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Loess deposition from wind is one of the major sources of soil development in many regions including northeastern China. The dust deposition on snow from 6 sites in Shenyang, northeastern China was collected and characterized physically and chemically to determine its origin (modern vs. re-deposited loess). The origin and characteristics of the dust deposition were evaluated through comparisons with possible sources of sand desert areas based on elemental compositions, such as the A-CN-K (Al2O3-CaO + Na2O-K2O) ternary plot and ratios of Al2O3/TiO2, Fe2O3/TiO2, and Fe2O3/Al2O3. The texture and composition results showed that the dust deposition was most recent and not re-deposited loess. The dust deposition was predominantly silt-sized grains (2–50 μm) and had greater amounts of Na2O (1.7% vs. 1.5% average), MgO (2.1% vs. 1.2%), and CaO (3.2% vs. 1.2%) when compared to the local soil. Its mineral composition was mainly quartz, feldspar, and mica. The magnetic susceptibility of the dust deposition was 30.9 × 10−8 m3/kg and lower than the local soil which was 40.4 × 10−8 m3/kg. Based on the A-CN-K ternary plot, the dust had a weaker weathering intensity than the local soil, loess-paleosol sequences, and red clay. This indicated that the dust was not a re-deposited loess. Furthermore, according to the discriminating analysis using ratios of Al2O3/TiO2, Fe2O3/TiO2, and Fe2O3/Al2O3, the source for the dust was traced from the deserts of south Mongolia, Tengger, Gulban Tungut, and Taklimakan. The two primary factors transporting the dust were the Mongolia Cyclone and westerlies. The dust was blown into the atmosphere, and transported by the atmospheric circulation from west to east and/or from northwest to southeast, which was accompanied also by a cold front cyclone moving southeast towards the study area. The origin, transportation, and deposition process of the dust deposition in this study provides further evidence about the origin of the loess and its likely role in soil development in Shenyang, northeastern China.

Terrain attributes are commonly used as predictors of soil organic carbon (SOC) in digital soil mapping. However, there are no fixed rules in the selection of suitable grid size and models with different attribute combinations. Past studies have used a few empirical, as well as pedological guidelines to determine scale dependency of terrain attributes on SOC prediction. The aim of this paper was to evaluate the scale dependency of terrain attributes using varying grid sizes and select the most important attributes and optimum grid size for SOC prediction. A 7500 km 2 area located in Denmark was selected; a total of 2,514,820 prediction models were generated in Cubist data-mining tool in which 8570 SOC observations and 22 terrain attributes at 71 different grid sizes ranging from 12.8 m to 2304 m were used as inputs. Terrain attributes were derived from the Light Detection And Ranging (LiDAR) based digital elevation model (DEM) (1.6 m × 1.6 m grid size) and was subsequently resampled to different resolutions by simple mean aggregation. Relative importance and usage of each terrain attribute in each prediction model were computed and only the top 5 attributes were reported for different attribute combinations. The results showed that the relative contribution of terrain attributes to predict SOC distribution varied by grid sizes, and by grid size and attribute combinations. Overall, Relative Slope Position (rsp), Channel Altitude (chnl_alti), and Standard Height (standh) were the three most important terrain attributes in the five-attribute-model at all grid resolutions and the remaining two attributes being Normalized Height (normalh) and Valley Depth (vall_depth) at resolutions finer than 30 m, and elevation and Channel Base (chnl_base) at resolutions coarser than 30 m. The models at 88 m and 92.8 m grid size (nearest to the 90 m SRTM data resolutions) and 30.4 m (nearest to the 30 m TM satellite image resolution) were validated. We observed that the model performance was dependent on grid size, and by attribute combinations. For example, for a 4-attribute model that used rsp, chnl_alti, elevation and vall_depth, the best performance was for 30.4 m compared to 88 m and 92.8 m grid sizes. We found that for modeling SOC distribution, the three terrain attributes rsp, chnl_alti, and standh were found to be the most important at all resolutions and should be considered as important variables in future SOC modeling studies in young moraine landscapes.

Long-term weathering patterns in northeastern China are not well known due to few well-preserved terrestrial paleo-environmental archives in this area. The Chaoyang section is a typical well-preserved loess-paleosol sequence in northeast China with continuous deposition since 423 ka BP; the deposit thus represents an archive of climate change for this region. The sequence was geochemically characterized by major elemental compositions, elemental ratios, a ternary diagram of Al2O3-(CaO* + Na2O)-K2O, improved quantitative reconstruction and elemental distributions with respect to the average for the upper continental crust (AUCC). Another typical loess-paleosol sequence of Lingtai section from the central Chinese Loess Plateau was used for comparison with the loess-paleosol one. The similar AUCC-normalized major elemental distributions and strong correlation of the major elemental compositions between the loess-paleosol sequences indicate that they may have originated predominantly from a similar loess source. Based on the variability in chemical indices corresponding to soil magnetic susceptibility (SUS) and field observations, the loess-paleosol formation period of 423–77 ka BP was separated into eight sub-periods including four periods with greater chemical weathering intensity (paleosols) and four periods characterized by the relative lesser chemical weathering intensity (loess). Relatively intense desilication and fersiallitization primarily occurred in the loess-paleosol sequence from northeast China with greater losses of SiO2 (3.54% in average) and gains of Fe2O3 (0.77%) and Al2O3 (0.33%). Such processes also were reflected in an increase in the amount of K2O (0.41%). Ca and Na leaching was still predominant in the loess-paleosol sequence from the central Chinese Loess Plateau with greater losses of CaO (28.03%) and Na2O (14.03%). The sequence from northeast China records chemical weathering during 423–77 ka BP, which is comparable to the weathering cycles in the sequence from the central Chinese Loess Plateau.

Little is known about how Fe-Mn nodules vary in relation to pedogenetic horizons in soils derived from loess. In this investigation nodules were collected according to soil genetic horizons from a Fragiudalf in loess at Southeast Purdue Agricultural Center (SEPAC) in Indiana and physical, chemical, micro- and macromorphological features of nodules and their soil matrix were determined as a function of soil depth. The quantity, chemical composition, and micromorphology of the nodules varied with genetic horizons. The Ap horizon was found to contain the largest amount by weight of nodules (25.43 g/kg) and this was likely due to soil erosion. After the Ap horizon, the 2Btx1 and 2Btx2 horizons had the greatest content of Fe-Mn nodules by weight. Nodules had lower concentrations of SiO2, Al2O3, K2O, TiO2, MgO, Na2O, and CaO; in contrast Fe2O3, MnO, and P2O5 in nodules showed clearly greater concentrations than that in the corresponding soil material. The SiO2 content decreased with nodule size whereas Fe2O3 and MnO increased. The least depletion of SiO2 in nodules of 1–2 mm (N2) and 0.5–1 mm (N1) was in the Bt2 horizon, and was greatest in the 2Btx2. The Al2O3 had accumulated in the N2 and N1 nodules within the Ap horizon and in the N2 within the 2Btx2 while other horizons were depleted of Al2O3. Brown nodules had a small Mn/Fe ratio ( 1). The internal morphology of the nodules also varied with Mn/Fe ratios. The fungal hyphae or bacteria resulted in honeycomb, cell, cone, or dendritic structures. The cell and cone morphology required large Mn/Fe ratios, low pH, and high content of Ald. The scanning electron microscopy (SEM) analysis illustrated an elemental distribution trend such that the Mn was centralized in the nodule internal structure; whereas, Fe was concentrated towards the exterior. The band structure of nodules likely formed in response to seasonal wetting and drying as an accretionary process. Three basic formation stages for a well developed nodule can be inferred which correspond to different soil forming environments: 1) Fe enrichment for nucleus formation in wet soil, 2) Mn enrichment in an intense dry soil, and 3) Fe banding enrichment in a slightly dry soil. Nodule formation occurred synchronously with soil pedogenesis and can therefore be used as a proxy for understanding soil pedogenesis and the environment under which it occurred.

Soil horizons are the manifestation of pedogenesis and contain the basic morphologic indicators of soil formation. Accurate and quantitative tools for delineating soil horizons in-situ can assist soil scientists towards rapid and dynamic soil survey information. The objective of this study was to develop a deep-learning-based, soil-profile-imaging method for identifying and delineating soil master horizons to assist in digital soil descriptions. A total of 160 soil profile images from four soil orders (Alfisols, Entisols, Inceptisols, and Mollisols) were collected from north China in the Inner Mongolia and Liaoning regions. The 160 profile images were amplified to 2400 individual images for model building using data-augmentation procedures. The augmented profile image dataset was divided into training (70%), validation (15%), and test (15%) datasets. The training and validation datasets were imported into a nested, U-net network for model building. The proposed deep-learning (DL) model classified soil profiles into A, B, and C horizons. The mean pixel accuracy of the DL model was 0.86 with the training dataset, 0.82 with the validation dataset, and 0.83 for the test dataset. Results showed that the DL model was judged to be accurate enough for identifying and delineating soil master horizons from profile images in practice. Based on the proposed DL model, a smartphone application was subsequently developed to digitalize the soil profile and assist field evaluation of soil profile horizonation. The smartphone application, for the Android operating system (version 5.0 and greater), featured a response time of

We review the evidence for a naturally occurring acid sulfate weathering event along the southern shore of Lake Michigan that was initiated ~ 15,000 years BP (before present) at the close of the Wisconsin Glaciation. The evidence includes: (1) “unproductive black soils” that were encountered when settlers drained the wetlands in the area for agriculture, (2) areas of soils that have anomalously low base saturation and are classified into ultic subgroups in a region where typic subgroups are the norm, (3) unexpectedly high arsenic concentrations in streambed sediments of the Kankakee River, (4) pyrite, jarosite, and gypsum in cobbles from outwash deposits in the areas, and (5) identification of a modern day “unproductive black soil” with a surface soil pH of 2.1. We propose that the acid sulfate weathering event was initiated when the Lake Michigan Lobe of the Wisconsin Glacier eroded pyrite-rich bedrock and deposited it in an outwash fan in front of the Valparaiso Moraine, where sorting by water depleted calcium carbonate-rich fine material relative to pyrite-rich coarse material. Acid sulfate weathering on the higher landscape positions led to well-drained soils depleted of calcium and magnesium and resulted in anomalously low base saturation and soils now classified into ultic subgroups. Calcium, iron and sulfate moved to adjacent wetlands in the lower lying landscape positions, where reduction of iron and sulfate resulted in the precipitation of secondary pyrite in the accumulating organic material. Drainage of these wetlands for agriculture beginning in the late 1800s exposed the secondary pyrite to oxygen and initiated a second cycle of acid sulfate weathering that led to some of the reports of “unproductive black soils.” These soils developed extremely low pH upon drainage. Some of these highly acid conditions exist today. Arsenic in the original pyrite is moving through the landscape and is manifested in the unexpectedly high arsenic contents in streambed sediments of the Kankakee River that drains the area. This work shows that a whole landscape approach is necessary to understand how seemingly unrelated observations are all manifestations of a single natural phenomenon.

Accounting for soil organic carbon (SOC) in stony soils is critical for estimating global SOC stocks. However, accurate and cost-effective assessments of SOC stocks in stony soil profiles remain challenging due to the difficulties in accurately determining the rock fragment volume. The objective of this study was to develop a stereological profile imaging method for rock fragment volume fraction estimation and SOC stock assessment in stony soil profiles. Three soil profiles with different rock fragment lithologies, concentrations, and distributions were imaged and quantitatively sampled. The stereological profile imaging method reproduced consistent estimation of the 3-dimensional rock fragment number density distribution and volume fraction compared to the direct measurement. The SOC stocks of the three stony profiles estimated by visual estimation, profile imaging, and stereological profile imaging methods were also assessed and compared to direct measurement. The average difference in total SOC stock between direct measurement and visual estimation, profile imaging, and stereological profile imaging methods was 0.32, 0.17, and 0.09 kg m−2, respectively. The total profile (0–100 cm) SOC stock for profile 2 differed among the visual estimation and profile imaging methods, but estimated SOC stock from the proposed method did not differ from direct measurement for the three profiles. Results also showed that the differences in SOC stock assessment between rock fragment estimation methods increased with rock fragment concentration. The proposed method was less affected by rock fragment concentration and was more stable compared to the visual and profile imaging methods. The stereological profile imaging method was demonstrated to be a reasonably accurate method for the quantification of rock fragment number density distribution, volume fraction, and SOC stock in stony soil profiles.

Diverse projects are being carried out worldwide focusing on development of more accurate soil maps and one of the most valuable sources of data are the existing soil maps. This work aimed to (i) compare two data mining tools, KnowledgeMiner and decision trees, to retrieve legacy soil data from a detailed soil map, (ii) to create and validate the predicted soil maps in the field with the objective to identify the best method for modeling and refining soil maps, (iii) extrapolating soils information to the surrounding similar areas and (iv) to assess the accuracy of this soil map. The study was carried out in Minas Gerais state, Southeastern Brazil. From a detailed soil map, information of 12 terrain attributes was retrieved from the entire polygon of each mapping unit of the map (MUP) and from a circular buffer around the sampled points (CBP). KnowledgeMiner and decision trees were employed to retrieve information per soil class and soil maps were created per method. A field validation of 20 samples was chosen by a cost-constrained conditioned Latin hypercube sampling scheme and the accuracy of all maps was assessed using a global index, Kappa index, and errors of omission and commission. The KnowledgeMiner MUP map had a greater accuracy than the other methods, being even more accurate than the original map, accounting for 80% of global index and a Kappa index of 0.6524. The information extracted by KnowledgeMiner provided rules for mapping the watershed surroundings with 70.97% of global index and a kappa index of 0.5586. Legacy soil data extracted by KnowledgeMiner from a detailed soil map and used to model soil class distribution outperformed decision trees, promoted improvements on the existing soil map, and allows for the creation of a low cost soil map for the surroundings of the study area.

A typical loess–paleosol sequence at Chaoyang in China, deposited continuously since 423 ka BP, was examined to quantitatively reconstruct a loess–paleosol sequence. First, particle size distribution was evaluated based on clay-free fractions (where the sum of sand + silt = 100%) and fine-earth fractions and used to characterize the parent material uniformity as well as field morphology, geochemical features, and micro-morphology characteristics. Second, mass balance (MB) and soil reconstruction (SR) approaches were employed to examine the linkage between pedogenesis and loess deposition. The results indicated a uniform loess deposit below 228 cm depth, and the presence of four reddish stratigraphic layers interbedded with four yellowish stratigraphic layers, all derived mostly from silt eolian deposits. The main soil pedogenic processes following silt deposition were clay eluviation–illuviation, silification, and ferrallitization. The strongest pedogenesis occurred for the S3 deposit layer during 225–243 ka BP and was associated with the greatest mass loss of non-clay (7.6%), Na2O (42%), CaO (21%), MgO (28%), SiO2 (10%), and at the same time with the greatest gains in clay (42%), Fe2O3 (8%), and Al2O3 (4%) together with mass loss changes (MC) of 620 g/100 cm2. The greatest mass gain changes (234 g/100 cm2) occurred in the L4 deposit layer during 243–311 ka BP and were associated with gains in Na2O (18%) and P2O5 (4.5%) indicating the fastest deposition rate. During the long period of sedimentation (423 ka BP) loess deposition was predominant during dry-cold periods, whereas paleosol formation from pedogenic processes was predominant during wet-warm periods. Thus, the formation of paleosols and loess deposition were dependent on the relative rate of pedogenesis and deposition as controlled by climate change. Under an East Asian summer monsoon (EAS) paleosol formation dominated, whereas loess deposition dominated under an East Asian winter monsoon (EAW). Quantification of the degree of loess development (paleosols) or loess deposits aids reconstructing soil genesis and understanding the loess–paleosol formation sequence and its linkage to climate change.

Most existing soil classification systems were developed to understand and provide information on soils, their natural properties and potential use for certain purposes. The conceptual developments of the systems took place before the recent boom of observation technologies, data storage and data processing achievements that can support to determine or predict soil differences. Until the recent past soils under agricultural or forestry use received more attention than other soils, such as anthropogenically modified or urban soils, or soils of the cold regions. The broader view of soil functions and the understanding of global environmental processes require a better understanding and description of all soils. Precisely recorded and harmonized data is needed to serve the new era of modern agricultural practices, other land uses as well as different scientific applications. The soil science community is challenged to apply the accumulated knowledge on soil formation, soil differences and functions, as well as, new tools of robust data processing to evaluate current systems and define objective relationships for better future classification systems. The evaluation of existing soil classification systems may help the understanding of taxonomic relationships of differentiated soil groups and improve our methods of classifying soils. This paper is summarizing the approaches and methods of evaluation that was applied for the great group and higher levels of Soil Taxonomy. Simple statistical and pedometric methods were applied on centroids and, calculated on the basis of properties commonly used to define the classification units. The centroids provide an objective tool to evaluate the concepts of taxa and the taxonomic relationships between them. Examples of conceptual evaluations and detailed discussions of the taxonomic distance calculations between the great groups within their orders and members of other orders are provided. The presented methods and relationships were found very useful for the evaluation purpose. The extension of the methods for other systems, other data bases and the combination of those is in progress. The initial results suggest that the objective, pedometric approaches can support the development of an envisioned Universal Soil Classification System.

Spatial heterogeneity in environmental factors on the land surface moderates exchanges of water, energy, and greenhouse gases between the land and the atmosphere. However, appropriately representing this heterogeneity in earth system models remains a critical scientific challenge. We used a large dataset of environmental factors (n = 31) representing soil-forming factors, field observations of soil organic carbon (SOC) (n = 6213), and a machine-learning algorithm (Cubist) to analyze the scaling behavior of SOC across the conterminous United States. We found that various environmental factors are significant predictors of SOC stocks at different spatial scales. Out of the 31 environmental factors we investigated, only 13 were significant predictors of SOC stocks at spatial scales ranging from 100 m to 50 km. Overall, topographic variables had higher influence at finer scales, whereas climatic variables were more important at coarser scales. The model performance worsened with increasing scale or the spatial resolution of prediction (R2 = 0.38–0.65). The strength of environmental controls (median regression coefficient) on SOC weakened with scale, and we represented them using mathematical functions (R2 = 0.38–0.98). Both the mean and variance of SOC stocks decreased linearly with increasing the scale in soils of the conterminous United States. Fitted linear functions accounted for 81% and 82% of the variability in the mean and variance of SOC, respectively. We also found linear relationships among mean and high-order moments of SOC (R2 = 0.51–0.97). Improved understanding of the scaling behavior of SOC stocks and their environmental controllers can improve earth system model benchmarking and may eventually improve representation of the spatial heterogeneity of land surface biogeochemistry.