Bhabani S. Das

Modeling transient water distributions around landmines in bare soils

Sensors for landmine detection are often affected by soil water content, temperature, electrical conductivity, and/or dielectric constant. The most important of these is water content because it influences the three other properties directly. Using HYDRUS-2D, we modeled water distributions around antitank mines buried in six soil textures varying from sandy loam to clay loam under the climatic conditions of Bosnia and Kuwait. The modeling results demonstrate that soil water content regimes around landmines are strongly affected by the interaction between climate, soil type, and landmine geometry. The occasional short-term accumulation or loss of soil water around landmines depends greatly on weather conditions and soil types. Results also show that steady-state analysis of water flow around buried objects and time averaging of observed water contents may lead to unrealistic conclusions regarding the transient behavior of soil water distributions around land-mines.

Temperature dependence of nitrogen mineralization rate constant: a theoretical approach

Experimental evidence suggests that nitrogen (N) mineralization proceeds differently under fluctuating temperature conditions than it does at constant temperature. Although N mineralization is believed to follow first-order kinetics, and the mineralization rate constant is believed to follow Q10 temperature dependence, no effort has been made to use this information to predict the effect of fluctuating temperature on N mineralization. In this paper, we present solutions to the first-order N mineralization equation for a rate coefficient with Q10 temperature dependence. Solutions are presented for a number of simple patterns of temperature fluctuation in time. Example calculations show that the nonlinear temperature dependence of the Q10 relationship causes mineralization under fluctuating temperature conditions to exceed that occurring at constant temperature. Sensitivity analysis shows that the Q10 constant and the amplitude of the temperature fluctuation strongly influence the difference in mineralization obtained for the two temperature patterns. These results can be used to improve the design of experiments conducted to study the effect of-temperature fluctuations.

Enhancing dielectric contrast between land mines and the soil environment by watering: modeling, design, and experimental results

The complex dielectric constant of the soil surrounding a land mien and its contrast with the dielectric constant of the landmine are critical to the effectiveness of ground penetrating radar (GPR) for landmine detection. These parameters affect the velocity and attenuation of the radar signal as well as the strength of the reflection form the mine. The dielectric properties of the soil depend on the soil texture and bulk density as well as the soil water content. In previous work, we have simulated the unsaturated water flow around a landmine. In this paper we summarize a collection of models that can be used to predict the dielectric constant, velocity of the GPR signal, attenuation, and reflection coefficient form soil type and soil water content. These models have been integrated into a MATLAB software package. Using these models, we can determine whether or not field conditions are appropriate for use of GPR. Under dry conditions, the soil water content may be too low for good GPR performance. If the soil is too dry, we can select an appropriate level of soil water content and design a watering scheme to bring the soil water content up the desired level. We present a case study in which a soil watering scheme was designed, simulated, and the performed at a field site.

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation=3.2, validation r(2)=0.91 and RMSE=13.38 μg g(-1) h(-1)) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky-Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.

Spatial prediction of soil properties in a watershed scale through maximum likelihood approach

Surface map of soil properties plays an important role in various applications in a watershed. Ordinary kriging (OK) and regression kriging (RK) are conventionally used to prepare these surface maps but generally need large number of regularly girded soil samples. In this context, REML-EBLUP (REsidual Maximum Likelihood estimation of semivariogram parameters followed by Empirical Best Linear Unbiased Prediction) shown capable but not fully tested in a watershed scale. In this study, REML-EBLUP approach was applied to prepare surface maps of several soil properties in a hilly watershed of Eastern India and the performance was compared with conventionally used spatial interpolation methods: OK and RK. Evaluation of these three spatial interpolation methods through root-mean-squared residuals (RMSR) and mean squared deviation ratio (MSDR) showed better performance of REML-EBLUP over the other methods. Reduction in sample size through random selection of sampling points from full dataset also resulted in better performance of REML-EBLUP over OK and RK approach. The detailed investigation on effect of sample number on performance of spatial interpolation methods concluded that a minimum sampling density of 4/km2 may successfully be adopted for spatial prediction of soil properties in a watershed scale using the REML-EBLUP approach.

Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines

Abstract. Bilateral filter (BF) theory is applied to integrate spatial contextual information into the spectral domain for improving the accuracy of the support vector machine (SVM) classifier. The proposed classification framework is a two-stage process. First, an edge-preserved smoothing is carried out on a hyperspectral image (HSI). Then, the SVM multiclass classifier is applied on the smoothed HSI. One of the advantages of the BF-based implementation is that it considers the spatial as well as spectral closeness for smoothing the HSI. Therefore, the proposed method provides better smoothing in the homogeneous region and preserves the image details, which in turn improves the separability between the classes. The performance of the proposed method is tested using benchmark HSIs obtained from the airborne-visible-infrared-imaging-spectrometer (AVIRIS) and the reflective-optics-system-imaging-spectrometer (ROSIS) sensors. Experimental results demonstrate the effectiveness of the edge-preserved filtering in the classification of the HSI. Average accuracies (with 10% training samples) of the proposed classification framework are 99.04%, 98.11%, and 96.42% for AVIRIS–Salinas, ROSIS–Pavia University, and AVIRIS–Indian Pines images, respectively. Since the proposed method follows a combination of BF and the SVM formulations, it will be quite simple and practical to implement in real applications.

Discrete Wavelet Transform Approach for the Estimation of Crop Residue Mass From Spectral Reflectance

Estimation of crop residue mass (CRM) using cellulose absorption index (CAI) from spectral reflectance data is a widely used approach in crop residue management. A specific limitation with the CAI approach is its inefficacy to predict CRM at high residue loadings and its failure to account for the overlapping of residue fragments on soil surface. In this study, we used a combination of discrete wavelet transform (DWT) and partial least square regression (PLSR) to estimate CRM of rice, wheat, maize, sugarcane and soybean. We followed a wavelet packet approach to select appropriate DWT coefficients by examining the variance (referred to as DWTv-PLSR) and correlation (referred to as DWTc-PLSR) structure of the multi-resolution DWT coefficients. Results showed that the DWTc-PLSR approach yielded excellent predictability regardless of crop residue types. An interesting observation of this study is that the wavelet-based approaches showed significant spectral features in the visible and NIR range in contrast to the commonly used SWIR (2100 nm) range representing the CAI. Spectral reflectance curves in our study and those reported in the literature clearly show that both the depth and width of cellulose absorption peaks generally do not vary much with the residue mass. Such lack of sensitivity may have been portrayed in the DWTc-PLSR approach and this method appears to overcome the limitations of using CAI for crop residue assessment.

Modeling distributions of water and dielectric constants around land mines in homogeneous soils

Many sensors for landmine detection are affected by soil water content, temperature, electrical conductivity and dielectric constant. The most important of these is water content since it directly influences the three other properties. We model water distribution around antitank mines buried in a loam and loamy sand soil under the climatic conditions of Bosnia and Kuwait. In Kuwait the loam and loamy sand have mean soil water contents of about 16 and 7 volume percent, respectively; in Bosnia, the mane water contents are higher with means of 30 and 14 volume percent in the loam and loamy sand. As a result the soil dielectric constant in Kuwait varied from about 4 to 8 in the loamy sand and from 8 to 14 in the loam. In Bosnia the higher water contents result in a soil dielectric constant from 4 to 12 in the loamy sand and from 9 to 50 in the loam. Water contents below the landmine were sometimes higher than above it. The modeling result demonstrate that a solid water content regimes and the resulting distributions of soil dielectric constants around landmines are strongly affected by the interaction between climate, soil type, and landmine geometry.

An Ensemble Modeling Approach for Estimating Diffusive Tortuosity for Saturated Soils From Porosity

ABSTRACT Apparent diffusion constants in soil are generally estimated by dividing molecular diffusion coefficient for a solute with soil tortuosity (&tgr;) values. Several models have been proposed to estimate &tgr; from soil porosity (ϕ) alone, but most of these models fail when the variability in observed &tgr;-ϕ pairs increases. Pedotransfer functions can be used to predict &tgr; from easy-to-measure soil properties such soil texture, organic carbon contents, and ϕ, but such an approach requires more measurements to be performed than just measuring ϕ. Here, we show that &tgr; may be estimated from ϕ alone using the ensemble averaging approach. We examined seven different analytical expressions for &tgr;-ϕ and seven different ensemble-modeling approaches to estimate &tgr; for 100 pairs of &tgr;-ϕ collected from a wide geographical area. Modeling results showed that the Bayesian model averaging method was the best ensemble-modeling approach for estimating &tgr; from ϕ. Of 119 different combinations of &tgr; (ϕ) models, three models derived considering (1) packing of square-shaped particles, (2) fractal geometry with particles of different sizes, and (3) percolation theory were identified as the best individual models for ensemble modeling. The coefficient of determination (0.67), root-mean-squared error (0.23), and the Akaike information criterion (94.37) values for this ensemble model were better than those when a single model was used for prediction. Inclusion of these three models that are based on both fractal and regular geometrical shapes for particles of different sizes may be a reason for improved performance of the ensemble approach. These results suggest that &tgr; may be estimated from ϕ using the ensemble approach without the need for additional soil data, as is done in a pedotransfer function approach.

Digital Soil Mapping and Best Management of Soil Resources: A Brief Discussion with Few Case Studies

Soil plays a key role in agricultural production system by supporting plant growth as well as in hydrological cycle by partitioning rainwater into runoff and infiltration. Therefore, knowledge on soil properties helps in better management of both soil and water resources for sustainable crop production. However, soils vary largely in space and therefore characterizing it for a particular landscape with a set of soil parameters is a difficult task. Often, there is need to collect multiple soil samples from a landscape for characterization purpose in order to minimize the spatial variation effect and is not always feasible. Most of the times, a homogeneous zone is assumed with similar soil properties to eliminate the variation effect. Soil mapping helps in characterizing the soil resources in a better way and recently introduced digital soil mapping approach is more appropriate for this purpose. In this approach, spatial variation of soil properties and its relation with other landscape and environment variables in the form of ‘scorpan’ factors are considered while mapping soil properties in a spatial domain. Mathematical models are also established between soil properties and environment variables exploiting the available legacy soil data and hugely available digital data on earth features in recent times. Hyperspectral soil signatures have also a potential role to improve the digital soil products further. In this chapter, we discuss the basics of digital soil mapping approach and its needs, semivariogram fitting, kriging and its variations, accuracy and uncertainty of digital maps, role of pedotransfer (PTF) and spectrotransfer (STF) models in digital soil mapping, future prospect of hyperspectral signatures in mapping soil properties and few cases studies on digital soil mapping. Finally, it is expected that digital soil maps are available in different IT platforms, e.g. internet, desktop computer, mobile apps, webGIS platform, etc., to make them useful to end users.