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With increasing urban area and population, global cities are playing a more important role in the alteration of the global biogeochemical cycles. The aims of this study are to understand the concentrations and stoichiometric relations of biogenic elements (C, N, and P) in urban soils, further to reveal the effects of C, N, and P accumulation on the urban environment. We collected 317 surface (0–10 cm) soil samples taken from Nanjing, China, a typical city with more than 2000 years of history. These soil samples were located in different urban zones of Nanjing with different land use histories. The soil C, N, and P concentrations were determined. The stoichiometric relations of soil C, N, and P were investigated in urban soils. Meanwhile, some studies on sources of C, N, and P in diverse urban settings from literature were combined to explore the universal rule of C, N, and P cycling and their ecological and environmental effects in urban area. Compared to rural soils, more C, N, and P are accumulated in the urban soils, which also change their stoichiometric relations. The concentrations of OC, TN, and TP in urban top soils are 17.0 ± 9.69 g kg−1, 1.53 ± 0.92 g kg−1, and 1.31 ± 0.67 g kg−1, respectively. The mean atomic ratio of C:N:P is 37:3:1 in the surface of urban soils that strictly differs from natural soils in China and the whole world. The mean of C:N ratio in urban soils is similar to that of agriculture, grassland, and forest soils. However, the ratios of C:P and N:P in urban soils are much lower than that in agricultural, forest, and grassland soils. This implies that P is extremely enriched in the urban soils. The high C in urban soils are considered coming from natural and anthropogenic sources. The high N and P mainly come from anthropogenic sources. The well-constrained C:N:P ratio in rural soils does not apply for urban soils. The abnormal C:N:P ratio of urban soils is the result of unbalanced accumulation of C, N, and P from human activities. Urban soils are already an important storage of carbon. High N and P in urban soils may bring threat of surface water eutrophication and ground water contamination. These effects are expected to increase with the city development time.

Purple paddy soils cover about 66% of the paddy soils in Sichuan Basin. Complex interactions made the effects of potassium fertilizer application on NH4+ adsorption more complicated and the behavior of NH4+ and K+ interaction in purple paddy soils has rarely been evaluated. We hypothesize that correct management of these soils can enhance the efficiency of ammonium and potassium fertilizers. In order to determine correct management methods, it is important to understand the interaction of main nutrient cations NH4+ and K+ and maturation hydroponics in purple paddy soils with different paddy cultivation times. A purple paddy soil chronosequence with cultivation history from 0 to 300 years was studied to understand the respective effects of SOM and mineralogy on NH4+ and K+ sorption behavior by adsorption experiments using samples both before and after H2O2 oxidation. The study shows that maturation hydroponics influenced NH4+ and K+ adsorption mainly through changing clay mineralogy and enhancing SOM accumulation. SOM and smectite play positive effects for NH4+ adsorption. The K+ adsorption, however, is mainly affected by the smectite changes. The adsorption percentages are obviously higher at 25–50 mg L−1 for each NH4+ solution and higher at 50–100 mg L−1 for each K+ solution. When ammonium and potassium were applied together, NH4+ and K+ adsorption are mutually weakened. In addition, the percentage of K+ adsorption increased from 49.7–57.7% for the 100 mg L−1 K+ solution to 50.9–58.5% for the 200 mg L−1 K+ solution at 200 mg L−1 of added NH4+. SOM enrichments could be a positive way to improve the ammonium fertilizer utilization efficiency in purple paddy soils. Potassium may be more vulnerable to loss at low K+ concentrations given that ammonium and potassium fertilizers were applied together and it should be separately applied to enhance the utilization efficiency of applied fertilizer.

Depth patterns of soil microbial distribution have not been well characterized and little is known about the balance between stochastic and deterministic processes in shaping microbial community th...

To probe Fe isotopic signatures of the Quaternary Red Clay (QRC) in subtropical southeastern China, two QRC profiles located in southern Anhui Province, Southeast China, were studied. The whole profiles were generally in a narrow range in δ 56 Fe of −0.040 ± 0.061‰—0.117 ± 0.048‰. The Yellow-brown Earth (YBE) and Uniform Red Clay (URC) of the upper profiles were similar in δ 56 Fe to the Xiashu Loess along the Yangtze River, Southeast China, and the loess, paleosols and Tertiary red clay in the Chinese Loess Plateaus, Northwest China, as well as the baseline of terrestrial igneous rocks. The slight variation of δ 56 Fe from the YBE to the URC suggests that the chemical weathering and rubification on the aerobic surface did not fractionate Fe isotopes significantly. The Reticulate Red Clay (RRC) underlying the YBE and URC fluctuated in δ 56 Fe to some extent. The white veins separated from the lower RRC were depleted of Fe oxides and heavier in Fe isotopes, and the red veins, on the contrary, were isotopically lighter in Fe. δ 56 Fe of the white veins, 0.345 ± 0.053‰ on average, was significantly higher than the bulk samples, 0.033 ± 0.044‰ on average, and the red veins, −0.021 ± 0.046‰ on average. This suggests that the reticulate textures of the RRC were formed by the reductive removal/re-precipitation of Fe due to the cyclical changes of redoxic conditions. Fe isotopic composition of the QRC was dominantly controlled by the redoxic mobility of Fe, rather than the degree of weathering. δ 56 Fe of the profiles was not correlated with the paleoclimatic events, and thus cannot be used as a paleoclimatic proxy. The fractionation of Fe isotopes in the lower RRC, however, indicated the occurrence of over-humid paleoclimate with alternations between drought and wetness in Southern China during the Pleistocene.

Spatial prediction is an important approach to obtain location-specific values of soil electrical conductivity (EC), which is a proxy of soil salinity and important for agricultural management in arid and semi-arid areas. Linear regression models assume that the relation between soil EC and environmental covariates is constant over the area to be predicted. This is problematic at the regional scale, at which some of the regression parameters may indeed be globally constant, whereas others may vary locally. Moreover, model residuals often exhibit spatial dependence, which invalidates the ordinary least squares linear regression. This study examined the combination of a mixed geographically weighted regression model with simple kriging of the residuals (MGWGK) for mapping soil EC in the Heihe River Basin, an inland river basin in arid northwest China. We compared the performance of MGWRK with those of multiple linear regression (MLR), regression kriging (RK), geographically weighted regression (GWR), geographically weighted regression kriging (GWRK) and mixed geographically weighted regression (MGWR). Environmental covariates were developed from spatial information on topography, climate, vegetation, and geographic position. A ten-fold cross-validation was applied to evaluate predictive accuracy of the various methods. Soil EC ranged from 0.031 to 182.100 dS m−1, exhibiting a contrasting distribution of soil EC in the upper, middle, and lower river reaches. The MGWRK method outperformed other methods. The effects of different environmental covariates on soil EC were revealed by the fixed and geographically varying parameters of MGWRK. The nugget-to-sill ratios of fitted variogram models all fell between 25 and 32%, exhibiting moderate spatial autocorrelation of models residuals. Predictive accuracy was improved by MGWRK, as the spatial dependence of model residuals were included in the prediction. When selecting an optimal linear regression model, covariates should be tested to see if they are constant (as in MLR) or spatially varying (as in GWR) or semi-varying (as in MGWR), and model residuals should be tested for spatial dependence (as in MGWRK).

Organic carbon (OC) plays a pivotal role in earth surface systems. However, current three-dimensional (3D) mapping studies usually focus on a soil depth of 1 m rather than the depth to the bedrock. A top-down method using piecewise depth functions was proposed to fit the OC vertical decline patterns in a subtropical catchment in southern China. The vertical variation in OC was greatly affected by the heterogeneous topsoil due to natural processes and anthropogenic disturbances. Thus, topsoil OC maps were produced and utilized as covariates to indicate the OC decline rates and to benefit the 3D OC simulation. A distribution map of the underground critical zone thickness (UCZT) was applied as a lower boundary for the 3D simulation. Six widely used mapping techniques were performed to predict the spatial distribution of topsoil OC and depth function parameters. The overall cross-validation results showed a root mean squared error (RMSE) of 1.7 g kg–¹ and a ratio of performance to deviation (RPD) of 1.82. Given limited boreholes, validation showed that the depth function performed better in the lower part (>1 m) than in the upper part (

Nitrate accumulation has been reported in the top 1 m and subsurface soil (> 1 m) across arid to semi-humid regions, but not in humid regions. Nitrate inventories through the whole regolith, referred to collectively as soil and saprolite, in humid regions have received little attention to date, likely due to previously assumed low nitrification rates and large nitrogen (N) losses by severe surface runoff and erosion. In order to understand if and how reactive N exists in the below ground (soil and saprolite) in humid environment, the amount of NO3--N, NH4+-N and dissolved organic N (DON) present in the regolith to a depth of 9 m in a typical red soil Critical Zone was investigated under different land uses (upland, woodland and paddy field). The Red Soil Critical Zone Observatory is located in the subtropical Jiangxi Province, China, with a mean annual precipitation of 1795 mm and mean annual potential evapotranspiration of 1229 mm. The examined regoliths were acidic, highly weathered, and mainly clay loam in texture. Results showed that on average 92% (827 ± 97 kg N ha-1) of NO3--N and 82% (521 ± 153 kg N ha-1) of DON were stored at depth (from a depth of 1 m to the bedrock surface) in the upland regolith, while 92% (283 kg N ha-1) of NO3--N and 78% (820 kg N ha-1) of DON were stored at depth in the woodland regolith. Nitrate N significantly accumulated with depth in the upland regolith from the 1- to 4-m depth interval (p < 0.01), while the inventory (632 ± 75 kg N ha-1) in the top 3-m zone accounted for on average 71% of the total. Dissolved organic N significantly accumulated with depth in the upland regolith from the 0- to 3-m depth interval (p < 0.01), while the inventory (408 ± 75 kg N ha-1) in the top 3-m zone accounted for on average 64% of the total. There was no significant accumulation for NH4+-N throughout the upland regolith (p = 0.35). No substantial accumulation of dissolved N was measured at depth in paddy field regoliths with different cultivation ages. The finding that large reservoirs of reactive N can exist in deep regolith rather than in the routinely investigated solum of subtropical regions shows a missing part of the terrestrial N budget and raises concerns about potential 34 groundwater nitrate pollution.

The soil's pH is the single most important indicator of the soil's quality, whether for agriculture, pollution control or environmental health and ecosystem functioning. Well documented data on soil pH are sparse for the whole of China — data for only 4700 soil profiles were available from China's Second National Soil Inventory. By combining those data, standardized for the topsoil (0–20 cm), with 17 environmental covariates at a fine resolution (3 arc-second or 90 m) we have predicted the soil's pH at that resolution, that is at more than 109 points. We did so by parallel computing over tiles, each 100 km × 100 km, with two machine learning techniques, namely Random Forest and XGBoost. The predictions for the tiles were then merged into a single map of soil pH for the whole of China. The quality of the predictions were assessed by cross-validation. The root mean squared error (RMSE) was an acceptable 0.71 pH units per point, and Lin's Concordance Correlation Coefficient was 0.84. The hybrid model revealed that climate (mean annual precipitation and mean annual temperature) and soil type were the main factors determining the soil's pH. The pH map showed acid soil mainly in southern and north-eastern China, and alkaline soil dominant in northern and western China. This map can provide a benchmark against which to evaluate the impacts of changes in land use and climate on the soil's pH, and it can guide advisors and agencies who make decisions on remediation and prevention of soil acidification, salinization and pollution by heavy metals, for which we provide examples for cadmium and mercury.

Modern monothetic hierarchical soil classification systems such as the Chinese Soil Taxonomy (CST) are semi‐quantitative and their future development is likely to trend towards a fully quantitative system using the concepts of numerical taxonomy. Previous researchers have calculated the taxonomic distances between individual soils based on soil physiochemical properties, not, however, based on spectra of full soil profiles with different horizons. We hypothesized that numerical taxonomy implemented by cluster analysis of the taxonomic distance matrix based on vis–NIR spectra would accord with some CST Orders assigned by pedologists, and not with others, depending on how closely spectral features represent the diagnostic features used in the classification. Taxonomic distances in spectral space were computed for all pairs of 191 profiles, resulting in a distance matrix on which hierarchical cluster analysis was performed. Different indices were calculated to determine the optimum number of clusters, resulting in four spectral soil classes. These were then compared with CST Orders assigned to the profiles by expert allocation. The numerical classes and CST Orders matched poorly because of the completely different classification philosophies behind numerical taxonomy and the CST, which is based largely on presumed genesis and uses sharp thresholds leading to very similar soils being allocated to different classes. Thus, we consider the numerical classification as information that is complementary to the monothetic hierarchical system. Numerical classification can reveal the taxonomic objective aspects of the relation between the defined classes and can suggest new groupings. HIGHLIGHTS: Taxonomic distances can serve as an objective measure of soil similarity. Soil spectra are a good candidate for numerical classification based on taxonomic distances. The conceptual basis of Chinese Soil Taxonomy (CST) does not match that of taxonomic distance. Numerical classification based on spectra can suggest revisions to the CST.

The concentration of soil organic carbon (SOC) is one of the most important soil properties, and its spatio-temporal variability greatly affects the global climate and agroecology. To investigate the effects of land use and climate change on SOC, a heuristic cellular automaton (HCA) model was proposed and applied to a plains area in eastern China with a high population density and rapid urbanization rate. The HCA model was designed to simulate the geographical variation in SOC dynamics over the long term (2080), and lateral carbon (C) migration is represented by revised neighbourhood variables at the macro scale. Three widely used soil mapping techniques were applied for comparison: multiple linear regression (MLR), support vector machine (SVM) and kriging with external drift (KED). The HCA model enhanced the accuracy of the predicted SOC by 15.27% over MLR, 12.31% over SVM and 10.98% over KED. Future land use maps were produced using legacy land use data and artificial neural network-based cellular automata (CA), and the simulation results showed the rapid urbanization of this area, where the percentage of cropland declined by 23.75% and that of village/urban areas increased by 22.90% from 2010 to 2080. The overall SOC concentrations are anticipated to increase by 2080 given the rising mean annual air temperature and mean annual precipitation. Our results also suggested that land use change clearly influenced the change in soil C, with village/urban areas exhibiting higher SOC than cropland. To provide stakeholders with accurate soil information, it is important to understand the comprehensive impacts of land use and climate change on soil evolution; this study illustrates the value of integrating pedogenetic information in soil C simulation models.

The soil organic carbon (SOC) and total nitrogen (TN) of topsoil could be greatly affected by the conversion of grassland to cropland (CGC) due to vegetation harvesting and soil erosion. However, few studies have evaluated the effects of these changes in the subsoil and distinguished its main controls at the regional scale. This paper investigated the total changes in the SOC stock (SOCS) and TN stock (TNS) of soils from 0 to 30 cm and 0–100 cm in depth over the past three decades in the Songnen Plain, China, a typical Mollisol region. The CGC resulted in a moderate loss of topsoil C and N but an increase in subsoil C and N, and the soil mapping results showed that approximately 7.47 × 1011 kg C and 1.51 × 1011 kg N were accumulated during the past three decades. Prediction uncertainty was assessed by 100 model runs with random jackknife partitions, and high uncertainty was found in the areas with rapidly changing SOCS and TNS levels. Our findings indicated that subsoil should be considered in the estimation of the SOCS and TNS at the regional scale. The SOCS and TNS of the CGC areas were anticipated to increase after long-term cultivation. In view of ongoing cropland expansion, up-to-date land use and soil type data are vital for selecting monitoring sites and understanding long-term soil evolution at the regional scale.

Soil diagnostic horizons, which each have a set of quantified properties, play a key role in soil classification. However, they are difficult to predict, and few attempts have been made to map their spatial occurrence. We evaluated and compared four machine learning algorithms, namely, the classification and regression tree (CART), random forest (RF), boosted regression trees (BRT), and support vector machine (SVM), to map the occurrence of the soil mattic horizon in the northeastern Qinghai-Tibetan Plateau using readily available ancillary data. The mechanisms of resampling and ensemble techniques significantly improved prediction accuracies (measured based on area under the receiver operator characteristic curve score (AUC)) and produced more stable results for the BRT (AUC of 0.921 ± 0.012, mean ± standard deviation) and RF (0.908 ± 0.013) algorithms compared to the CART algorithm (0.784 ± 0.012), which is the most commonly used machine learning method. Although the SVM algorithm yielded a comparable AUC value (0.906 ± 0.006) to the RF and BRT algorithms, it is sensitive to parameter settings, which are extremely time-consuming. Therefore, we consider it inadequate for occurrence-distribution modeling. Considering the obvious advantages of high prediction accuracy, robustness to parameter settings, the ability to estimate uncertainty in prediction, and easy interpretation of predictor variables, BRT seems to be the most desirable method. These results provide an insight into the use of machine learning algorithms to map the mattic horizon and potentially other soil diagnostic horizons.

Nitrate accumulated deep (>100 cm) in the regolith (soil and saprolite) threatens groundwater quality, but most studies focus only on nitrate nearer the surface (

Soil colour can indicate soil physical, chemical and biological properties and processes, and is an important indicator for soil classification, soil quality evaluation and soil management. It varies in both horizontal and vertical dimensions, and thus regional maps of soil colour can reveal spatial patterns of these properties, processes, and indicators. However, although soil regions are sometimes named for their dominant soil colour, it is directly measured only at “point” support, i.e., during soil profile description, whereas it is desirable to know soil colour over the entire soilscape. To achieve this for China we used predictive soil mapping methods to produce soil colour maps (dry and moist colours) at 1 km2 grid cell size and over multiple depths from a consistent dataset of approximately 4 600 full profile descriptions taken as part of a national survey to define soil series in Chinese Soil Taxonomy, and a set of environmental covariates covering the national territory. The covariates characterized soil forming factors including climate, parent materials, terrain, vegetation, land surface water and thermal conditions. Soil colour descriptions in the Munsell system were extracted from the genetic horizon descriptions at the selected depths and converted to the sRGB and L*a*b* colour spaces. Dry and moist colour separates were not well-correlated in either space ( r 0.76 ). Random forest models were constructed in both spaces, for dry and moist colours separately. Models in sRGB space were moderately successful ( R 2 ≈ 0.43 , RMSE ≈ 26 / 255 ) at 5 cm, with success decreasing with depth. Models smoothed the colour space and thus did not predict the more extreme values or chromas, nor the rarer hues. Models in L*a*b* space were less successful. The fitted sRGB models were used to produce predictive maps over all of China. Regional patterns as well as local detail are clearly shown. Solar radiation, wind exposure, regolith thickness, and Landsat TM bands 7 and 5 contributed most to the predictions, followed by elevation, mean annual precipitation, terrain wetness index, air temperature seasonality, precipitation standard deviation and standard deviation of NDVI. These suggest pedological processes acting on the development of soil colours, including weathering of parent materials, oxidation-reduction chemistry and biochemistry of the decomposing of organic matter. This study shows that predictive methods from points using suitable covariates are an alternative to spatial predictions over map units from their representative profiles.

Visible‐near infrared (vis–NIR) spectroscopy can reveal various soil properties and facilitate soil classification. However, few studies have attempted to classify vertical soil profiles that contain several genetic horizons. Here, we propose the ‘multiple objectives mixed support vector classification’ (MOM–SVC) method to classify soil profiles. A total of 130 soil profiles were collected from genetic horizons in Zhejiang Province, China. After laboratory analysis, soil profiles were classified according to the Chinese Soil Taxonomy system. Vis–NIR spectra were recorded from each genetic horizon of each soil profile and were then pre‐processed. We performed the MOM–SVC method as follows: (i) created a support vector machine (SVM) model (one‐versus‐one approach) using spectral data from all soil horizons in calibration profiles, (ii) applied the SVM model on each horizon of the profile to be predicted, (iii) extracted ‘votes’ from each horizon and mixed (or summarized) them into the votes of each profile to be predicted and (iv) classified each profile by the majority‐voting method. We also investigated whether the additional input of auxiliary soil information (e.g. moist soil colour, soil organic matter and soil texture), which could be measured easily or be well predicted by vis–NIR spectroscopy, could improve the accuracy of soil classification when combined with it. Independent validation results showed that the MOM–SVC method performed better at the soil order level than at the suborder level. Adding auxiliary soil information to the classification model improved the overall accuracy of classification at the soil order level. The proposed MOM–SVC method provides a fast objective diagnostic of soil classes for use in soil surveys and can help to update soil databases when a more objective soil classification system is developed. HIGHLIGHTS: The MOM–SVC method can be used to classify soil profiles objectively with a variety of soil horizons. Stratified random sampling was used to quantify prediction uncertainty in classification MOM–SVC can predict soil orders with greater accuracy than suborders. Adding auxiliary soil information into the classification model improved prediction accuracy.

Up-to-date maps of soil organic carbon (SOC) concentrations can provide vital information for monitoring global or regional soil C changes and soil quality. In this study, a national soil dataset collected in the 2010 s was applied to produce SOC maps of mainland China at soil depths of 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and 100–200 cm. A stacking ensemble learning framework was utilized to take advantage of the optimal predictions from individual models. A voting-based ensemble learning model (VELM) was proposed with consideration of pedoclimatic zones. In this model, three machine learning models were separately trained for every pedoclimatic zone, and their predictions were selectively merged together. A weighted ensemble learning model (WELM), in which the parameterization considered all zones (i.e., the whole study area) simultaneously, was also trained for comparison. The overall R2 values of these two methods ranged from 0.16 to 0.57 and decreased with depth. Based on the independent validation, the R2 values ranged from 0.41 to 0.57 in the topsoil (0–5 cm, 5–15 cm and 15–30 cm). Overall accuracy metrics implied that the VELM and WELM yielded nearly the same prediction performances. However, model validation in the pedoclimatic zones showed that the VELM obviously outperformed the WELM, with the VELM generally improving the accuracy by 12.6%. Based on the independent validation, we also compared our predictions with other soil map products. Although the spatial patterns were similar, the predicted SOC maps outperformed two other products. The comparison of the two ensemble models should serve as a reminder that if new national or regional soil maps are generated, validation based on pedoclimatic zones or other soil-landscape units may be necessary before applying these maps.

A paddy soil chronosequence consisting of four profiles derived from Quaternary red clay (QRC) in Southern China with cultivation history from 0 to 300 years was studied to understand the underlying mechanisms and processes controlling the centennial scale Fe evolution during anthropedogenesis. We evaluated the chronosequencial changes in depth distribution of Fe oxides and Fe isotopic compositions. Results showed that paddy soil development on QRC over a centennial time scale caused increasing profile differentiation of Fe oxides and measurable Fe isotope fractionation. Total Fe and oxide bound Fe as well as the maximum Fe illuviation depth decreased as paddy soils age, leading to significant differences in morphological properties between paddy and non-paddy soils with the same parent material. Selective extractions showed that the weakly-bound, oxide-bound and silicate bound Fe corresponded to 4–18%, 49–81%, and 11–40% of the total Fe, respectively, and these proportions varied with both time and depth due to the redox-related Fe transformation and redistribution. δ56Fe values in the studied paddy soil chronosequence ranged from 0.05‰ to 0.26‰ and exhibited a strong negative correlation with the logarithm of total Fe concentrations, suggesting mass-dependent Fe isotope fractionation occurred as a result of the preferential removal of lighter Fe isotopes during Fe leaching loss under the predominant reducing conditions. However, the Fe isotopic ratio of a specific paddy soil horizon was a result of a complex interaction of different processes, which were summarized and interpreted in our proposed conceptual model of Fe evolution in terraced paddy soils. Further investigations of Fe isotope signatures in the soil-plant-water system with a combination of laboratory simulation and prediction of Fe isotope fractionation under different pedogenic processes are needed to evaluate the relative contribution of multiple processes to Fe isotope fractionation during anthropedogenesis of paddy soils.

Knowledge of soil carbon (C) distribution and its relationship with the environment can improve our understanding of its biogeochemical cycling and help to establish sound regional models of C cycling. However, such knowledge is limited in environments with complex landscape configurations. In this study, we investigated the vertical distribution and storage of soil organic carbon (SOC) and soil inorganic carbon (SIC) in the 10 representative landscapes (alpine meadow, subalpine shrub and meadow, mountain grassland, mountain forest, typical steppe, desert steppe, Hexi Corridor oases cropland, Ruoshui River delta desert, Alxa Gobi desert, and sandy desert) with contrasting bioclimatic regimes in the Heihe River Basin, Northwest China. We also measured the 87Sr/86Sr ratio in soil carbonate to understand the sources of SIC because the ratio can be used as a proxy in calculating the contribution of pedogenic inorganic carbon (PIC) to total SIC. Our results showed that SOC contents generally decreased with increasing soil depth in all landscapes, while SIC contents exhibited more complicated variations along soil profiles in relation to pedogenic processes and parent materials at the various landscapes. There were significant differences of C stocks in the top meter among different landscapes, with SOC storage ranging from 0.82 kg C/m2 in sandy desert to 50.48 kg C/m2 in mountain forest and SIC storage ranging from 0.19 kg C/m2 in alpine meadow to 21.91 kg C/m2 in desert steppe. SIC contributed more than 75% of total C pool when SOC storage was lower than 10 kg C/m2, and the proportion of PIC to SIC was greater than 70% as calculated from Sr isotopic ratio, suggesting the critical role of PIC in the C budget of this region. The considerable variations of SOC and SIC in different landscapes were attributed to different pedogenic environments resulted from contrasting climatic regimes, parent materials and vegetation types. This study provides an evidence for a general trade-off pattern between SOC and SIC, showing the compensatory effects of environmental conditions (especially climate) on SOC and SIC formation in these landscapes. This is largely attributed to the fact that the overall decrease in temperature and increase in precipitation from arid deserts to alpine mountains simultaneously facilitate the accumulation of SOC and depletion of SIC.

Plants play an important role in Silicon (Si) cycling of terrestrial ecosystems. However, how plant Si is related to soil Si translocation and transformation in subtropical areas with ample weatherable silicates and agricultural activities is largely unknown. Three typical watersheds with different cropland and forest configurations in subtropical China were selected to investigate the biomass of different plant species, including coniferous forest, bamboo and paddy, as well as the geochemical composition of soil, rock, vegetation and water. The main soil types are Lithic Udorthents, Lithic Dystrudepts and Typic Epiaquepts derived from granite. Si concentrations in rock, soil, plant and water, as well as Si cycling fluxes, were measured. The results show that although a large amount of Si is cycled by plants, weathering and runoff contribute similarly to, or even more significantly than, plant cycling. High Si bio-accumulators assimilate and return more Si than low Si bio-accumulators, and also cause more phytolith-Si to be added to the surface soil, which decreases desilification rate of surface soil. Agricultural activities increase silicate weathering. Stream water Si concentrations are not affected by plant Si cycling. Thus, both agricultural activities and plant species affect Si translocation. In contrast to results from strong tropical weathering environments, Si cycling by plants in this subtropical area with abundant weatherable silicates does not dominate Si loss from soil; rather, it promotes silicate weathering and controls Si redistribution in soil profiles.

A paddy soil chronosequence consisting of five profiles derived from calcareous marine sediments with cultivation history from 0 to 1000 years was studied to understand the underlying mechanisms and processes controlling the millennial scale Fe evolution. We evaluated the chronosequencial changes in depth distribution of Fe oxide contents and Fe isotopic compositions. Results showed that paddy soil evolution under the influence of periodic flooding and groundwater fluctuation resulted with time in variations of soil moisture regime and redox condition that control Fe mobilization, translocation and redistribution, leading to enhanced profile differentiation of Fe oxides and measurable Fe isotope fractionation. Total Fe and oxide bound Fe as well as their differentiation between surface and subsurface horizons increased as paddy soils age, leading to the formation of diagnostic horizons and features characterizing Fe distribution and redistribution. Selective extractions showed that the weakly-bound, oxide-bound and silicate bound Fe corresponded to 1–16%, 8–46%, and 52–91% of the total Fe, respectively, and these proportions varied with both time and depth due to the redox-related Fe transformation and translocation. δ56Fe values in the studied paddy soil chronosequence ranged from − 0.01‰ to 0.18‰ and exhibited a strong negative correlation with the logarithm of total Fe concentrations, suggesting mass-dependent Fe isotope fractionation occurred as a result of the preferential removal of lighter Fe isotopes during long-term paddy soil evolution under the predominant reducing conditions. However, the Fe isotopic ratio of a specific paddy soil horizon was a result of a complex interaction of different processes, which were summarized and interpreted in our proposed conceptual model. Comparison of Fe isotopic compositions in the worldwide soils demonstrated that Fe isotopes can evidence Fe transfer and pinpoint the factors and processes that control Fe mobilization and redistribution particularly in soils with changing moisture regimes and redox conditions. Our findings provide new insights into the behavior and geochemical cycle of Fe at the Earth's surface strongly affected by human activities and contributes to an improved understanding of how anthropedogenesis affects Fe evolution in the Earth's Critical Zone.

Loess, as an allochthonous material, plays a significant role in the formation of Pleistocene periglacial slope deposits (PPSD), and thus also for Holocene soil formation, in the central European low mountain range. Although the differing loess proportions in different layers of the PPSD have been well recognized, quantitative distribution and mixing processes of loess in PPSD are still not fully understood. Detailed particle-size distribution was applied for quantifying loess proportions in PPSD in a sandstone area in central Germany. The bimodal pattern of the particle-size distribution curves clearly reflected loess and sandstone-weathering material as two components of the loess-affected layers, which allowed for the quantification of these two components by applying a two-endmembers unmixing model. By inspecting variations of particle-size distribution curves both vertically and spatially, we gained insights into the mixing processes of loess in PPSD and Holocene colluvium. Our results suggest that the loess component in upper layers most likely results from the incorporation of preexisting loess. Holocene colluvium and upper layers were thoroughly homogenized by various earth-surface processes. Radiocarbon dating of charcoal indicated strong human impact during historic times. Close correlation between the overall thickness of loess-containing layers and the depth-weighted loess content pointed to the erosional-depositional history at each site. Our research demonstrates the utility of detailed particle-size data for detecting and interpreting geomorphological processes and landscape evolution. It shows that the spatial distribution patterns of loess are subjected not only to topographic factors controlling primary loess deposition, but also to erosional-depositional histories and anthropogenic impact.

Accurate estimation of soil carbon is essential for accounting carbon cycling on the background of global environment change. However, previous studies made little contribution to the patterns and stocks of soil inorganic carbon (SIC) in large scales. In this study, we defined the structure of the soil depth function to fit vertical distribution of SIC based on pedogenic knowledge across various landscapes. Soil depth functions were constructed from a dataset of 99 soil profiles in the alpine area of the northeastern Tibetan Plateau. The parameters of depth functions were mapped from environmental covariates using random forest. Finally, SIC stocks at three depth intervals in the upper 1 m depth were mapped across the entire study area by applying predicted soil depth functions at each location. The results showed that the soil depth functions were able to improve accuracy for fitting the vertical distribution of the SIC content, with a mean determination coefficient of R2 = 0.93. Overall accuracy for predicted SIC stocks was assessed on training samples. High Lin's concordance correlation coefficient values (0.84–0.86) indicate that predicted and observed values were in good agreement (RMSE: 1.52–1.67 kg m− 2 and ME: − 0.33 to − 0.29 kg m− 2). Variable importance showed that geographic position predictors (longitude, latitude) were key factors predicting the distribution of SIC. Terrain covariates were important variables influencing the three-dimensional distribution of SIC in mountain areas. By applying the proposed approach, the total SIC stock in this area is estimated at 75.41 Tg in the upper 30 cm, 113.15 Tg in the upper 50 cm and 190.30 Tg in the upper 1 m. We concluded that the methodology would be applicable for further prediction of SIC stocks in the Tibetan Plateau or other similar areas.

To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.

Multi-scale soil variations are increasingly employed to improve the accuracy for digital soil mapping (DSM). In this study, we attempted to explore a methodology of wavelet analysis on this topic. The terrain attributes of a study area were decomposed using the wavelet analysis, and the resulted components were applied to map soil organic carbon (SOC) content, pH and clay content using multiple linear regression (MLR) and regression kriging (RK). The results showed that the wavelet components strengthened soil-landscape relationships in terms of correlation coefficients, enhanced soil-landscape modelling in terms of MLR modelling coefficients of determination (R2). Compared with several standard DSM approaches, i.e., ordinary kriging (OK), MLR and RK with the original terrain attributes, the use of wavelet components improved the prediction accuracy at some scales, but not all the scales. Most of the improvements were at the slight to moderate levels, e.g., 3.66–14.24% increases in the accuracy based on mean error, mean absolute error, root mean square error and R2. Maps made with wavelet components were relatively smooth and sometimes contained hotspots due to characteristics of wavelet components, which differed a lot from those made by the standard DSM methods. The potential benefits of using wavelet components as predictors in DSM may be further revealed in the future when more predictor selection approaches and mapping methods are considered.

Soil class maps are useful representations of the landscape distribution of holistic soil functions. However these are often only available as generalized classes at small cartographic scales. One reason is that allocating a soil profile to a class in most current soil classification system requires laboratory determination of many diagnostic soil properties. The advantage of reflectance spectroscopy along with the development of spectral libraries can provide a relatively low-cost solution to this problem. Reflectance spectroscopy has demonstrated its ability to rapidly predict soil physio-chemical properties; however prediction accuracy varies among soil properties. When properties predicted with different accuracies are used to substitute for traditional laboratory determinations in allocating a soil profile to a class, the resulting reliability of the allocation is questionable. The objective of this research is to explore whether the soil properties predicted by reflectance spectroscopy can be used to correctly allocate soil profiles into soil taxa at different hierarchical levels. Two hundred and six soil profiles were allocated to eight Orders, 12 Suborders, 23 Groups and 49 Subgroups according to Chinese Soil Taxonomy, with the help of ten soil properties predicted by spectra using ten-fold cross-validated PLSR modelling. The overall allocation accuracy at Order, Suborder, Group and Subgroup level was 98.5%, 98.5%, 87.7% and 76.0% respectively. These results show that soil reflectance spectroscopy can assist in allocation of profiles. When predicted soil properties with varying accuracy are used for soil allocation, propagation of prediction errors and model uncertainties must be considered. We propose the use of multiple indicators (RPD, confidence intervals, comparison of RMSE and threshold requirements) to evaluate the allocation results.

Changes in soil organic matter (SOM) can affect food security, soil and water conservation, and climate change. However, the drivers of changes in SOM in paddy soils of China are not fully understood because the effects of agricultural management and environmental factors are studied separately. Soil, climate, terrain, and agricultural management data from 6 counties selected based on representative soil types and cropping systems in China were used in correlation analysis, analysis of variance, and cforest modeling to analyze the drivers of changes in SOM in paddy soils in the Middle and Lower Yangtze River Plain from 1980 to 2011. The aims of this study were to identify the main factors driving the changes in SOM and to quantitatively evaluate their individual impacts. Results showed that the paddy SOM stock in the study area increased by 12.5% at an average rate of 0.023 kg m−2 year−1 over the 31-year study period. As a result of long-term rice planting, agricultural management practices had a greater influence than soil properties, climate, and terrain. Among the major drivers, straw incorporation, the most influential driver, together with fertilization and tillage practices, significantly increased the accumulation of SOM, while an increase in temperature significantly influenced SOM decomposition. Therefore, to confront the challenge of rising temperatures, it is important to strengthen the positive effects of agricultural management. Rational fertilizer use for stabilizing grain production and crop straw incorporation are promising measures for potential carbon sequestration in this region.

This paper was oriented to characterise spatio-temporal variation of soil organic carbon (SOC) over a 30-year period and to explore its driving forces in East China. A comparative study was done of data obtained during 1980 and 2010 in Anhui Province. Spatio-temporal changes of SOC density and storage in the surface (0–30 cm) and 0–100 cm layers were analysed using geographic information system (GIS) spatial analysis techniques. Relationships between soil erosion, land use change, agricultural management, and changes in SOC are discussed. Results show: (1) over 30 years, the average SOC density decreased by 0.59 kg C m−2 in the surface layer and 1.63 kg C m−2 in 0–100 cm layer. The average SOC density increased in Calcaric Cambisols and Haplic Fluvisols, and decreased in other soil types.The average SOC density in upland increased but decreased as a result of other land use practices. (2) SOC density in the surface layer increased to the north but decreased to the south in Anhui. SOC density in the 0–100 cm layer increased in the central area and decreased to the south. About 59% of all soil increased in SOC density. (3) SOC storage decreased by 117.91 Tg C in the surface layer and decreased by 237.65 Tg C in the 0–100 cm layer. The SOC storage increased mainly in the northern and central areas. (4) Severe soil erosion was a main reason for the large reduction in SOC storage in western and southern Anhui. Cultivated land use that changed to paddy field from upland improved SOC more than changes to other land use types. SOC storage change in farmland was significantly and positively related to organic matter content in crop roots.

High-precision digital soil organic carbon (SOC) stocks mapping is very important for agricultural production management and global carbon cycle. The spatial heterogeneity of farmland SOC is not only influence by the environmental factors of soil formation but also the management practices of tillage, fertilization, and irrigation. However, the traditional modeling covariates of digital soil mapping, such as terrain factors, land use types and climate factors have weak spatial variations in low-relief agricultural areas, and they cannot reflect the large spatial variation of SOC. Thus the time-series multispectral remote sensing images will be used for mapping soil properties in low relief regions in this study, meanwhile a new collaborative verification strategy was put forward to evaluate the spatial distribution characteristics of soil maps. The current study was performed in a nearly flat agricultural region southeast of Iowa (with an area of approximately 385.45 ha), where 195 surface soil samples (0–15 cm) were collected. A hyperspectral image (Headwall-Hyperspec, 380–1700 nm) and the time-series multispectral remote sensing images of Sentinel 2 and Landsat 8 were used to construct the prediction models of SOC stock and its relevant soil properties of SOC and soil bulk density (SBD) through partial least square regression (PLSR) and extreme learning machine (ELM) models. The collected soil samples and evaluation indexes of root mean square error (RMSE), R2, and ratio of performance to interquartile range (RPIQ) were used to evaluate the model performance. Results are as follows: (1) hyperspectral images were successfully used to predict the SOC stock, SOC, and SBD through PLSR and ELM, while ELM (RPIQ = 2.03, 1.97, 1.64) outperformed PLSR (RPIQ = 1.83, 1.97, 1.53); (2) the time-series multispectral remote sensing images of Sentinel 2 and Landsat 8 can reflect the spatial distribution characteristics of the SOC stock, SOC and SBD by PLSR and ELM, but the combination of Sentinel 2 images and ELM obtained the best prediction results (RPIQ = 1.45, 1.25, 1.26); and (3) the differences of the soil maps predicted by the hyperspectral image and time-series multispectral remote sensing images were small, and the largest percentage errors nearly appeared on the edges of the farmland patches owing to mixed pixels. This study further confirmed the good prediction abilities of the time-series multispectral remote sensing images in low relief farmland regions. Lastly, this mapping strategy can provide additional valuable information for agricultural management and carbon cycle.

Increasing acid deposition and intense nitrogen (N) fertilization have resulted in severe soil acidification in many regions of the world. However, the soil acidification rate, especially quantitative contributions of different proton (H+) sources remain unclear in various cropping systems. In this study, a two-year field lysimeter experiment was conducted with a set of different N fertilization treatments (0, 100, 200 and 400 kg N ha−1 yr−1) for maize crop in typical red soil in subtropical China. The pathways and budgets of different H+ sources were quantified by calculating the input–output balance of major elements. Results showed that fertilization significantly accelerated soil acidification by increasing the leaching of NO3– and base cations (K+, Ca2+, Na+, and Mg2+) as well as increasing the cation removals by plant uptake and harvest. Unbalanced plant uptake of anions and cations was the primary H+ source (64.4–80.5%) to soils. Plants have an important effect on soil acidification by redistributing cations in soils. Due to cation removals by plant uptake and harvest, there were significant decreases of soil exchangeable base cations under high urea-N treatments (200 and 400 kg N ha−1 yr−1) and significant increases of soil exchangeable H+ and Al3+ under three fertilization treatments. Of course, N transformation also plays an important role (12.1% to 38.8%). However, the effect of direct H+ deposition is minor in this area comparatively (5.78% to 7.34%). Quantification of acidification contribution of different sources is useful for the establishment of further soil remediation practices. The field management is considered very important to alleviate soil acidification, including increasing straw return and controlling fertilizer application.

Mattic epipedon (ME), a special diagnostic surface horizon for soils in alpine meadow, plays an important role in carbon storage, soil water retention, and indication of alpine meadow degradation. At the global scale, it mainly distributes in the Qinghai-Tibetan Plateau and other similar alpine environments. However, its spatial patterns and the relations with environmental conditions remain unknown. This study attempts to explore key environmental variables responsible for the occurrence of the special surface soil horizon and based on those factors to predict and map digitally the spatial distribution of ME. By combining a variable selection procedure and the Random Forest (RF) algorithm, the variables extracted from Landsat 5 TM mosaic image, ASTER GDEM, and climate data were optimized and their importance was measured. The classification accuracy was compared with that obtained from binary logistic regression algorithm. In addition, a land use/land cover (LULC) map-based modification was conducted to further improve the classification accuracy. Results showed that the variable selection procedure had little effect in improving prediction accuracy. However, the number of used variables markedly reduced from 26 to 6 with a 77% decrease, which could speed up the training of the RF model (about one-third of the computation time could be saved). Analysis of variable importance showed that band 3 of TM and normalized difference vegetation index were the most important environmental variables influencing the occurrence of ME. The final overall accuracy of the ME map was predicted to be 84%. Our results demonstrate that the proposed procedure, which combined the proposed variables (derived from remote sensing, GDEM, and climate data), the variable selection approach, the RF algorithm, and the LULC map-based modification method can identify key environmental variables influencing the occurrence of ME and map the spatial distribution of ME effectively on the Qinghai-Tibetan Plateau.

Local terrain attributes, which are derived directly from the digital elevation model, have been widely applied in digital soil mapping. This study aimed to evaluate the mapping accuracy of soil organic carbon (SOC) concentration in 2 zones of the Heihe River in China, by combining prediction methods with local terrain attributes derived from different polynomial models. The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice, rather than how morphometric variables and their geomorphologic interpretations are understood and calculated. In this study, 2 neighborhood types (square and circular) and 6 representative algorithms (Evans-Young, Horn, Zevenbergen-Thorne, Shary, Shi, and Florinsky algorithms) were applied. In general, 35 combinations of first- and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods (i.e., kriging with an external drift and geographically weighted regression). The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography. Among the different combinations of first- and second-order derivatives used, there was a best combination with a more accurate estimate. For different prediction methods, the relative improvement in the two zones varied between 0.30% and 9.68%. The SOC maps resulting from the higher-order algorithms (Zevenbergen-Thorne and Florinsky) yielded less interpolation errors. Therefore, it was concluded that the performance of predictive methods, which incorporated auxiliary variables, could be improved by attempting different terrain analysis algorithms.

Calcareous soils are those soils containing layers of carbonate accumulation formed by either secondary accumulation or inheritance of CaCO 3 from calcareous parent materials. These soils are widely distributed in arid, semi-arid and even some humid climate environments. However, in the Chinese Soil Taxonomy (CST, 3rd version), the soils that contain large amounts of CaCO 3 but do not meet requirements of Aridosols are classified as Hapli-Ustic Cambosols. This group also includes many non-calcareous soils and those soils strongly affected by secondary carbonate accumulation together with quite dissimilar soils in terms of their morphology and properties. This study was conducted to determine calcification patterns and calcic processes occurring in representative pedons of calcareous soils in northwestern Henan Province, China and to classify these soils in CST. A Calcic subgroup was proposed to add within the Hapli-Ustic Cambosols. A diagnostic key was also provided to separate this subgroup from the Typic subgroup. In this way the different calcification degrees of soils were better reflected in the classification, which should lead to more uniform interpretive groups for better soil management.

Summary Holocene loess has been recognized as the primary source of the silty topsoil in the northeast Qinghai-Tibetan Plateau. The processes through which these uniform loess sediments develop into diverse types of soil remain unclear. In this research, we examined 23 loess-derived soil samples from the Qilian Mountains with varying amounts of pedogenic modification. Soil particle-size distribution and non-calcareous mineralogy were changed only slightly because of the weak intensity of chemical weathering. Accumulation of soil organic carbon (SOC) and leaching of carbonate were both identified as predominant pedogenic responses to soil forming processes. Principal component analysis and structural analysis revealed the strong correlations between soil carbon (SOC and carbonate) and several soil properties related to soil functions. Accretion of SOC effectively decreased soil bulk density (R2 = 0.81) and increased cation exchange capacity (R2 = 0.96), soil water retention at saturation (R2 = 0.77), field capacity (R2 = 0.49) and wilting point (R2 = 0.56). These results indicate that soil ecological functions are strengthened during pedogenic modification of such loess sediments. Soil C/N ratio was constant at small SOC contents, but after reaching a threshold of approximately 35 g kg−1 SOC, soil C/N increased linearly with SOC. This indicates a change from a carbon-limited loess ecosystem in arid regions to a nitrogen-limited one in alpine settings. This research suggests that loess sequences within environmental gradients offer great potential as natural experiments to explore intrinsic soil behaviour and ecosystem evolution because the effect of parent material is well constrained. Highlights We examined pedogenic modifications of loess with uniform origin from contrasting environments. Accumulation of SOC and depletion of carbonate coincide during pedogenesis of loess-derived soil. Pedogenesis underpins functional evolution of loess-derived soil across the Qilian Mountains. Loess sequences provide ideal natural experiments to study soil and ecosystem evolution.

Soil organic phosphorus transformation during ecosystem development exerts a crucial influence on soil fertility and ecosystem properties. This paper reviews the use of solution 31P NMR spectroscopy for characterizing organic phosphorus speciation in soil chronosequence and long-term field experiments in order to improve our understanding of the temporal changes, fundamental processes, and associated natural and anthropogenic controls of organic phosphorus transformation during long-term ecosystem evolution. Published soil chronosequence studies show that organic phosphorus compounds under aerobic conditions are dominated by phosphate monoesters (occurred mainly as inositol phosphates) followed by phosphate diesters (occurred mainly as DNA) and phosphonates, irrespective of the different parent materials, vegetation covers and climatic conditions. This contrasted markedly with wetland soils in which phosphate monoesters and diesters maintained approximately equal proportions, which is attributed to the limited reactive clay surfaces for stabilization and/or decomposition of myo-inositol hexakisphosphate under frequent anaerobic conditions. Most organic phosphorus compounds in soil chronosequences increase with age to reach a maximum and then decline with time, although the apex varies significantly among different organic phosphorus compounds and chronosequences. Variations of the potential for phosphorus stabilization resulting from mineralogical transformation, changes in phosphorus sources due to shifts in plant and microbial communities, and differences in the biological utilization of various phosphorus compounds have been suggested as three main mechanisms controlling the temporal changes in organic phosphorus species, abundance and availability during natural ecosystem development. In agricultural soils, the amounts, forms, and dynamics of organic phosphorus are determined by internal soil properties, external environmental conditions and managements, including the history and intensity of land use, different tillage practices and fertilizer treatments. These mechanisms are interlinked and more research is required to isolate both internal and external factors that regulate organic phosphorus transformation in agricultural ecosystems. Given the universal dependence on organic phosphorus for life and its critical roles in biogeochemical cycling, we put forward several open questions that need to be resolved in the future studies by emphasizing the multidisciplinary collaborations, the use of multiple analytical techniques and the establishment of quantitative organic phosphorus transformation models.

Evaluating the accuracy of allocation to classes in monothetic hierarchical soil classification systems, including the World Reference Base for Soil Classification, US Soil Taxonomy, and Chinese Soil Taxonomy, is poorly-served by binomial methods (correct/incorrect allocation per evaluation observation), since some errors are more serious than others in terms of soil properties, map use, pedogenesis, and ease of mapping. Instead, evaluations should account for the taxonomic distance between classes, expressed as class similarities, giving partial credit to some incorrect allocations. These can then be used in weighted accuracy measures, either direct measures of agreement or measures that account for chance agreement, such as the tau index. Similarities can be determined in one of four ways: (1) by the expert opinion of a soil classification specialist; (2) by the distance between classes in a numerical taxonomy assessment; (3) by distance within a taxonomic hierarchy; or (4) by an error loss function. Expert opinion can be from the point of view of the map user, to assess map utility, or map producer, to assess mapping skill. Examples are given of determining similarity between a subset of Chinese Soil Taxonomy classes by expert opinion and by numerical taxonomy from soil spectra, and then using these for weighted accuracy assessment. A method for assessing the accuracy of probabilistic predictions of several classes at a location is also proposed.

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps: soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales, could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon (SOC) at 0–20 and 20–40 cm depths in a study area of 5 900 km 2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c -means clustering was applied to two climate and four terrain variables in each stratum. The clustering results (environmental cluster chains) were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error (RMSE). The declining rates of RMSE with the addition of samples slowed down for 20–40 cm depth, but fluctuated for 0–20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20–40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soil parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.

The parental material of soils in the Qilian Mountains of northwest China is mainly aeolian loess containing CaCO3 which may remain in soils under the semiarid-arid climate. To disclose the CaCO3 characteristics change with the altitude and the terrain attributes, we surveyed 18 soil profiles in an altitude sequence from 3076 m to 4510 m in the Hulugou Watershed in the Qilian Mountains, measured CaCO3 contents of all genetic horizon samples, analyzed the densities, illuviation modes and depths of CaCO3 in the profiles, extracted values of the terrain attributes of the profiles including altitude slope, aspect, plane curvature, profile curvature and terrain wetness index (TWI) from the 90 m resolution SRTM3 DEM data on ArcGIS 9.3 platform. We found that CaCO3 weighted content of the profiles ranged from 1.30 g·kg-1 to 93.09 g·kg-1, CaCO3 density from 0.05 kg/m2 to 75.69 kg/m2, CaCO3 illuviation depth from 12 cm to 54 cm. CaCO3 illuviation modes could be divided into three types, i.e., no illuviation mode in which the profile has only A horizon or CaCO3 content -1, middle illuviation mode in which CaCO3 accumulated in a middle horizon, and down illuviation mode in which CaCO3 content increases with the depth. CaCO3 weighted content, density and illuviation depth had significant correlation with certain terrain attributes. In general, the altitude sequence is an effective way to study CaCO3 characteristics in the alpine region, and the data of terrain attributes which can influence the precipitation and its redistribution in soil are potential in predicting soil CaCO3 characteristics in the alpine region.

Plant growth contributes to mineral weathering, but this contribution remains poorly understood. Weathering rates in an aggrading forested watershed in subtropical China were studied by means of geochemical mass balance. Rainfall, dry deposition, and streamwater were monitored from March 2007 to February 2012. Samples of vegetative components, rainfall, dry deposition, streamwater, representative soils, and parent rock were collected and determined for mass balance calculation and clarifying plant-driven weathering mechanisms stoichiometrically. Ignoring biomass, weathering rates of Ca 2+ , Mg 2+ , Na + , and Si were 25.6, 10.7, 2.8, and 51.0 kg ha −1 year −1 , respectively. Taking biomass into consideration, weathering rates of Ca 2+ , Mg 2+ , and Si and the sum of weathering rates of Ca 2+ , Mg 2+ , Na + , K + , and Si were 2.6, 1.8, 1.2, and 1.5-fold higher than those ignoring biomass, respectively. This is attributed to plant-driven weathering due to the nutrient ( e.g ., Ca 2+ , Mg 2+ , and K + ) absorption by vegetation and substantial proton production during assimilation of these nutrients, with the former acting as a pump for removing weathering products and the latter being a source of weathering agents solubilizing mineral components. The same pattern of weathering, i.e ., higher rates of weathering with than without including biomass in mass balance calculation, was reported in previous studies; however, the extent to which plants drive weathering rates varied with vegetation types and climatic zones. The documented biological weathering driven by plants is expected to play a critical role in regulating nutrient cycling and material flows within the Earth's Critical Zone.

Visible near-infrared (VNIR) spectra can provide rich information on soil physical and chemical properties, which implies the possibility of using soil spectra to aid in the discrimination of soil types. Pedological soil classification systems use a selected set of soil properties to identify diagnostic horizons and features, and to build a classification key. This research explored the application of VNIR spectra to classify typical soil profiles collected in Anhui province, China. The 279 soil profiles used are classified into five orders (Cambosols, Vertosols, Argosols, Primosols and Anthrosols), six suborders and 21 groups according to Chinese Soil Taxonomy. Soil spectra were collected within 350–2500 nm and principal component analysis (PCA) was applied to reduce data dimension. These principal components were used as independent variables in multinomial logistic regression for soil classification. Topsoil spectra, subsoil spectra and their combination were compared for prediction accuracy. Accuracy achieved at the level of suborder using spectra of topsoil, subsoil and combined horizons were 76.3%, 71.3% and 70.3% respectively, while the results for the level of soil group using the topsoil horizon was 40.5%. Since topsoil spectra alone achieved a prediction accuracy of more than 75%, reflectance spectroscopy can be judged a promising tool for soil classification. Taxonomic distances between classes calculated on the basis of physio-chemical properties and spectra were quite different, showing that the concept of distance between classes in feature space depends on the features chosen for evaluation. Taxonomic distances can serve as a supplement for better selection and evaluation of prediction models.

The Qilian Mountains are generally capped by a productive silty soil layer wherever the environment allows. As Holocene loess is prevalent across the Qilian Mountains, we assume that at least some of these fine sediments are derived from loess deposition and that soils across the region may be genetically linked. To test these hypotheses, nine pedons in different landscapes within a typical alpine watershed of the Qilian Mountains were sampled. We also collected fine earth samples from glacier surfaces and crevices of glacial debris in the moraine zone. The particle-size distribution (PSD) is used as a proxy for identifying aeolian fractions in soils. The PSD curves of all of the fine-earth fractions examined are polymodal, although three modal sizes of roughly 16 μm, 35 μm and 80 μm are found in almost all sediments in varying proportions. These modal sizes have been identified as three sources of aeolian sediments in different transport systems. The composition and thickness of loess sediments are related to local site conditions and may be related to the paleoenvironmental history of the site. Clastic debris and vegetation cover serve as dust traps during different stages of soil formation in this alpine environment, and soils grow upward with accumulating loess sediment. Our study demonstrates that PSD curves can be used to determine the origins (and especially aeolian origins) of soils. Furthermore, this study provides insight into the pedology, ecology and paleoenvironmental history of loess-affected ecosystems in alpine areas of the Qinghai-Tibetan Plateau.

Due to the almost homogeneous topography in low relief areas, it is usually difficult to make accurate predictions of soil properties using topographic covariates. In this study, we examined how time series of field soil moisture observations can be used to estimate soil texture in an oasis agricultural area with low relief in the semi-arid region of northwest China. Time series of field-observed soil moisture variations were recorded for 132 h beginning at the end of an irrigation event during which the surface soil was saturated. Spatial correlation between two time-adjacent soil moisture conditions was used to select the factors for fuzzy c-means clustering. In each of the ten generated clusters, soil texture of the soil sample with the maximum fuzzy membership value was taken as the cluster centroid. Finally, a linearly weighted average was used to predict soil texture from the centroids. The results showed that soil moisture increased with the increase of clay and silt contents, but decreased with the increase of sand content. The spatial patterns of soil moisture changed during the entire soil drying phase. We assumed that these changes were mainly caused by spatial heterogeneity of soil texture. A total of 64 independent samples were used to evaluate the prediction accuracy. The root mean square error (RMSE) values of clay, silt and sand were 1.63, 2.81 and 3.71, respectively. The mean relative error (RE) values were 9.57% for clay, 3.77% for silt and 12.83% for sand. It could be concluded that the method used in this study was effective for soil texture mapping in the low-relief oasis agricultural area and could be applicable in other similar irrigation agricultural areas.

Water storage and flow in shallow subsurface drives runoff generation, vegetation water use and nutrient cycling. Modelling these processes under non-steady state conditions is challenging, particularly in regions like the subtropics that experience extreme wet and dry periods. At the catchment-scale, physically-based equations (e.g., Richards equation) are impractical due to their complexity, while conceptual models typically rely on steady state assumptions not found in daily hydrological dynamics. We addressed this by developing a simple modelling framework for shallow subsurface water dynamics based on physical relationships and a proxy parameter for the fluxes induced by non-unit hydraulic gradients. We demonstrate its applicability for six generic soil textures and for an Acrisol in subtropical China. Results showed that our new approach represents top soil daily fluxes and storage better than, and as fast as, standard conceptual approaches. Moreover, it was less complex and up to two orders of magnitude faster than simulating Richards equation, making it easy to include in existing hydrological models.

Subsurface soil bacterial community composition and the controlling factors remain largely unknown, especially the micro-zone differentiation of community composition within a horizon. We studied a plinthic horizon to determine how different micro-zones in a horizon affect the bacterial community. The plinthic horizon is a net-like horizon characterized by the segregation of iron forms as shown by contrasting red matrix and white veins, which share common macro-environmental conditions such as climate and land use but differ only in physical and chemical compositions. The studied horizon is typical of the red soils of southeastern China and is an important layer in the red soil Critical Zone. The plinthite is considered to have been formed in the Quaternary and thus is a record of the paleo-environment. We evaluated the difference in the bacterial community composition between the red matrix and white veins and explored the possible assembly mechanisms of their co-occurrence patterns. Compared to the eutrophic environments of a red matrix, higher relative abundances of Acidobacteria and Nitrospirae were observed in the white veins. Similarly, more niches led to a higher density of bacterial co-occurrence patterns in the red matrix. The differences in the bacterial community composition and association networks are due to environmental selection, including the legacy of the paleoclimate that is represented by major element contents and contemporary hydrological properties that are mainly controlled by the soil texture. Our study shows that micro-zones even within a same plinthic horizon can provide different habitats and thus select for specific bacterial communities. Furthermore, this study could improve our understanding of the differentiation of bacterial communities among microenvironments caused by both historical and contemporary processes and help to predict how these communities may respond to future environmental changes.

Ancillary data, such as soil type, may improve the visible and near-infrared (vis-NIR) estimation of soil organic carbon (SOC); however, they require data collection or expert knowledge. The application of a national soil spectral library to local SOC estimations usually requires soil type information, because the relationships between vis-NIR spectra and SOC from different populations may vary. Using 515 samples of five soil types (genetic soil classification of China, GSCC) from the Chinese soil spectral library (CSSL), we compared three strategies in the vis-NIR estimation of SOC. Different regression models were calibrated using the entire dataset (Strategy I, without using soil type as ancillary data) and the subsets stratified by soil type from CSSL as ancillary data (strategies II and III). In Strategy II, the subsets were stratified by soil type from the CSSL for validation. In Strategy III, the subsets were stratified by spectrally derived soil type for validation. The results showed that 86.72% of the samples were successfully discriminated for the soil types by using the vis-NIR spectra. The coefficients of determination in the prediction ( R p 2 ) of SOC estimation by strategies I, II, and III were 0.74, 0.83, and 0.82, respectively. The stratified calibration strategies (strategies II and III) improved the vis-NIR estimation of SOC. The misclassification of the soil type in the application of Strategy III slightly affected the SOC estimations. Nevertheless, this strategy is inexpensive and beneficial when expert knowledge on soil classification is lacking. We concluded that vis-NIR spectroscopy could be applied to distinguish some soil types in terms of GSCC, which further provided essential and easily accessible ancillary data for the application of stratified calibration strategies in the vis-NIR estimation of SOC.

Nitrate contamination of water bodies is a serious concern in regions with intensive agriculture. Previous studies, which only considered surface water and groundwater but not the regolith, do not provide a comprehensive accounting of nitrate sources and their proportional contributions in the Critical Zone, which extends from the top of the vegetation canopy down to the bottom of the fresh groundwater. In this study, dual nitrate isotopes, hydrochemical compositions and a Bayesian isotope mixing model were used to investigate nitrate sources, processes and individual source contributions across surface water, regolith and groundwater at the Red Soil Critical Zone Observatory of China. Denitrification was found to be weak in the Critical Zone. Dilution processes were important in eliminating nitrate from streamflow and groundwater. More than 90% of the nitrate inventory was stored at depths from 1 m to the bedrock surface in the upland regolith. Nitrate with light nitrogen (14N) isotopes was preferentially adsorbed by the variable charge soils with the help of weak electrostatic attraction. The contribution of soil organic N (SON) to the nitrate in the upland catchment streamflow was found to be highest with a mean of 56%, followed by manure and sewage (MS, 26%), NH4+ in chemical fertilizers (CF, 17%). For the paddy subcatchment streamflow, the contribution of SON (68%) was highest, followed by CF (19%) and MS (13%). In contrast, for the groundwater, the contributions of CF and SON were 60% and 36%, respectively. For the regolith, the contributions of CF and SON were 72% and 25%, respectively. Thus the most important sources of nitrate were SON for the streamflow and CF for the groundwater and regolith. These findings show that the different partitioning and isotopic fractionation within the regolith strongly affect the sources of nitrate reaching groundwater, compared to the sources reaching surface water.

The growing human population and demand for food have significantly impacted soil resources. Understanding the spatiotemporal change of soil conditions is important to support food production, environmental sustainability, and climate change adaptation. Nevertheless, spatiotemporal prediction of soil properties could be seriously influenced by the uncertainties of the data and model. Integrated Nested Laplace Approximation (INLA) with the Stochastic Partial Differential Equation (SPDE) was proposed as a general model that can account for the uncertainties in spatiotemporal soil modelling and prediction. INLA-SPDE has significant advantages in computation efficiency over commonly-used geostatistical methods with Markov Chain Monte Carlo. However, until now, only few pedometrics studies used it for soil spatial modelling. This study demonstrates an application of INLA-SPDE within a hierarchical spatiotemporal model for soil organic matter based on soil survey data collected in Jiangsu, China, during three periods, i.e., 1979–1982, 2000 and 2006–2007. Compared with updating digital soil maps using the Bayesian Maximum Entropy approach, the prediction generated using INLA-SPDE is more accurate. For example, the root mean square error using INLA-SPDE (i.e., 6.57 g kg−1) was reduced by 20% compared to the updating approach (i.e., 8.39 g kg−1). Moreover, accounting for sources of uncertainties made the prediction using INLA-SPDE more certain. Nevertheless, the uncertainty in the temporal prediction of soil change is still large due to the scarcity of data across the sampling periods. The INLA-SPDE model predicts much detailed spatiotemporal changes along the sampling periods. Therefore, this study recommends the use of INLA-SPDE within a hierarchical model as an effective method for studying spatiotemporal soil change.

The stability of soil colloids influences soil physicochemical properties, soil development, and transfer of nutrients and contaminants to surface and ground waters. A better understanding of soil colloids stability dynamics during soil evolution is important for the evaluation of soil's capacity to retain nutrients and/or accommodate toxic contaminants. This study was aimed to determine changes in the stability of water-dispersible soil colloids that accompany mineral transformation and surface charge evolution during pedogenesis using a well characterized chronosequence derived from basalt in the humid tropical region of Hainan Island, South China. The results demonstrated that the pH-dependent colloid stability decreased significantly with tropical soil development, which we attribute to the substantial changes in clay mineral compositions and colloid surface charge properties. Clay minerals in the studied chronosequence were characterized by an increase of kaolinite, gibbsite and Fe oxides and a decrease of quartz and halloysite towards more advanced stages of weathering, which resulted in the decline of permanent negative charges in the older soils. The point of zero charge (pHPZC) increased while ∆ pH decreased across the tropical soil chronosequence, being in good agreement with the observed lower colloid stability in aged soils dominated by kaolinitic minerals. Our study of colloid stability at long-term pedogenic time scale suggests young tropical soils (

Atmospheric deposition, a major pathway of metals entering into soils, plays an important role in soil environment, especially in urban regions where a large amount of pollutants are emitted into atmosphere through various sources. In order to understand the characteristics of atmospheric deposition in urban area and its relation with natural and anthropogenic sources, a three-year study of atmospheric deposition at three typical sites, industrial zone (IN), urban residential area (RZ) and suburban forested scenic area (FA), was carried out in Nanjing, a metropolitan city in eastern China from 2005 to 2007. The bulk deposition rate and element composition of atmospheric deposition varied spatio-temporally in the urban zones of Nanjing. The concentrations of Cu, Zn, Pb and Ca in the atmospheric deposits were strongly enriched in the whole Nanjing region; however, anthropogenic pollutants in atmospheric deposits were diluted by the input of external mineral dust transported from northwestern China. Source apportionment through principal component analysis (PCA) showed that the background atmospheric deposition at the FA site was the combination of external aerosol and local emission sources. The input of long-range transported Asian dust had an important influence on the urban background deposition, especially in spring when the continental dust from the northwestern China prevailed. Marine aerosol source was observed in summer and autumn, the seasons dominated by summer monsoon in Nanjing. In contrast, the contribution of local anthropogenic emission source was constant regardless of seasons. At the RZ and IN sites, the atmospheric deposition was more significantly affected by the nearby human activities than at the FA site. In addition, different urban activities and both the winter and summer Asian monsoons had substantial impacts on the characteristics of dust deposition in urban Nanjing.

Conventional soil maps generally contain one or more soil types within a single soil polygon. But their geographic locations within the polygon are not specified. This restricts current applications of the maps in site-specific agricultural management and environmental modelling. We examined the utility of legacy pedon data for disaggregating soil polygons and the effectiveness of similarity-based prediction for making use of the under- or over-sampled legacy pedon data for the disaggregation. The method consisted of three steps. First, environmental similarities between the pedon sites and each location were computed based on soil formative environmental factors. Second, according to soil types of the pedon sites, the similarities were aggregated to derive similarity distribution for each soil type. Third, a hardening process was performed on the maps to allocate candidate soil types within the polygons. The study was conducted at the soil subgroup level in a semi-arid area situated in Manitoba, Canada. Based on 186 independent pedon sites, the evaluation of the disaggregated map of soil subgroups showed an overall accuracy of 67% and a Kappa statistic of 0.62. The map represented a better spatial pattern of soil subgroups in both detail and accuracy compared to a dominant soil subgroup map, which was commonly used in practice. Incorrect predictions mainly occurred in the agricultural plain area and the soil subgroups that are very similar in taxonomy, indicating that new environmental covariates need to be developed. We concluded that the combination of legacy pedon data with similarity-based prediction is an effective solution for soil polygon disaggregation.