Journal Paper Digests

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Journal Paper Digests 2018 #2

  • Global patterns of nitrate storage in the vadose zone
  • Identifying scale-specific controls of soil organic matter distribution in mountain areas using anisotropy analysis and discrete wavelet transform
  • Scale-location specific soil spatial variability: A comparison of continuous wavelet transform and Hilbert-Huang transform
  • Stochastic hydro-mechanical stability of vegetated slopes: An integrated copula based framework
  • Plant exudates may stabilize or weaken soil depending on species, origin and time
  • Multi-year simulation and model calibration of soil moisture and temperature profiles in till soil
  • rs-local data-mines information from spectral libraries to improve local calibrations
  • Model updating for the classification of different varieties of maize seeds from different years by hyperspectral imaging coupled with a pre-labeling method
  • Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning
  • Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships

Global patterns of nitrate storage in the vadose zone

Authors: Ascott, MJ; Gooddy, DC; Wang, L; Stuart, ME; Lewis, MA; Ward, RS; Binley, AM

Source: NATURE COMMUNICATIONS, 8 1416-1416; NOV 10 2017

Abstract: Global-scale nitrogen budgets developed to quantify anthropogenic impacts on the nitrogen cycle do not explicitly consider nitrate stored in the vadose zone. Here we show that the vadose zone is an important store of nitrate that should be considered in future budgets for effective policymaking. Using estimates of groundwater depth and nitrate leaching for 1900-2000, we quantify the peak global storage of nitrate in the vadose zone as 605-1814 Teragrams (Tg). Estimates of nitrate storage are validated using basin-scale and national-scale estimates and observed groundwater nitrate data. Nitrate storage per unit area is greatest in North America, China and Europe where there are thick vadose zones and extensive historical agriculture. In these areas, long travel times in the vadose zone may delay the impact of changes in agricultural practices on groundwater quality. We argue that in these areas use of conventional nitrogen budget approaches is inappropriate.

Identifying scale-specific controls of soil organic matter distribution in mountain areas using anisotropy analysis and discrete wavelet transform

Authors: Guo, YD; Zhao, RY; Zeng, YNA; Shi, Z; Zhou, Q

Source: CATENA, 160 1-9; JAN 2018

Abstract: Soil organic matter (SOM) is an important index to evaluate soil fertility. Knowing the spatial distribution of SOM and its controlling factors at different scales is basic to sustainable farmland management. The variability was explored mostly in plain farmlands or at small scales in previous studies. In the present study, combined with anisotropy analysis (AA) and discrete wavelet transform (DWT), we examined the spatial variability of SOM and its controlling factors at various scales in a mountainous area. Transect with dominant directions (major axis and minor axis) of SOM variability was extracted using AA and then the scale-specific variability was examined using DWT. Dominant factors of SOM variability at different scales were identified using correlation coefficients between SOM at different scales and various soil environmental factors. The results showed that the major axis along which SOM varied the most was 24 south by west, consistent with the strike of Wuling Mountains. The minor axis was perpendicular to the major axis direction. DWT separated the SOM variations into nine scale components (eight details, D1 through D8, and one approximation, A8) along the major axis and into eight scale components (seven details, Dl through D7, and one approximation, A7) along minor axis. The largest-scale component (A8 in major axis and A7 in minor axis) explained the most variance of SOM along both axes, accounting for half of the total variance. Compared with the original SOM before separation of scale components (undecomposed SOM), the scale components showed significant correlation with environmental factors. Both elevation and mean annual precipitation had positive correlation with SOM at large scales. However, there was a negative correlation between SOM and mean annual temperature. This indicates that the topography and local climate may have a stronger influence in controlling SOM spatial distribution in mountain regions. The relationship provides important information on environmental covariate selection in mapping soil resource. The combination of AA and DWT shows promise quantifying SOM spatial distribution and its control factors at different scales in mountainous areas.

Scale-location specific soil spatial variability: A comparison of continuous wavelet transform and Hilbert-Huang transform

Authors: Biswas, A

Source: CATENA, 160 24-31; JAN 2018

Abstract: Soil spatial variability has become the rule rather than the exception; it is the consequence of spatial dependence, periodicity, nonstationarity, and nonlinearity. The continuous wavelet transform (CWT) has been extremely useful in revealing scale-and location-specific information of nonstationary soil spatial variation. The Hilbert Huang transform (HHT) has also been used in soil science to reveal scales and locations of variations in soil properties. These variations may be controlled by the underlying soil processes that can also be represented using a linear or nonlinear equation/function. The objective of this manuscript was to compare the inherent strengths and weaknesses of CWT and HHT in quantifying scale-and location-specific soil spatial variation. Examples using four simulated spatial series (stationary linear, stationary nonlinear, nonstationary linear, and nonstationary nonlinear) and two real world measurements of soil properties (organic carbon and soil water storage) were used to compare the methods. With its algorithmic basis, HHT identified the scale components present in the spatial series more flexibly, while the redundancy in CWT identified a diffuse band of scales as it is based on an underlying mathematical model. Additionally, the CWT identified variations that were biased towards large scales. The HHT used a more flexible basis for interpreting real data and could deal with nonlinear issues, while CWT could not. A similar result was also observed for soil organic carbon and soil water storage. Both methods could produce certain levels of information but the choice should be made based on the type of information that is required while taking into consideration the underlying assumptions. For example, to quantify the scale-and location-specific spatial variability of soil properties as controlled by soil processes which can be represented by a nonlinear equation, one achieves benefits from using HHT rather than CWT. In this case study, HHT showed superior performance in identifying scales and locations of soil spatial variability over CWT. In this study, HHT is compared with CWT only and needs further comparison with other types of wavelet analysis.

Stochastic hydro-mechanical stability of vegetated slopes: An integrated copula based framework

Authors: Das, GK; Hazra, B; Garg, A; Ng, CWW

Source: CATENA, 160 124-133; JAN 2018

Abstract: Vegetation induces considerable uncertainties in the hydrological (suction, psi) and mechanical (cohesion, c and frictional angle, phi) parameters of soil, due to which, it is essential that the stability of vegetated slope is evaluated in a probabilistic framework. Moreover, from previous studies, it has been found that the mechanical parameters of soil share inherent correlation, which has a profound effect on slope stability. The combined effect of stochastic hydro-mechanical parameters is not well studied, more so in vegetated slopes. This study demonstrates a probabilistic approach to analyse the stability of vegetated slopes, under the combined effect of univariate suction and bivariate c - phi. Data corresponding to suction and the mechanical parameters, are obtained from a field monitoring programme, conducted on a homogeneously compacted vegetated slope (adopted from previous literature). The suction responses are probabilistically evaluated by estimating their probability distribution functions, and the dependence structure of c and phi is established via copula theory. Treed slopes are found to be more stable than grassed and bare (i.e. sparsely vegetated) slopes, since suction induced in treed soil is relatively higher. The probability of failure for vegetated slopes decreases substantially with increase in magnitude of c - phi correlation, thereby yielding more conservative estimates than the uncorrelated case.

Plant exudates may stabilize or weaken soil depending on species, origin and time

Authors: Naveed, M; Brown, LK; Raffan, AC; George, TS; Bengough, AG; Roose, T; Sinclair, I; Koebernick, N; Cooper, L; Hackett, CA; Hallett, PD

Source: EUROPEAN JOURNAL OF SOIL SCIENCE, 68 (6):806-816; NOV 2017

Abstract: We hypothesized that plant exudates could either gel or disperse soil depending on their chemical characteristics. Barley (Hordeum vulgare L. cv. Optic) and maize (Zea mays L. cv. Freya) root exudates were collected using an aerated hydroponic method and compared with chia (Salvia hispanica L.) seed exudate, a commonly used root exudate analogue. Sandy loam soil was passed through a 500-m mesh and treated with each exudate at a concentration of 4.6 mg exudate g(-1) dry soil. Two sets of soil samples were prepared. One set of treated soil samples was maintained at 4 degrees C to suppress microbial processes. To characterize the effect of decomposition, the second set of samples was incubated at 16 degrees C for 2 weeks at -30 kPa matric potential. Gas chromatography-mass spectrometry (GC-MS) analysis of the exudates showed that barley had the largest organic acid content and chia the largest content of sugars (polysaccharide-derived or free), and maize was in between barley and chia. Yield stress of amended soil samples was measured by an oscillatory strain sweep test with a cone plate rheometer. When microbial decomposition was suppressed at 4 degrees C, yield stress increased 20-fold for chia seed exudate and twofold for maize root exudate compared with the control, whereas for barley root exudate decreased to half. The yield stress after 2 weeks of incubation compared with soil with suppressed microbial decomposition increased by 85% for barley root exudate, but for chia and maize it decreased by 87 and 54%, respectively. Barley root exudation might therefore disperse soil and this could facilitate nutrient release. The maize root and chia seed exudates gelled soil, which could create a more stable soil structure around roots or seeds.Highlights Rheological measurements quantified physical behaviour of plant exudates and effect on soil stabilization. Barley root exudates dispersed soil, which could release nutrients and carbon. Maize root and chia seed exudates had a stabilizing effect on soil. Physical engineering of soil in contact with plant roots depends on the nature and origin of exudates.

Multi-year simulation and model calibration of soil moisture and temperature profiles in till soil

Authors: Okkonen, J; Ala-Aho, P; Hanninen, P; Hayashi, M; Sutinen, R; Liwata, P

Source: EUROPEAN JOURNAL OF SOIL SCIENCE, 68 (6):829-839; NOV 2017

Abstract: In Nordic regions water infiltration into soil is controlled by soil moisture content and frozen soil conditions, which are regulated by soil temperature. For long-term model predictions of the effects of climate change, models need to be tested with long-term data to assess model sensitivity to parameter uncertainties under both typical and exceptional conditions. Ten-year (2002-2011) daily soil moisture and temperature data at different depths in glacial till soils in central Finland were used to assess the sensitivity of a coupled heat and water transfer model, COUP, to model parameters. The model was most sensitive to the parameters controlling snow accumulation and melt, the thermal conductivity of frozen soil and soil water retention characteristics. Observed time series for soil temperature and moisture at different depths were matched reasonably well by model simulations, although the model performance with respect to moisture dynamics in the topsoil was relatively poor. The model was not able to simulate accurately exceptional winter conditions, such as mid-winter snowmelt events. This study showed that the main characteristics of long-term variation in soil temperature for till-derived soil in a cold climate can be resolved by a coupled water and heat transport model. Better characterization of infiltration in cold climates would require measurement of water fluxes, and soil frost occurrence and penetration.Highlights Ten-year soil temperature and moisture observations are predicted with coupled heat and water model. Snow processes and soil thermal and water retention properties proved critical in our simulations. Exceptional winter conditions pose a challenge in parameterization of the model. Studies measuring water fluxes and soil frost occurrence are needed for advances in modelling.

rs-local data-mines information from spectral libraries to improve local calibrations

Authors: Lobsey, CR; Viscarra Rossel, RA; Roudier, P; Hedley, CB

Source: EUROPEAN JOURNAL OF SOIL SCIENCE, 68 (6):840-852; NOV 2017

Abstract: Diffuse reflectance spectroscopy in the visible-near infrared (vis-NIR) and mid infrared (mid-IR) can be used to estimate soil properties, such as organic carbon (C) content. Compared with conventional laboratory methods, it enables practical and inexpensive measurements at finer spatial and temporal resolutions, which are needed to improve the assessment and management of soil and the environment. Measurements of soil properties with spectra require empirical calibration and soil spectral libraries (SSL) have been developed for this purpose at the regional, continental and global scales. Calibrations derived with these SSLs, however, are often shown to predict poorly at local sites. Here we present a new method, rs-local, that uses a small representative set of site-specific (or local’) data and re-sampling techniques to select a subset of data from a large vis-NIR SSL to improve calibrations at the site. We demonstrate the implementation of rs-local by estimating soil organic C in two fields with different soil types, one in Australia and one in New Zealand. We found that with as few as 12 to 20 site-specific samples and the SSL, training datasets derived with rs-local could accurately predict soil organic C concentrations. Predictions with the rs-local data were comparable to, or better than those made with site-specific calibrations with up to 300 samples. Our method outperformed other published local’ spectroscopic techniques that we tested. Thus, rs-local can effectively improve both the accuracy and financial viability of soil spectroscopy.Highlights We describe a new algorithm (rs-local) for site-specific calibration using existing spectral libraries. rs-local is a data driven method that makes no assumptions on spectral or sample similarities. rs-local improved the accuracy of soil organic carbon estimates using spectroscopy. rs-local improves the economic viability of soil spectroscopy.

Model updating for the classification of different varieties of maize seeds from different years by hyperspectral imaging coupled with a pre-labeling method

Authors: Guo, DS; Zhu, QB; Huang, M; Guo, Y; Qin, JW

Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 142 1-8; A NOV 2017

Abstract: The use of hyperspectral imaging technology combined with chemometrics is an effective nondestructive method for sorting seed varieties. However, the performance of the method is susceptible to the influence of time and depends on the training set used in the modeling process. The accuracy of classification models maybe deteriorate when they are used to differentiate the same variety of seeds harvested in different years, due to new variances in the test set are introduced by changes in the cultivation conditions, soil environmental conditions and climatic changes from one year to another. To maintain the accuracy and robustness of model, a model-updating algorithm for differentiating maize seed varieties from different years based on hyperspectral imaging coupled with a pre-labeling method was proposed in this work. The pre-label of each unlabeled sample was obtained using the original classification models developed by the least squares support vector machine classifier. The representative unlabeled samples, which had reliable pre-labels, were selected for updating classification models based on Pearson correlation coefficients. After model updating, the average classification accuracies were improved by 8.9%, 35.8% and 9.6%, compared with those of non-updated models for three test sets, respectively. This shows the effectiveness of the proposed method for classifying maize seeds of different years.

Value of dimensionality reduction for crop differentiation with multi-temporal imagery and machine learning

Authors: Gilbertson, JK; van Niekerk, A

Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 142 50-58; A NOV 2017

Abstract: This study evaluates the use of automated and manual feature selection - prior to machine learning - for the differentiation of crops in a Mediterranean climate (Western Cape, South Africa). Five Landsat-8 images covering the different crop class phenological stages were acquired and used to generate a range of spectral and textural features within an object-based image analysis (OBIA) paradigm. The features were used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) supervised classifiers. Testing was done by performing classifications (using all spatial variables) and then incrementally reducing the feature counts (based on importance allocated to features by filters), feature extraction, and manual (semantic) feature selection. Classification and regression trees (CART) and RF were used as methods to filter feature selection. Feature-extraction methods employed include principal components analysis (PCA) and Tasselled cap transformation (TCT). The classification results were analysed by comparing the overall accuracies and kappa coefficients of each scenario, while McNemar’s test was used to assess the statistical significance of differences in accuracies among classifiers. Feature selection was found to improve the overall accuracies of the DT, k-NN, and RF classifications, but reduced the accuracy of SVM. The results showed that SVM with feature extraction (PCA) on individual image dates produced the most accurate classification (96.2%). Semantic groupings of features for classification also revealed that using the image bands and indices is not sufficient for crop classification, and that additional features are needed. The accuracy differences of the classifiers were, however, not statistically significant, which suggests that, although dimensionality reduction can improve crop differentiation when multi-temporal Landsat-8 imagery is used, it had a marginal effect on the results. For operational crop-type classification in the study area (and similar regions), we conclude that the SVM algorithm can be applied to the full set of features generated.

Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships

Authors: Vincent, S; Lemercier, B; Berthier, L; Walter, C

Source: GEODERMA, 311 130-142; FEB 1 2018

Abstract: Digital soil mapping is becoming a powerful tool to increase the spatial detail of soil information over large areas, which is essential to address agronomic and environmental issues. When it exists, information about soil is often sparse or available at a coarser resolution than required. The spatial distribution of soil at the regional scale is usually represented as a set of polygons defining Soil Map Units (SMUs), each including several Soil Type Units (STUs), which are not spatially delineated but semantically described in a database. Delineation of STUs within SMUs, i.e. spatial disaggregation of SMU, should improve the precision of soil information derived from legacy and ancillary data. The aim of this study was to predict STUs by spatially disaggregating SMUs through a decision-tree approach that considered expert knowledge about soil-landscape relationships embedded in soil databases. In a 27,376 km(2) study area in north-western France (Brittany), 434 SMUs were delineated at 1:250,000 scale, and 320 STUs, their relative area in the SMUs, and their geomorphological and geological contexts were described. A calibration dataset of points was established using stratified random sampling,(n = 352,188). To retrieve soil-landscape relationships, expert rules for soil distribution defined by soil surveyors and based on topography, parent material and waterlogging index were considered in order to allocate an STU to 83% of the calibration dataset. The calibration dataset and covariates (i.e. pedological, geological and terrain attributes; land use; airborne gamma-ray spectrometry) were then used to: build and extrapolate the decision tree using the C.5 algorithm in DSMART software. Several iterations were performed, providing a probability of occurrence of each possible STU within the study area. External validation was perforined by comparing predictions of the disaggregation procedure to available soil maps at scales of 1:25,000 or 1:50,000 and observed profiles. Overall accuracies ranged from 41 to 72%, depending on the validation method.(per pixel vs. 3 x 3 windows of pixels, per STU vs. STU grouped by semantic proximity (n = 204)). Introducing expert rules based on soil-landscape relationships to allocate STUs to calibration samples enabled production of a soil”map with clear spatial structures, yielding expected spatial patterns of soil organisation. Future work notably concerns estimating soil properties at multiple depths deriving from STU predictions, according to the GlobalSoilMap project

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