Journal Paper Digests

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Journal Paper Digests 2019 #3

  • The minimum level for soil allocation using topsoil reflectance spectra: Genus or species?
  • Deconstructing aeolian landscapes
  • Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy
  • Agricultural landscape evolution and structural connectivity to the river for matter flux, a multi-agents simulation approach
  • Forecasting dryland vegetation condition months in advance through satellite data assimilation
  • Connectivity as an emergent property of geomorphic systems
  • Topographic variation in soil erosion and accumulation determined with meteoric Be-10
  • A mechanical-dielectric-high frequency acoustic sensor fusion for soil physical characterization
  • A new method to analyse the soil movement during tillage operations using a novel digital image processing algorithm
  • Managing for soil carbon sequestration: Let’s get realistic

The minimum level for soil allocation using topsoil reflectance spectra: Genus or species?

Authors: Wang, X; Zhang, XK; Li, HX; Zhang, XL; Liu, HJ; Dou, X; Yu, ZY

Source: CATENA, 174 36-47; MAR 2019

Abstract: Spectroscopy has been used for rapid determination of soil physicochemical parameters and soil classification or allocation, usually at the great group level of soil genetic classification. Soil genetic classification is still the main system of classification used in China, on which the Chinese Soil Classification System is based, and includes order, suborder, great group, subgroup, genus, species, and subspecies. In this paper, we introduce a soil allocation model that uses topsoil spectral characteristics and determined the minimum level for soil allocation. The topsoil spectra of four typical soils from the Songnen Plain in Northeast China were used to extract spectral feature parameters (SFPs) with clear physical meaning for soil allocation at the great group, genus, and species levels, and principal components of reflectance and first-derivative reflectance were used for allocation to determine the optimal input. Multinomial logistic regression MLR, multi-layer percepti on neural network, MLPNN, and decision trees (DT) were used to build allocation models. The results show the following: 1) the first and second absorption valleys of the topsoil spectral curves are substantially different between soils at the great group and genus levels but similar at the species level; 2) the topsoil samples can be allocated with SFPs, for which genus is the minimum level within the Chinese Soil Classification System for soil allocation with topsoil hyperspectral reflectance; and 3) the DT model with SFPs as input is the most accurate at the genus level; the accuracy of allocation is 83.3% and the Kappa coefficient, an evaluation index, is 0.8. Our results suggest that soil allocations using topsoil hyperspectral reflectance can be performed accurately at genus level of classification, which can be of considerable help in research on the effects of soil minerals on soil spectral characteristics and in detailed soil mapping.

Deconstructing aeolian landscapes

Authors: Barrineau, P; Dobreva, I; Bishop, MP; Houser, C; Forman, SL

Source: CATENA, 174 452-468; MAR 2019

Abstract: Semi-stabilized dune systems are important indicators of Quaternary drought variability across central North America. The South Texas sand sheet (STSS) is the southernmost relict dune system on the Great Plains, and is exposed to higher evapotranspiration and moisture variability than similar landscapes farther north. In this study, a combination of surface and sub-surface remote sensing is used to analyze the semi-stabilized dune landscapes of the STSS in order to delineate distinct aeolian sediments that can represent generations of sedimentation or particular climate conditions. The combination of multi-resolution analysis of a LiDAR dataset, electromagnetic conductivity surveys, and XRF scans of soil cores are shown to be useful tools for deconstructing and modeling the environmental history of the STSS. Optically-stimulated luminescence (OSL) dating of identified surfaces suggests that the STSS is older than previously thought. Since dune systems are excellent repositori es of climate and biophysical data for a landscape, and are also sensitive to changes in climate and ecology, the methodology employed in this study can be used to characterize the vulnerability of other similar environments to climate change through the Holocene and over the next century.

Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy

Authors: Hong, YS; Chen, SC; Liu, YL; Zhang, Y; Yu, L; Chen, YY; Liu, YF; Cheng, H; Liu, Y

Source: CATENA, 174 104-116; MAR 2019

Abstract: Visible and near-infrared (Vis NIR) spectroscopy is used to estimate soil organic matter (SOM). Spectral preprocessing techniques and multivariate modeling methods play important roles in the quantitative analysis of SOM. First and second derivatives (i.e., the conventional integer order derivatives) are commonly used spectral derivatives, which, however, may ignore some detailed spectral information regarding SOM. Here, we presented a fractional order derivative (FOD) method to preprocess the reflectance spectra. Robust modeling methods are still required for accurate estimation of SOM. Local modeling technique (memory-based learning, MBL) was introduced to compare with two global modeling approaches, namely, partial least square (PIS) and random forest (RF). A total of 535 topsoil samples were gathered from Hubei Province, Central China, with their reflectance spectra and SOM contents measured in the laboratory. FOD was allowed to vary from 0 to 2 with an increment of 0.25 at each step. Coefficient of determination (R-2) and ratio of the performance to deviation (RPD) were employed as performance statistics during validation. Results showed that with the increase of derivative order, the baseline drifts and overlapping peaks were gradually removed but the spectral strength decreased concurrently. Higher derivative order reflectance (i.e., 1.5-order, 1.75-order, and 2-order reflectance) were more susceptible to spectral noise interferences. The correlation coefficient of SOM with FOD processed spectra at some specific wavelengths was larger than that with the original reflectance. MBL performed better than PLS and RF, regardless of FOD transformation. Calibration with 0.25-order reflectance and MBL provided the most accurate estimation of SOM, with an RPD of 2.23. Our results confirm the effectiveness of FOD and local modeling (MBL) in the development of Vis NIR models for SOM estimation.

Agricultural landscape evolution and structural connectivity to the river for matter flux, a multi-agents simulation approach

Authors: Reulier, R; Delahaye, D; Viel, V

Source: CATENA, 174 524-535; MAR 2019

Abstract: Agricultural activity evolution during the second half 20th century has profoundly changed agricultural landscapes in western Europe. Numerous scientific studies have shown that the consequences on erosive runoff processes are important. This study aims show one of the important consequences of these transformations that is the structural connectivity between cultivated plots and the river. The study is based on the analysis of aerial images taken at four different dates (1947, 1967, 1988 and 2014) and on the use of the LASCAR (LAndscape StruCture And Runoff) multi-agent model. It shows how agricultural landscapes have evolved over the past 70 years and how this has altered the connectivity of cultivated plots to the river. In addition to the development of spatial analysis indices, the results of this study offer key understandings relevant to all spatial scales (global to local). The study was carried out on a small agricultural watershed in the northwest of France (Lingevr es, 17.6 km(2)), whose recent landscape evolution is part of the overall dynamics of northwest French agricultural landscapes transformation.

Forecasting dryland vegetation condition months in advance through satellite data assimilation

Authors: Tian, SY; Van Dijk, AIJM; Tregoning, P; Renzullo, LJ

Source: NATURE COMMUNICATIONS, 10 469-469; JAN 28 2019

Abstract: Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands.

Connectivity as an emergent property of geomorphic systems

Authors: Wohl, E; Brierley, G; Cadol, D; Coulthard, TJ; Covino, T; Fryirs, KA; Grant, G; Hilton, RG; Lane, SN; Magilligan, FJ; Meitzen, KM; Passalacqua, P; Poeppl, RE; Rathburn, SL; Sklar, LS


Abstract: Connectivity describes the efficiency of material transfer between geomorphic system components such as hillslopes and rivers or longitudinal segments within a river network. Representations of geomorphic systems as networks should recognize that the compartments, links, and nodes exhibit connectivity at differing scales. The historical underpinnings of connectivity in geomorphology involve management of geomorphic systems and observations linking surface processes to landform dynamics. Current work in geomorphic connectivity emphasizes hydrological, sediment, or landscape connectivity. Signatures of connectivity can be detected using diverse indicators that vary from contemporary processes to stratigraphic records or a spatial metric such as sediment yield that encompasses geomorphic processes operating over diverse time and space scales. One approach to measuring connectivity is to determine the fundamental temporal and spatial scales for the phenomenon of interest and to m ake measurements at a sufficiently large multiple of the fundamental scales to capture reliably a representative sample. Another approach seeks to characterize how connectivity varies with scale, by applying the same metric over a wide range of scales or using statistical measures that characterize the frequency distributions of connectivity across scales. Identifying and measuring connectivity is useful in basic and applied geomorphic research and we explore the implications of connectivity for river management. Common themes and ideas that merit further research include; increased understanding of the importance of capturing landscape heterogeneity and connectivity patterns; the potential to use graph and network theory metrics in analyzing connectivity; the need to understand which metrics best represent the physical system and its connectivity pathways, and to apply these metrics to the validation of numerical models; and the need to recognize the importance of low level s of connectivity in some situations. We emphasize the value! in evaluating boundaries between components of geomorphic systems as transition zones and examining the fluxes across them to understand landscape functioning

Topographic variation in soil erosion and accumulation determined with meteoric Be-10

Authors: Marquard, J; Aalto, RE; Barrows, TT; Fisher, BA; Aufdenkampe, AK; Stone, JO


Abstract: Understanding natural soil redistribution processes is essential for measuring the anthropogenic impact on landscapes. Although meteoric beryllium-10 (Be-10) has been used to determine erosion processes within the Pleistocene and Holocene, fewer studies have used the isotope to investigate the transport and accumulation of the resulting sediment. Here we use meteoric Be-10 in hilltop and valley site soil profiles to determine sediment erosion and deposition processes in the Christina River Basin (Pennsylvania, USA). The data indicate natural erosion rates of 14 to 21 mm 10(-3) yr and soil ages of 26 000 to 57 000 years in hilltop sites. Furthermore, valley sites indicate an alteration in sediment supply due to climate change (from the Pleistocene to the Holocene) within the last 60 000 years and sediment deposition of at least 0.5-2 m during the Wisconsinan glaciation. The change in soil erosion rate was most likely induced by changes in geomorphic processes; probably soliflu ction and slope wash during the cold period, when ice advanced into the mid latitudes of North America. This study shows the value of using meteoric Be-10 to determine sediment accumulation within the Quaternary and quantifies major soil redistribution occurred under natural conditions in this region

A mechanical-dielectric-high frequency acoustic sensor fusion for soil physical characterization

Authors: Naderi-Boldaji, M; Tekeste, MZ; Nordstorm, RA; Barnard, DJ; Birrell, SJ


Abstract: The aim of this study was to develop a new soil multi-sensor fusion by combining mechanical, dielectric and acoustic responses in layered soils for estimation of soil water content, degree of compactness and texture. The load cell, dielectric and acoustic sensors were integrated on a 25 mm diameter, 45-deg conical probe. Functioning of the dielectric sensor is based on the fringe-field between the two adjacent ring electrodes. Passive acoustic responses (emissions) at high frequency (10-350 kHz) were measured using an acoustic emission (AE) piezo sensor during probe penetration into the layered soil. The AE sensor was installed inside the cone (close to the cone tip) to receive passive AEs originating at the cone-soil interface and between soil particles surrounding the moving probe. The multi-sensor probe was evaluated using laboratory vertical penetration tests in various layered soil columns consisting of three soil types: clay, loam and sand. Each soil texture was prepare d at three soil water contents (0.4, 0.6 and 0.8 x lower plastic limit) and remolded at three bulk densities (1.25, 1.4 and 1.55 Mg m(-3)) in the column. The acoustic frequency distribution spectra clearly distinguished the sandy soil texture (irrespective of bulk density and water content) from the clay and loam soil textures, with a relatively poor discrimination between clay and loam soil. The results indicate that the dielectric sensor output is highly correlated to soil volumetric water content (R-2 = 0.78, mean absolute error (MAE) = 0.025 m(3) m(-3)) with a minor effect of bulk density. Data fusion of mechanical, dielectric and acoustic sensors significantly improved the measurement of soil water content (R-2 = 0.93, MAE = 0.014 m(3) m(-3)). Degree of soil compactness was well predicted from data fusion of the three sensors (R-2 = 0.80, MAE = 3.32%). Therefore, the multi-sensor probe developed in this study is an economical and viable instrument that shows promising p otential for in-situ use in soil physical characterization. ! Laboratory and field evaluations to further examine the sensor for practical applications, such as on-the-go soil physical sensing in precision agriculture, are needed.

A new method to analyse the soil movement during tillage operations using a novel digital image processing algorithm

Authors: Li, PL; Ucgul, M; Lee, SH; Saunders, C


Abstract: Tillage operations are a vital part of agricultural crop production. Economic and environmental considerations are forcing farmers to manage soil tillage with optimum tool configurations to get the desirable soil condition. Soil disturbance caused by soil engaging tools, in particular soil layer mixing, is an important phenomenon that needs to be clearly understood. Current methods used to investigate soil layer mixing are limited, especially when multiple layer mixing needs to be investigated. The use of physical tracers is the most common approach. Although this method can provide some useful information, since it does not provide a full representation of how soil layers are moving or mixing, error-prone rough estimation is unavoidable. In this study, the mixing of soil layers was investigated using different coloured sands placed in layers at different depths (to continuously investigate the soil layer mixing). A new colour clustering algorithm to analyse such multi-colour ed layer mixing performance was developed using K-means clustering with a PCA (principal component analysis) approach. The validation study of the proposed algorithm was conducted using the Columbia multispectral image database before implementation on the experimental coloured soil test. The test results showed that the proposed method and algorithm is effective in analysing multiple soil layer mixing.

Managing for soil carbon sequestration: Let’s get realistic

Authors: Schlesinger, WH; Amundson, R

Source: GLOBAL CHANGE BIOLOGY, 25 (2):386-389; FEB 2019

Abstract: Improved soil management is increasingly pursued to ensure food security for the world’s rising global population, with the ancillary benefit of storing carbon in soils to lower the threat of climate change. While all increments to soil organic matter are laudable, we suggest caution in ascribing large, potential climate change mitigation to enhanced soil management. We find that the most promising techniques, including applications of biochar and enhanced silicate weathering, collectively are not likely to balance more than 5% of annual emissions of CO2 from fossil fuel combustion.

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