Journal Paper Digests

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Journal Paper Digests 2018 #4

  • A review of laser-induced breakdown spectroscopy signal enhancement
  • Review of recent UV-Vis and infrared spectroscopy researches on wine detection and discrimination
  • Analysis of spatial data with a nested correlation structure
  • Generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data
  • Dynamic Bayesian network inferencing for non-homogeneous complex systems
  • CubeSats in Hydrology: Ultrahigh-Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation
  • Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps
  • A Field-Scale Sensor Network Data Set for Monitoring and Modeling the Spatial and Temporal Variation of Soil Water Content in a Dryland Agricultural Field
  • Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984-2014)
  • ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions

A review of laser-induced breakdown spectroscopy signal enhancement

Authors: Li, YC; Tian, D; Ding, Y; Yang, G; Liu, K; Wang, CH; Han, X

Source: APPLIED SPECTROSCOPY REVIEWS, 53 (1):1-35; 2018

Abstract: A review of the methods of signal enhancement in laser-induced breakdown spectroscopy (LIBS) is presented. Conventional LIBS suffers from disadvantages of low sensitivity and high limits of detection compared with other analytical techniques, such as inductively coupled plasma mass spectrometry. During the last two decades, various methods have been applied to LIBS in order to realize highly quantitative and qualitative analysis. Current approaches include double-pulse excitation, spatial or magnetic confinement, spark discharge, etc. Different configurations of experimental setups and conditions are suggested for the realization of these improved techniques, while various parameters influence significantly on the enhancement effect. With the aim to study the laser ablation process and characterize the effectiveness of each method, several parameters such as plasma volume and emission intensity are reviewed. Several suggestions are proposed to explain the mechanism of each enhancement method. These modified techniques have been applied on various materials and fields.

Review of recent UV-Vis and infrared spectroscopy researches on wine detection and discrimination

Authors: Yu, J; Wang, H; Zhan, JC; Huang, WD

Source: APPLIED SPECTROSCOPY REVIEWS, 53 (1):65-86; 2018

Abstract: Wine has become a commodity of significant commercial value, and the demand for high quality wine by consumers has been increasing. Suitable analytical techniques are needed for its quality control. Ultraviolet, Visible, Near-infrared and infrared spectroscopy is by far one of the most important techniques for determining the wine quality, including its components and characterization. This review will overview the available most recent applications of spectroscopic techniques in the past decade for wine quality prediction and discrimination both quantitatively and qualitatively. The fundamental principles of these techniques will be introduced briefly, and some innovative setups/instrumentations will also be illustrated. At last the limitations and prospects of spectroscopic techniques for wine industry will be discussed.

Analysis of spatial data with a nested correlation structure

Authors: Adegboye, OA; Leung, DHY; Wang, YG

Source: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 67 (2):329-354; FEB 2018

Abstract: Spatial statistical analyses are often used to study the link between environmental factors and the incidence of diseases. In modelling spatial data, the existence of spatial correlation between observations must be considered. However, in many situations, the exact form of the spatial correlation is unknown. This paper studies environmental factors that might influence the incidence of malaria in Afghanistan. We assume that spatial correlation may be induced by multiple latent sources. Our method is based on a generalized estimating equation of the marginal mean of disease incidence, as a function of the geographical factors and the spatial correlation. Instead of using one set of generalized estimating equations, we embed a series of generalized estimating equations, each reflecting a particular source of spatial correlation, into a larger system of estimating equations. To estimate the spatial correlation parameters, we set up a supplementary set of estimating equations based on the correlation structures that are induced from the various sources. Simultaneous estimation of the mean and correlation parameters is performed by alternating between the two systems of equations.

Generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data

Authors: de Souza, JB; Reisen, VA; Franco, GC; Ispany, M; Bondon, P; Santos, JM

Source: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 67 (2):453-480; FEB 2018

Abstract: Environmental epidemiological studies of the health effects of air pollution frequently utilize the generalized additive model (GAM) as the standard statistical methodology, considering the ambient air pollutants as explanatory covariates. Although exposure to air pollutants is multi-dimensional, the majority of these studies consider only a single pollutant as a covariate in the GAM model. This model restriction may be because the pollutant variables do not only have serial dependence but also interdependence between themselves. In an attempt to convey a more realistic model, we propose here the hybrid generalized additive model-principal component analysis-vector auto-regressive (GAM-PCA-VAR) model, which is a combination of PCA and GAMs along with a VAR process. The PCA is used to eliminate the multicollinearity between the pollutants whereas the VAR model is used to handle the serial correlation of the data to produce white noise processes as covariates in the GAM. Some theoretical and simulation results of the methodology proposed are discussed, with special attention to the effect of time correlation of the covariates on the PCA and, consequently, on the estimates of the parameters in the GAM and on the relative risk, which is a commonly used statistical quantity to measure the effect of the covariates, especially the pollutants, on population health. As a main motivation to the methodology, a real data set is analysed with the aim of quantifying the association between respiratory disease and air pollution concentrations, especially particulate matter PM10, sulphur dioxide, nitrogen dioxide, carbon monoxide and ozone. The empirical results show that the GAM-PCA-VAR model can remove the auto-correlations from the principal components. In addition, this method produces estimates of the relative risk, for each pollutant, which are not affected by the serial correlation in the data. This, in general, leads to more pronounced values of the estimated risk compared with the standard GAM model, indicating, for this study, an increase of almost 5.4% in the risk of PM10, which is one of the most important pollutants which is usually associated with adverse effects on human health.

Dynamic Bayesian network inferencing for non-homogeneous complex systems

Authors: Wu, PPY; Caley, MJ; Kendrick, GA; McMahon, K; Mengersen, K

Source: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 67 (2):417-434; FEB 2018

Abstract: Dynamic Bayesian networks (DBNs) provide a versatile method for predictive, whole-of-systems modelling to support decision makers in managing natural systems subject to anthropogenic disturbances. However, DBNs typically assume a homogeneous Markov chain which we show can limit the dynamics that can be modelled especially for complex ecosystems that are susceptible to regime change (i.e. change in state transition probabilities). Such regime changes can occur as a result of exogenous inputs and/or because of past system states; the latter is known as path dependence. We develop a method for non-homogeneous DBN inference to capture the dynamics of potentially path-dependent ecosystems. The method enables dynamic updates of DBN parameters at each time slice in computing posterior marginal probabilities given evidence for forward inference. An approximate algorithm for forward-backward inference is also provided noting that convergence is not guaranteed in a path-dependent system. We demonstrate the methods on a seagrass dredging case-study and show that the incorporation of path dependence enables conditional absorption into and release from the zero state in line with ecological observations. The model helps managers to develop practical ways to manage the marked effects of dredging on high value seagrass ecosystems.

CubeSats in Hydrology: Ultrahigh-Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation

Authors: McCabe, MF; Aragon, B; Houborg, R; Mascaro, J

Source: WATER RESOURCES RESEARCH, 53 (12):10017-10024; DEC 2017

Abstract: Satellite-based remote sensing has generally necessitated a trade-off between spatial resolution and temporal frequency, affecting the capacity to observe fast hydrological processes and rapidly changing land surface conditions. An avenue for overcoming these spatiotemporal restrictions is the concept of using constellations of satellites, as opposed to the mission focus exemplified by the more conventional space-agency approach to earth observation. Referred to as CubeSats, these platforms offer the potential to provide new insights into a range of earth system variables and processes. Their emergence heralds a paradigm shift from single-sensor launches to an operational approach that envisions tens to hundreds of small, lightweight, and comparatively inexpensive satellites placed into a range of low earth orbits. Although current systems are largely limited to sensing in the optical portion of the electromagnetic spectrum, we demonstrate the opportunity and potential that CubeSats present the hydrological community via the retrieval of vegetation dynamics and terrestrial evaporation and foreshadow future sensing capabilities.

Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

Authors: Underwood, KL; Rizzo, DM; Schroth, AW; Dewoolkar, MM

Source: WATER RESOURCES RESEARCH, 53 (12):10293-10316; DEC 2017

Abstract: Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge-which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

A Field-Scale Sensor Network Data Set for Monitoring and Modeling the Spatial and Temporal Variation of Soil Water Content in a Dryland Agricultural Field

Authors: Gasch, CK; Brown, DJ; Campbell, CS; Cobos, DR; Brooks, ES; Chahal, M; Poggio, M

Source: WATER RESOURCES RESEARCH, 53 (12):10878-10887; DEC 2017

Abstract: We describe a soil water content monitoring data set and auxiliary data collected at a 37 ha experimental no-till farm in the Northwestern United States. Water content measurements have been compiled hourly since 2007 by ECH2O-TE and 5TE sensors installed at 42 locations and five depths (0.3, 0.6, 0.9, 1.2, and 1.5 m, 210 sensors total) across the R. J. Cook Agronomy Farm, a Long-Term Agro-Ecosystem Research Site stationed on complex terrain in a Mediterranean climate. In addition to soil water content readings, the data set includes hourly and daily soil temperature readings, annual crop histories, a digital elevation model, Bt horizon maps, seasonal apparent electrical conductivity, soil texture, and soil bulk density. Meteorological records are also available for this location. We discuss the unique challenges of maintaining the network on an operating farm and demonstrate the nature and complexity of the soil water content data. This data set is accessible online through the National Agriculture Library, has been assigned a DOI, and will be maintained for the long term.

Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984-2014)

Authors: Rogge, D; Bauer, A; Zeidler, J; Mueller, A; Esch, T; Heiden, U

Source: REMOTE SENSING OF ENVIRONMENT, 205 1-17; FEB 2018

Abstract: Soil information with high spatial and temporal resolution is crucial to assess potential soil degradation and to achieve sustainable productivity and ultimately food security. The spatial resolution of existing soil maps can commonly be too coarse to account for local soil variations and owing to the cost and resource needs required to update information these maps lack temporal information. With improved computational processing capabilities, increased data storage and most recently, the increasing amount of freely available data (e.g. Landsat, Sentinel-2A/B), remote sensing imagery can be integrated into existing soil mapping approaches to increase temporal and spatial resolution of soil information. Satellite multi-temporal data allows for generating cloud free, radiometrically and phenologically consistent pixel based image composites of regional scale. Such data sets are of particular use for extracting soil information in areas of intermediate climate where soils are rarely exposed. The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure. The objective of this paper is to demonstrate the automated processors ability to handle large image databases to build multispectral reflectance composite base data layers that can support large scale top soil analyses. The functionality of the SCMaP is demonstrated using Landsat imagery over Germany from 1984 to 2014 applied over 5 year periods. Three primary product levels are generated that will allow for a long term assessment and distribution of soils that include the distribution of exposed soils, a statistical information related to soil use and intensity and the generation of exposed soil reflectance image composites. The resulting composite maps provide useful value-added information on soils with the exposed soil reflectance composites showing high spatial coverage that correlate well with existing soil maps and the underlying geological structural regions.

ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions

Authors: Dorigo, W; Wagner, W; Albergel, C; Albrecht, F; Balsamo, G; Brocca, L; Chung, D; Ertl, M; Forkel, M; Gruber, A; Haas, E; Hamer, PD; Hirschi, M; Ikonen, J; de Jeu, R; Kidd, R; Lahoz, W; Liu, YY; Miralles, D; Mistelbauer, T; Nicolai-Shaw, N; Parinussa, R; Pratola, C; Reimer, C; van der Schalie, R; Seneviratne, SI; Smolander, T; Lecomte, P

Source: REMOTE SENSING OF ENVIRONMENT, 203 185-215; DEC 15 2017

Abstract: Climate Data Records of soil moisture are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. To respond to this need, in 2012 the European Space Agency (ESA) released the first multi-decadal, global satellite-observed soil moisture (SM) dataset as part of its Climate Change Initiative (CO) program. This product, named ESA CCI SM, combines various single-sensor active and passive microwave soil moisture products into three harmonised products: a merged ACTIVE, a merged PASSIVE, and a COMBINED active + passive microwave product. Compared to the first product release, the latest version of ESA CCI SM includes a large number of enhancements, incorporates various new satellite sensors, and extends its temporal coverage to the period 1978-2015. In this study, we first provide a comprehensive overview of the characteristics, evolution, and performance of the ESA CCI SM products. Based on original research and a review of existing literature we show that the product quality has steadily increased with each successive release and that the merged products generally outperform the single-sensor input products. Although ESA CCI SM generally agrees well with the spatial and temporal patterns estimated by land surface models and observed in-situ, we identify surface conditions (e.g., dense vegetation, organic soils) for which it still has large uncertainties. Second, capitalising on the results of >100 research studies that made use of the ESA CCI SM data we provide a synopsis of how it has contributed to improved process understanding in the following Earth system domains: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology. While in some disciplines the use of ESA CCI SM is already widespread (e.g. in the evaluation of model soil moisture states) in others (e.g. in numerical weather prediction or flood forecasting) it is still in its infancy. The latter is partly related to current shortcomings of the product, e.g., the lack of near-real-time availability and data gaps in time and space. This study discloses the discrepancies between current ESA CCI SM product characteristics and the preferred characteristics of long-term satellite soil moisture products as outlined by the Global Climate Observing System (GCOS), and provides important directions for future ESA CCI SM product improvements to bridge these gaps. (C) 2017 Elsevier Inc. All rights reserved.

Journal Paper Digests

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