Special Issue "Advanced Technologies and Methods for Soil Water Monitoring and Management"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Jingyi Huang
E-Mail Website
Guest Editor
Department of Soil Science, University of Wisconsin–Madison, Madison, WI 53706-1299, USA
Interests: soil physics; digital soil mapping; proximal soil sensing; geostatistics; inverse modeling
Dr. Brendan Malone
E-Mail Website
Guest Editor
Agriculture and Food Commonwealth Scientific and Industrial Research Organisation, Bruce E Butler Laboratory, Clunies Ross Street, Black Mountain, ACT 2601, Australia
Interests: soil science; digital soil mapping; pedometrics; GIS
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Soil moisture is at the nexus of nexus of food, energy and water systems. The partitioning of precipitation into infiltration, ground water percolation, runoff, and evapotranspiration, as well as the partitioning of heat fluxes at the ground surface are influenced by soil moisture. Soil moisture varies in space and time across scales, and the variations are controlled by meteorological forcing, land cover, topographic features, soil texture, and human activities. To measure, monitor, map, and model soil moisture for enhanced understanding of water and energy cycles across scales and for improved water resource management for global food security under a changing climate and human activities, in situ, proximal, and remote sensing technologies and novel conceptual frameworks have been developed and increasling used in many disciplines, such as agronomy, hydrology, meteorology, ecology, and environmental sciences. This Special Issue collects original research and review articles on the latest advances in technologies and models developed to assist in monitoring, mapping, and modeling soil water dynamics across scales to understand the water cycles and sustainable water resource management.

Dr. Jingyi Huang
Dr. Brendan Malone
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water resource management
  • sensing technologies
  • Internet of Things
  • food security
  • climate change
  • water cycles

Published Papers (3 papers)

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Research

Article
Vertical Electrical Sounding (VES) for Estimation of Hydraulic Parameters in the Porous Aquifer
Water 2021, 13(2), 170; https://doi.org/10.3390/w13020170 - 13 Jan 2021
Viewed by 950
Abstract
Similarities in both water and electric current flows allow the relation of hydraulic and geoelectric parameters of porous aquifers. Based on this assumption and the importance of the hydraulic parameters for groundwater analyses, this study aimed to estimate hydraulic conductivity (K) and transmissivity [...] Read more.
Similarities in both water and electric current flows allow the relation of hydraulic and geoelectric parameters of porous aquifers. Based on this assumption and the importance of the hydraulic parameters for groundwater analyses, this study aimed to estimate hydraulic conductivity (K) and transmissivity (T) with vertical electrical sounding (VES) in the porous aquifer at the experimental farm of the University of Brasilia, Brazil. VES is a geophysical technique that provides electrical resistivity (ρ, Ω m) and thickness (h) of the subsurface layers. The ρ and h aquifer data, associated with lithology, water table level (WTL), and groundwater electrical resistivity (ρw, Ω m), allowed the calculation of complementary geoelectric parameters (formation factor, F, and Dar Zarrouk parameters) and the relation with K and T, determined via slug test. VES data allowed the elaboration of geoelectric models, with mean absolute percentage error (MAPE) below 6% compared to field data, and the identification of the aquifer in each VES station. Significant exponential regression models (R2 > 0.5 and p-value < 0.05) showed the possibility of using geoelectric parameters to estimate hydraulic parameters. This study allowed the verification of the applicability of consolidated models and the identification of appropriate empirical relationships for hydrogeological characterization in the Brazilian tropical porous aquifers. The results of this work, besides the rapid sampling and low cost of performing vertical electrical sounding (VES), may justify the use of this geophysical technique for preliminary porous aquifer characterization, especially in regions absent of or with insufficient monitoring wells. Full article
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Article
Accuracy and Transferability of Artificial Neural Networks in Predicting in Situ Root-Zone Soil Moisture for Various Regions across the Globe
Water 2020, 12(11), 3109; https://doi.org/10.3390/w12113109 - 05 Nov 2020
Viewed by 845
Abstract
This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained [...] Read more.
This paper explores the accuracy in using an artificial neural network (ANN) to estimate root-zone soil moisture (RZSM) at multiple worldwide locations using only in situ surface soil moisture (SSM) as a training dataset. The paper also addresses the transferability of the trained ANN across climatic and soil texture conditions. Data from the International Soil Moisture Network (ISMN) were collected for several networks with variable soil texture and climate classes. Several scaling, feature extraction, and training approaches were tested. An artificial neural network employing rolling averages (ANNRAV) of SSM over 10, 30, and 90 days was developed. The results show that applying a standard scaling (SSCA) to the ANN input features improves the correlation, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) for 52%, 91%, and 87%, respectively, of the tested stations, compared to MinMax scaling (MMSCA). Different training sets are suggested, namely, training on data from all networks, data from one network, or data of all networks excluding one. Based on these trainings, new transferability (TranI) and contribution (ContI) indices are defined. The results show that one network cannot provide the best prediction accuracy if used alone to train the ANN. They also show that the removal of the less contributing networks enhances performance. For example, elimination of the densest network (SCAN) from the training enhances the mean correlation by 20.5% and the mean NSE by 42.5%. This motivates the implementation of a data filtering technique based on the ANN’s performance. A median, max, and min correlation of 0.77, 0.96, and 0.65, respectively, are obtained by the model after data filtering. The performances are also analyzed with respect to the covered climatic regions and soil texture, providing insights into the robustness and limitations of the approach, namely, the need for complementary information in highly evaporative regions. In fact, the ANN using only SSM to predict RZSM has low performance when decoupling between the surface and root zones is observed. The application of ANN to obtain spatialized RZSM will require integrating remote sensing-based surface soil moisture in the future. Full article
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Article
An Empirical Orthogonal Function-Based Approach for Spatially- and Temporally-Extensive Soil Moisture Data Combination
Water 2020, 12(10), 2919; https://doi.org/10.3390/w12102919 - 19 Oct 2020
Viewed by 499
Abstract
Modeling and prediction of soil hydrologic processes require identifying soil moisture spatial-temporal patterns and effective methods allowing the data observations to be used across different spatial and temporal scales. This work presents a methodology for combining spatially- and temporally-extensive soil moisture datasets obtained [...] Read more.
Modeling and prediction of soil hydrologic processes require identifying soil moisture spatial-temporal patterns and effective methods allowing the data observations to be used across different spatial and temporal scales. This work presents a methodology for combining spatially- and temporally-extensive soil moisture datasets obtained in the Shale Hills Critical Zone Observatory (CZO) from 2004 to 2010. The soil moisture was investigated based on Empirical Orthogonal Function (EOF) analysis. The dominant soil moisture patterns were derived and further correlated with the soil-terrain attributes in the study area. The EOF analyses indicated that one or two EOFs of soil moisture could explain 76–89% of data variation. The primary EOF pattern had high values clustered in the valley region and, conversely, low values located in the sloping hills, with a depth-dependent correlation to which curvature, depth to bedrock, and topographic wetness index at the intermediate depths (0.4 m) exhibited the highest contributions. We suggest a novel approach to integrating the spatially-extensive manually measured datasets with the temporally-extensive automatically monitored datasets. Given the data accessibility, the current data merging framework has provided the methodology for the coupling of the mapped and monitored soil moisture datasets, as well as the conceptual coupling of slow and fast pedologic and hydrologic functions. This successful coupling implies that a combination of diverse and extensive moisture data has provided a solution of data use efficiency and, thus, exciting insights into the understanding of hydrological processes at multiple scales. Full article
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