This technical note describes initial investigative efforts to use soil classification data in a manner suitable for producing more accurate soil analogues for specific purposes. Those purposes include improving environmental modeling efforts and predicting complex biogeochemical processes affecting the fate and transport of contaminants and affecting spectral responses. This technical note also documents initial data analysis methods and the data structure and query system; further, this publication discusses the team's next steps. Geomatics is the study of spatial properties, processes, and patterns inherent in existing spatial data. Soils data is therefore a suitable topic for geomatic research-and in fact, pedo-informatics is the hybrid discipline synthesizing information science, soils science, and geography. It is well known that soils mapping is an evolving science, constrained by the complex nature of soils, including geological heterogeneity, climatic and landscape variation, and anthropomorphic effects. These challenges create a ubiquitous and inescapable heterogeneity that confounds precise environmental modeling and prediction systems. Current modeling and geospatial tools cannot predict complex biogeochemical processes, because statistically accurate multivariate soil characteristics datasets do not exist. The lack of such datasets has been a limiting factor in the production of accurate soil analogues for predicting soil properties in austere and expeditionary environments. Here, the authors discuss the foundation upon which an evolutionary data system could yield better soil analogue suggestions over existing methodologies.

We exhibit theoretical and experimental evidence that some surface anomalies can be passively detected from their shadowing of low‐level gamma rays emitted from natural soil. Surface objects on the order of decimeter size with high electron density cause variable attenuation in several bands of gamma ray energies, while some surface holes cause other detectable changes in the gamma background. Using a broadly collimated portable 7.6 × 7.6 cm cylindrical NaI (Tl) scintillation detector, we characterize the small‐scale (on the order of meter scale or less) homogeneity of gamma rays at the soil surface in the context of detectability of objects and holes. We suggest that passive detection of anomalies below the surface may have impractically low count rates due to the greater collimation needed.

To assist US Army Corps of Engineers resource managers in monitoring for cyanobacteria bloom events, a laboratory method using hyperspectral imaging has been developed. This method enables the rapid detection of cyanobacteria in large volumes and has the potential to be transitioned to aerial platforms for field deployment. Prior to field data collection, validation of the technology in the laboratory using monocultures was needed. This report describes the development of the detection method using hyperspectral imaging and the stability/reliability of these signatures for identification purposes. Hyperspectral signatures of different cyanobacteria were compared to evaluate spectral deviations between genera to assess the feasibility of using this imaging method in the field. Algorithms were then developed to spectrally deconvolute mixtures of cyanobacteria to determine relative abundances of each species. Last, laboratory cultures of Microcystis aeruginosa and Anabaena sp. were subjected to varying macro (nitrate and phosphate) and micro-nutrient (iron and magnesium) stressors to establish the stability of signatures within each species. Based on the findings, hyperspectral imaging can be a valuable tool for the detection and monitoring of cyanobacteria. However, it should be used with caution and only during stages of active growth for accurate identification and limited interference owing to stress.

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