library: Prediction of soil properties and applications

Updating soil information systems (SIS) requires advanced technologies to support the time and cost-effective and environment-friendly soil data. The use of mid- infrared (MIR) Spectroscopy as alternative to wet chemistry has been tested. The MIR spectral library is a useful technique for predicting soil attributes with high accuracy, efficiency, and low cost. The Hungarian MIR spectral library contained data on 2200 soil samples from 10 counties representing the first Soil Information and Mentoring System (SIMS) survey. Archived soil samples were prepared and scanned based on Diffuse Reflectance Infrared spectroscopy (DRIFT) technique and spectra data were saved in the fourier transform infrared (FTIR) spectrometer OPUS software. Preprocessed filtering methods, outlier detection methods and calibration sample selection methods were applied for spectral library. MIR calibration models were built for soil attributes using Partial Least Square Regression (PLSR) method. Coefficient determination (R2), The Root Mean Squared Error (RMSE) and Ratio of Performance to Deviation (RPD) were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to significantly estimate soil properties such as SOC, CaCO3, sand, clay and silt through various scale models (national, county and soil type). The findings showed that our spectral library soil estimations are precise enough to provide information on national, county and soil type levels enabling a wide range of soil applications that demand huge amounts of data such as soil survey, precision agriculture and digital soil mapping.

Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network

Visible-to-near-infrared (vis–NIR) and mid-infrared (MIR) spectroscopy have been widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis–NIR and MIR data can be hypothesized to provide accurate and reliable prediction for SOC because spectral data within a specific range of each individual sensor may lack important absorptive features associated with SOC. In this study, six data fusion strategies, principally direct concatenation-partial least squares regression (DC-PLSR), outer product analysis-PLSR (OPA-PLSR), OPA-competitive adaptive reweighted sampling-PLSR (OPA-CARS-PLSR), sequentially orthogonalized-PLSR (SO-PLSR), DC-convolutional neural network (DC-CNN), and parallel input-CNN (PI-CNN), were compared for the spectral estimations of SOC. The data fusion and individual sensor models were developed using soil samples collected from Zhejiang Province, East China, and scanned under laboratory conditions with both vis–NIR and MIR spectrophotometers. The validation results of vis–NIR (validation coefficient of determination [R2] = 0.63–0.73) were generally better than those of MIR (validation R2 = 0.45–0.59). For data fusion, the best validation accuracy was achieved by the PI-CNN (validation R2 = 0.84), followed in descending order by DC-CNN (validation R2 = 0.78), SO-PLSR (validation R2 = 0.73), OPA-CARS-PLSR (validation R2 = 0.69), OPA-PLSR (validation R2 = 0.66), and DC-PLSR (validation R2 = 0.64). The better performance of PI-CNN over DC-CNN demonstrates the necessity of using different sizes of convolutional kernels before feeding into the fully connected layers in the CNN network for fusing vis–NIR and MIR spectral data. The deep-learning fusion method based on PI-CNN can be considered an efficient tool for integrating data from multiple sensors for estimating soil properties in the field of soil spectral modeling.

Evaluation of a miniaturized portable NIR spectrometer for the prediction of soil properties in Mediterranean central Chile

Near-infrared (NIR) spectrometry to estimate soil properties has been evolving toward more portable and accessible equipment in recent years for the prediction of indicators of soil fertility in agroecosystems. There were two objectives of this study. First, to calibrate and validate a miniaturized portable NIR spectrometer NeoSpectra-Module 2.5 with a limited spectral range (1350–2500 nm) for the prediction of total nitrogen (TN), soil organic carbon (SOC), soil pH, clay content and exchangeable cations (Ca2+, Mg2+, K+ and Na+) in dry conditions. And second, to evaluate the use of an external orthogonalization parameter (EPO) to reduce the effect of soil moisture in samples with field moisture conditions collected from Mediterranean central Chile (33° SL to 36° SL). Soil classified as Mollisols, Alfisols and Inceptisols was sampled (n = 170) from 0 to 30 cm depth and scanned with the NeoSpectra spectrometer. The spectrometer showed good performance (R2 > 0.5 and/or RPIQ>1.4) for predictions of SOC and exchangeable cations. When the spectra data without transformation was used (raw spectra) versus transformed spectra, better performance in the predictions was obtained for TN, SOC, pH, clay content and Ca2+. The cubist model (CM) showed better performance than partial least squares (PLSR) for most assessed soil properties. The EPO procedure allowed us to decrease the error due to soil moisture in the prediction of some soil properties. Although the use of a miniaturized portable NIR spectrometer showed promising results for prediction of soil properties, future testing should increase the number of soil samples, vary the spectral data processing, and/or test new miniaturized portable NIR spectrometers with a wider spectral range.

Prediction of some selected soil properties using the Hungarian Mid-infrared spectral library

Routine soil chemical and physical laboratory analysis provides a better understanding of the soil by evaluating its quality and functions. Demands for the development of national Mid-infrared (MIR) spectral libraries for predicting soil attributes with high accuracy have risen substantially in the recent past. Such MIR spectral library is usually regarded as a fast, cheap and non-destructive technique for estimating soil properties compared to laboratory soil analysis. The main objective of this research was to assess the performance of the Hungarian MIR spectral library in estimating four soil properties namely: Cation Exchange Capacity (CEC), Exchangeable Mg and Ca and pH water at different scenarios. Archived soil samples were scanned and spectra data were saved in the Fourier transform infrared spectrometer OPUS software. Preprocessed filtering, outlier detection and calibration sample selection methods were applied to the spectral library. MIR calibration models were built for soil attributes using partial least square regression method and the models were validated with sample predictions. R2, RMSE and RPD were used to assess the goodness of calibration and validation models. MIR spectral library had the ability to estimate soil properties such as CEC and exchangeable Ca and Mg through various scale models (national, county and soil type). The findings showed that the Hungarian MIR spectral library for estimation of soil properties has the ability to provide good information on national, county and soil type scales at different levels of accuracy.

Estimation of Some Mechanical Soil Properties from Static and Dynamic Plate Load Tests

When the depth of stressed soil is rather small, Plate Load Test (PLT) becomes the most efficient test to estimate the soil properties for design purposes. Among these properties, modulus of subgrade reaction is the most important one that usually employed in roads and concrete pavement design. Two methods are available to perform PLT: static and dynamic methods. Static PLT is usually adopted due to its simplicity and time saving to be performs in comparison with cyclic (dynamic) method. The two methods are described in ASTM standard.
 In this paper the effect of the test method used in PLT in estimation of some mechanical soil properties was distinguished via a series of both test methods applied in a same site. The comparison of the test results between both methods showed that the dynamic (cyclic) method gives lower values of soil properties than the static one does.

Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

Estimating Soil Properties Distribution at a Restored Wetland Using Electromagnetic Imaging and Limited Soil Core Samples

Estimating Soil Properties Distribution at a Restored Wetland Using Electromagnetic Imaging and Limited Soil Core Samples

Estimation of soil and crop residue parameters using AVIRIS-NG hyperspectral data

ABSTRACT Detailed ground cover information and efficient modelling approaches are needed for estimating soil properties from hyperspectral remote sensing (HRS) data. With the objective to estimate both soil and crop residue (CR) parameters using HRS data from the airborne visible-infrared imaging spectrometer-next generation (AVIRIS-NG) sensor, soil and CR samples were collected from 101 locations in the Western Catchment of Chilika lagoon, India. Nonlinear unmixing and two chemometric models were examined for estimating basic soil properties, nutrient contents, soil and CR wetness, and masses of fresh crop residue (FCR) and dry crop residue (DCR). Both soil and CR parameters showed wide variation with FCR and DCR masses varying from 0 to 13.33 Mg ha−1 and 0 to 10.16 Mg ha−1, respectively. Soils were relatively dry with an average gravimetric water content (θg) of 14% whereas CR moisture content (θc) ranged from 2.4 to 93%. Estimated coefficient of determination (R2) values in the validation datasets varied from 0.51 for soil base saturation to 0.91 for exchangeable Mg+2 using soil spectra obtained through linear polynomial unmixing of AVIRIS-NG spectra. The R2 values were more than 0.70 for clay content, soil organic carbon (SOC), cation exchange capacity (CEC), and several exchangeable cations although the CR parameters showed lower R2 values than soil parameters. With the chemometric models calibrated, high spatial resolution maps for soil and CR parameters were generated, which offer a continuous mapping capability for such parameters. Extensive soil property data also allowed us to evaluate SOC sequestration capability of different land use systems. A critical value of 1/25 for the SOC/Clay content ratio was observed for most of our agricultural land uses and degraded landscapes showed lower SOC/Clay ratios. Thus, high spatial resolution soil and crop residue parameters may be accurately assessed for large areas with multiple land use and soil cover conditions.

On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries

In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region, we developed an AI-based spectral transfer function that maps fields to laboratory spectra. Three test sites in Cyprus, Lithuania, and Greece were used to evaluate the proposed methodology, while the dataset was harmonized and augmented by GEO-Cradle regional soil spectral library (SSL). The developed dataset was used to calibrate and validate machine learning models, with the attained predictive performance shown to be promising for directly estimating soil properties in-situ, even with sensors with reduced spectral range. Aiming to set a baseline scenario, we completed the exact same modeling experiment under laboratory conditions and performed a one-to-one comparison between field and laboratory modelling accuracy metrics. SOC and pH presented an R2 of 0.43 and 0.32 when modeling the in-situ data compared to 0.63 and 0.41 of the laboratory case, respectively, while clay demonstrated the highest accuracy with an R2 value of 0.87 in-situ and 0.90 in the laboratory. Calcium carbonates were also attempted to be modeled at the studied spectral region, with the expected accuracy loss from the laboratory to the in-situ to be observable (R2 = 0.89 for the laboratory and 0.67 for the in-situ) but the reduced dataset variability combined with the calcium carbonate characteristics that are spectrally active in the region outside the spectral range of the used in-situ sensor, induced low RPIQ values (less than 0.50), signifying the importance of the suitable sensor selection.

Physics-Informed Optimization for Dynamic Soil–Structure Interaction Analysis of a Pile Partially Embedded in Nonlayered Soils

This study proposes a novel physics-informed optimization approach to account for nonlayered soil conditions for the dynamic soil–structure interaction analysis that is critical for structural health monitoring involving soil–structure interaction. This approach can estimate soil properties based on a few measurements and then predict the natural frequency of structures, a key element that has been very often utilized for structural health monitoring and evaluation. A case study for frequency-based bridge scour detection has been presented for the demonstration of this approach, where a bridge pile is partially embedded in complex nonlayered soils. With this case study, we show that the nonlinear frequency–scour depth relationship can be accurately estimated using 2–4 measured data points, which is in favor of situations where conducting a field test is difficult or the demand for conducting field measurements needs to be reduced. The impact of errors resulting from measurement uncertainty on the performance of this approach is found to be insignificant. This physics-informed optimization approach thus can help analyze dynamic responses of engineering structures in complex nonlayered soil conditions involving soil–structure interaction.

Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model.

This paper coupled a unified passive and active microwave observation operator-namely, an enhanced, physically-based, discrete emission-scattering model-with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.