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1. Online Soil Classification Using a UAS Sensor Emplacement System

   https://doi.org/10.1007/978303071151116  

   Plowcha, A. ; Hogberg, J. ; Detweiler, C. ; Bradley, J ( March 2021 , International Symposium on Experimental Robotics)

   Siciliano, B. ; Laschi, C. ; Khatib, O. (Ed.)

   Deployment of sensors in hard-to-access locations can improve data gathering for scientific studies. We have developed a sensor emplacement system that can be mounted to unmanned aircraft systems with vertical takeoff and landing capabilities to autonomously auger a sensor into the ground. Various techniques can be chosen to enhance the augering process when certain characteristics of the soil are known. Moisture content and compressive strength are the soil characteristics that most impact the augering process, yet directly measuring them would require additional sensors to an already-burdened airframe. We address this through a novel means of predicting these soil characteristics within the first 30 s of an average 85 s augering evolution using onboard sensors and a Gaussian process regression scheme that predicts the soil moisture content and compressive strength with accuracy of 86.53% and 90.53% of the respective measured values. 

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2. Probabilistic Analysis of In Situ Soil Water Characteristic Curve Using Kernel Density Estimation

   https://doi.org/10.1061/9780784485354.027  

   Alam, Md. Jobair ; Aggarwal, Maalvika ; Rahman, Naima ( February 2024 , American Society of Civil Engineers)

   T. Matthew Evans, Ph.D. Nina Stark (Ed.)

   Soil water characteristic curve (SWCC) which describes the relationship between water content and matric suction is important to analyzing unsaturated soil behavior. Because of the degree of uncertainty in field conditions due to climatic variability and soil heterogeneities, it becomes necessary to probabilistically characterize the SWCC. A satisfactory probabilistic characterization of field-based SWCCs requires a substantial data pair of water content and suction and their distribution characteristics. In this study, the kernel density estimate (KDE) approach was applied to water content and suction data measured from field-installed co-located sensors of a compacted clay bed to (1) determine the modality of water content and suction distribution and their constitutive relationship at variable weather conditions and (2) demonstrate the importance of probabilistic analysis of SWCC. The Gaussian function was used in the KDE analysis. A moisture sensor and soil water potential sensor were juxtaposed at 0.3 m depth of the 3 m × 3 m compacted clay bed to collect the water content and suction data and determine their distribution under the field condition. The density plots of both water content and suction at 0.3 m depth exhibited multimodal distribution due to the uneven distribution of climatic events. The KDE reasonably identified the air entry value, saturated moisture content, and residual moisture content in the field conditions, which were validated with field-based SWCC plots. The study showed that probabilistic analysis better interprets the realistic scenarios of field unsaturated soil behavior. 

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3. Soil Moisture Responses to Rainfall: Implications for Runoff Generation

   https://doi.org/10.1029/2020WR028827  

   Singh, Nitin K. ; Emanuel, Ryan E. ; McGlynn, Brian L. ; Miniat, Chelcy F. ( September 2021 , Water Resources Research)

   Soil moisture is a key control on runoff generation and biogeochemical processes on hillslopes. Precipitation events can evoke different soil moisture responses with depth through the soil profile, and responses can differ among landscape positions along a hillslope. We sought to elucidate the nature of these responses by estimating changes in water content, response time between peak precipitation and peak soil moisture, and wetting front velocities for 43 storms at 45 locations on three adjacent hillslopes within a headwater catchment of the southern Appalachian Mountains (NC, USA). We used a multivariate modeling approach to quantify the relative influences and the predictability of soil moisture responses by a combination of landscape and storm characteristics. We quantified the lag correlations between hillslope mean soil moisture and catchment runoff to demonstrate how storm properties and hillslope‐scale characteristics may influence runoff at the catchment outlet. Soil moisture responses varied widely, and no consistent patterns were observed among response metrics laterally or vertically along hillslopes. In contrast to other studies, we found that the relative influence of hillslope properties and storm characteristics varied with soil moisture responses and during storms. Antecedent conditions and storm depths influenced the strength of lag correlations between soil moisture and runoff, whereas storm mean intensity was correlated with the lag times. These results highlight the utility of intensive observations for characterizing heterogeneity in soil moisture responses, suggesting, among other things, a need for better representation of the subsurface processes in rainfall‐runoff models. Identifying the relative importance of drivers can be beneficial in building parsimonious hydrological models.

    

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4. Classifying Soil Saturation Levels Using a Network of UAV-Deployed Smart Penetrometers

   https://doi.org/10.1115/smasis2023-111009  

   Chowdhury, Puja ; Satme, Joud N ; Yount, Ryan ; Downey, Austin_R J ; Khan, Sadik ; Imran, Jasim ; Micheli, Laura ( September 2023 , American Society of Mechanical Engineers)

   Levees are built to safeguard human lives, essential infrastructure, and farmland. However, failure of levees can have catastrophic impacts due to a fast rate of inundation in areas protected by levees. Earthen levees are prone to failure due to excessive moisture content that reduces the shear strength of the soil. The use of levee monitoring systems has demonstrated the ability to reduce the likelihood of failure by creating maps that depict the saturation levels of the surface of the levee, both in terms of space and time. By utilizing extensive sensor networks to continuously monitor these geo-infrastructure systems, the structural deterioration attributed to changing climate can be studied. Measuring environmental parameters surrounding such structures provides insight into the potential stressors that cause structural failure. Steps can then be taken to mitigate those effects on the levees and maintain structural integrity. However, the massive scale of levees makes it difficult to monitor with conventional wired sensors. This paper presents a preliminary investigation into the development and validation of UAV-deployable smart sensing spikes for soil conductivity levels in levees, which is a measurement modality for determining soil saturation levels. For this work, Gaussian process regression (also known as kriging) is used to model the soil saturation levels between sensing spikes obtaining a continuous moisture map of the levees. The expanded data is then categorized using a clustering-based machine learning approach with conductivity data from sensing spikes as model inputs. The machine learning model output is sorted into three categories: dry, partially saturated, and saturated soil. The findings of a laboratory study are presented, and the implications of the raw and expanded data are discussed. This work will aid in predicting potential levee failure risks and maintenance requirements based on the analysis of the soil conditions using a network of smart sensing spikes.

    

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5. Characterization of a Deglaciated Sediment Chronosequence in the High Arctic Using Near‐Surface Geoelectrical Monitoring Methods

   https://doi.org/10.1002/ppp.2220  

   Cimpoiasu, Mihai O. ; Kuras, Oliver ; Harrison, Harry ; Wilkinson, Paul B. ; Meldrum, Philip ; Chambers, Jonathan E. ; Liljestrand, Dane ; Oroza, Carlos ; Schmidt, Steven K. ; Sommers, Pacifica ; et al ( March 2024 , Permafrost and Periglacial Processes)

   Mauro Guglielmin (Ed.)

   Accelerated climate warming is causing significant reductions in the volume of Arctic glaciers, such that previously ice‐capped bare ground is uncovered, harboring soil development. Monitoring the thermal and hydrologic characteristics of soils, which strongly affect microbial activity, is important to understand the evolution of emerging terrestrial landscapes. We instrumented two sites on the forefield of a retreating Svalbard glacier, representing sediment ages of approximately 5 and 60 years since exposure. Our instrumentation included an ERT array complemented by adjacent point sensor measurements of subsurface temperature and water content. Sediments were sampled at each location and at two more additional sites (120 and 2000 years old) along a chronosequence aligned with the direction of glacial retreat. Analysis suggests older sediments have a lower bulk density and contain fewer large minerals, which we interpret to be indicative of sediment reworking over time. Two months of monitoring data recorded during summer 2021 indicate that the 60‐year‐old sediments are stratified showing more spatially consistent changes in electrical resistivity, whereas the younger sediments show a more irregular structure, with consequences on heat and moisture conductibility. Furthermore, our sensors reveal that young sediments have a higher moisture content, but a lower moisture content variability.

    

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