Heterogeneity Of Subsoil Research Articles

Pattern Extraction of Topsoil and Subsoil Heterogeneity and Soil‐Crop Interaction Using Unsupervised Bayesian Machine Learning: An Application to Satellite‐Derived NDVI Time Series and Electromagnetic Induction Measurements

AbstractThe link between remotely sensed surface vegetation performances with the heterogeneity of subsurface physical properties is investigated by means of a Bayesian unsupervised learning approach. This question has considerable relevance and practical implications for precision agriculture as visible spatial differences in crop development and yield are often directly related to horizontal and vertical variations in soil texture caused by, for example, complex deposition/erosion processes. In addition, active and relict geomorphological settings, such as floodplains and buried paleochannels, can cast significant complexity into surface hydrology and crop modeling. This also requires a better approach to detect, quantify, and analyze topsoil and subsoil heterogeneity and soil‐crop interaction. In this work, we introduce a novel unsupervised Bayesian pattern recognition framework to address the extraction of these complex patterns. The proposed approach is first validated using two synthetic data sets and then applied to real‐world data sets of three test fields, which consists of satellite‐derived normalized difference vegetation index (NDVI) time series and proximal soil measurement data acquired by a multireceiver electromagnetic induction geophysical system. We show, for the first time, how the similarity and joint spatial patterns between crop NDVI time series and soil electromagnetic induction information can be extracted in a statistically rigorous means, and the associated heterogeneity and correlation can be analyzed in a quantitative manner. Some preliminary results from this study improve our understanding the link of above surface crop performance with the heterogeneous subsurface. Additional investigations have been planned for further testing the validity and generalization of these findings.

Passive seismic prospecting in Venice historical center for impedance contrast mapping

Extensive passive seismic prospecting has been conducted in the historical center of Venice (North Italy) aimed to map subsoil impedance contrast. Venice represents an emblematic study site of the challenging characteristics that can be encountered in historical sites, both stratigraphic and logistical, limiting the application of common microzonation techniques. A total of 82 single station passive HVSR acquisitions and passive surface wave arrays analysis have been performed to estimate the main impedance contrasts of the first subsoil, capable of interaction in terms of resonance with the monumental buildings. The results of the geophysical analysis have been integrated with available stratigraphic information derived from published researches and from hundreds of boreholes stored in an institutional database. The results have been then analyzed by means of a geostatistical approach based on cokriging interpolation, which permitted to map the impedance contrast beneath the ancient historical center. The adopted interpolation algorithm permitted to map the impedance contrast with an integrated use of existing information based on HVSR acquisitions and stratigraphic data. The map of impedance contrast is congruent with the literature geo-engineering evidences such as subsidence and geological reconstructions. The achieved results open new perspectives for the use and analysis of passive seismic techniques in challenging environments as historical urban centers characterized by high subsoil heterogeneity. Moreover, an improved knowledge of the shallow stratigraphy would permit to shed light on the complex interactions between anthropic activities and geoenvironmental processes, including climate change that characterized the long history of Venice.

Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements

Patterns in crop development and yield are often directly related to lateral and vertical changes in soil texture causing changes in available water and resource supply for plant growth, especially under dry conditions. Relict geomorphologic features, such as old river channels covered by shallow sediments can challenge assumptions of uniformity in precision agriculture, subsurface hydrology, and crop modeling. Hence a better detection of these subsurface structures is of great interest. In this study, the origins of narrow and undulating leaf area index (LAI) patterns showing better crop performance in large scale multi-temporal satellite imagery were for the first time interpreted by proximal soil sensor data. A multi-receiver electromagnetic induction (EMI) sensor measuring soil apparent electrical conductivity (ECa) for six depths of exploration (DOE) ranging from 0–0.25 to 0–1.9m was used as reconnaissance soil survey tool in combination with selected electrical resistivity tomography (ERT) transects, and ground truth texture data to investigate lateral and vertical changes of soil properties at ten arable fields. The moderate to excellent spatial consistency (R2 0.19–0.82) of ECa patterns and LAI crop marks that indicate a higher water storage capacity as well as the increased correlations between large-offset ECa data and the subsoil clay content and soil profile depth, implies that along this buried paleo-river structure the subsoil is mainly responsible for better crop development in drought periods. Furthermore, observed stagnant water in the subsoil indicates that this paleo-river structure still plays an important role in subsurface hydrology. These insights should be considered and implemented in local hydrological as well as crop models.

Characterization of subsoil heterogeneity, estimation of grain size distribution and hydraulic conductivity at the Krauthausen test site using Cone Penetration Test

A Cone Penetration Test (CPT) survey with a high spatial resolution was performed in order to investigate the stratigraphy as well as the spatial variability of various soil properties of the Krauthausen test site. Analyses of the CPT measurements showed the subsurface to be dominated by a planar layered structure. Variogram analysis of the various CPT parameters disclosed that within each layer the soil properties have an anisotropic spatial correlation structure. A correlation analysis of the measured CPT data and co-located grain size distributions from soil samples was performed. Since the correlation coefficients were greater equal to 0.7, a reliable empirical relationship between the data sets could be developed. Based on this empirical relationship grain size distributions were estimated at CPT locations. The statistical processing of estimated and measured grain size distributions with respect to their spatial correlation structure disclosed good agreement between the data sets. The estimated grain size distributions from CPT data were used to estimate the hydraulic conductivity in the aquifer. The results provide detailed information of the spatial heterogeneity of the hydraulic conductivity at Krauthausen test site. The validation of these results, using a prior investigation of hydraulic conductivity statistics, suggests the CPT a fast and inexpensive tool for the estimation of three dimensional hydraulic conductivity fields with sufficient accuracy.