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Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.

As the thin layer at the Earth’s terrestrial surface, the critical zone (CZ) ranges from the vegetation canopy to the aquifer or the interface between saprolite and bedrock and varies greatly in space. In the last decade, much attention has been paid to the establishment of Critical Zone Observatories (CZOs) that focus on various aspects of CZ science over different time scales. However, to the best of our knowledge, few studies have explicitly contributed to CZ classification or regionalization; thus, the spatial patterns of similar CZs have not been clearly identified. This study proposed a three-category CZ classification scheme by integrating environmental factors that greatly affect the transfer of energy and mass in the Earth’s near-surface environment and thus dominate CZ formation and evolution, i.e., climate, parent material, soil type, groundwater table depth, geomorphology and land use. The main goal was to highlight the zonality of these driving forces, of which the high-category classification units were overlaid to delineate the CZ boundaries. The CZ regionalization of China was performed as a case study, resulting in 44 major regions (1st category), 100 submajor regions (2nd category) and 1448 regions (3rd category). The spatial distributions and driving factors of the ten largest regions were identified, followed by a simple comparison of the CZO network. Then, the proposed CZ regionalization was compared with recent studies on regionalization in China to evaluate its successes and weaknesses. By linking together CZ studies from the last decade, we advocate that a theoretical framework integrating the CZ evolution processes with ecological functions acts as one of the frontiers of CZ science. Our study demonstrates that the proposed three-category CZ classification scheme effectively identifies the spatial variations in CZs and could thus be further applied in other areas to advance terrestrial environmental research and provide decision support for the sustainable management of natural resources.

Clay CEC is one of identification indexes of the LAC-ferric horizon which is the diagnostic horizon of ferrosols in Chinese Soil Taxonomy, and it is defined as soil CEC × 1000/clay content, rather than the measured CEC of the extracted clays; however, such a calculation method would definitely lead to an overestimation of clay CEC because it doesn’t remove the contribution to soil CEC from other soil parameters. In this study, the physiochemical data of the subhorizons from 82 soil series in the tropical and subtropical regions in south China were used, clay CEC was calculated according to the current formula and measured after clays being extracted, the measured and calculated clay CEC were compared, the influencing factors were analyzed for their difference, and the new algorithms were established for clay CEC. The results showed that the measured clay CEC was 21.86% - 99.53% with a mean of 66.88% of the calculated one (significantly lower at p 2O3 (the contribution was 52.51% and 25.36%, respectively). By comparison of established regression models of clay CEC with other soil parameters, two new algorithms were recommended for clay CEC as follows: 1) Clay CEC = 10.32 − 0.14pH − 0.05OM − 0.11Fe2O3 + 0.01Silt − 0.01Clay + 1.17CECsoil, R2 = 0.705, P −3.40 + 0.01Sand + 0.02Silt + 1.05CECsoil, R2 = 0.589, P < 0.01).

Cation exchange capacity (CEC) is one of the most important properties of soils. The NH4OAc (pH = 7.0) exchange method is usually recommended to determine CEC (CEC1) of all soils with different pH values, particularly for studies on soil taxonomy. But comparatively the BaCl2-MgSO4 forced-exchange method is more authentic in determining CEC (CEC2) of tropical and subtropical highly-weathered acid soils. But so far little is known about the difference between CEC1 and CEC2. In this study, the physiochemical data of 114 acid B horizon soils from 112 soil series of tropical and subtropical China were used, CEC1 and CEC2 were determined and compared, the influencing factors were analyzed for the difference between CEC1 and CEC2, and then a regression model was established between CEC1 and CEC2. The results showed that CEC2 was significantly lower than CEC1 (p 2 was 14.76% - 63.31% with a mean of 36.32% of CEC1. In view of the contribution to CEC from other properties, CEC2 was mainly determined by pH (45.92%), followed by silt (21.05%), free Fe2O3 (17.35%) and clay contents (12.76%), CEC1 was mainly decided by free Fe2O3 content (40.38%), followed by pH (28.39%) and silt content (27.29%; and the difference between CEC1 and CEC2 was mainly affected by free Fe2O3 (50.92%), followed by silt content (26.46%) and pH (21.80%). The acceptable optimal regression model between CEC2 and CEC1 was established as CEC2 = 2.3114 × CEC11.1496 (R2 = 0.410, P 2-MgSO4 forced-exchange method is recommended in determining CEC of the highly-weathered acid soils in the tropical and subtropical regions.

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.

The black soil region in northeastern China is one of the most important grain production areas in the country, and plays a key role in food security. Large lands have been brought into cultivation in the last 100 years. Because these lands have lesser slope gradients compared to those in the loess plateau and other regions in China, soil erosion has been ignored in this area for a long time. Few soil conservation practices have been implemented. Serious soil erosion problems due to long slope lengths have been identified based on recent research and observations in the study area. The government has realized the necessity and urgency of soil conservation. However, to create policies and plans for black soil conservation, questions such as how much of the black soil region, and where in the region, erosion is a problem must be addressed. Currently, the black soil region of northeastern China is regarded as having a total area of 1.09 million square kilometers, including Heilongjiang, Jilin, the north part of Liaoning, and the east part of Inner Mongolia, in which there are also many non-black soils in mountain regions of Daxingan, Xiaoxingan, and Changbaishan. Therefore, it is difficult to determine precisely the conservation priorities for this large area. In this study, the black soils were defined and their concentrated area was delineated in northeastern China. The black soils were defined as soils with the “mollic epipedon”, which were classified as Isohumosols in Chinese Soil Taxonomy or Mollisols in the USDA Soil Taxonomy, and classified as Phaezoems, Chernozems, Kastanozems and Grayzems soils according to the Genetic Soil Classification of China (GSCC). The region dominated by these four soils in GSCC was defined as the black soil region. Because Phaezoems and Chernozems have higher organic matter contents and deeper mollic epipedon depths, and are distributed in the center of the study area, these two soils were defined as the typical black soil, and their dominate area was defined as the typical black soil region. A soil map in the scale of 1:1000000, made from the second national soil survey, was used with ArcGIS 10.2 software to delineate both black soil and typical black soil regions. A method called “central gravitational agglomeration” was developed. All of the soil polygons were selected, and the farthest and the smallest polygon were merged into its closest adjacent one if its radius was larger than the distance between it and its closest one. Otherwise it was abandoned. The same process was repeated until only a few small polygons were left and a regional outline appeared. Four soils were used to determine the exact boundaries of the black soil region, and two soils were used to determine the exact boundaries of the typical black soil region. The results showed that the area of the black soil region was 556000 km2, including 146 counties in which 33.0% was cultivated land. The area of the typical soil region was 333000 km2, including 138 counties in which 50.2% was cultivated land. Due to the long slopes, the soil erosion rate from the cropland with slope greater than 0.25° is estimated to be larger than soil loss tolerance of 200 t/km2. The cultivated lands with slope greater than 0.25° were regarded as sloped cultivated land or sloped cropland, which totaled 89000 km2, or 16.0% of the total land for the black soil region, and 78000 km2, or 23.4% of the total land for the typical black soil region. The sloped cultivated land in both the black soil region and typical black soil region should be targeted as key areas for soil erosion control in northeastern China.

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...

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.

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and CO2 release. However, to date, few studies have addressed these processes. Here, we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon (SIC) stocks in China. We observe an overall decrease in SIC stocks in topsoil (0–30 cm) (11.33 g C m–2 yr–1) from the 1980s to the 2010s. Total SIC stocks have decreased by ∼8.99 ± 2.24% (1.37 ± 0.37 Pg C). The average SIC losses across China (0.046 Pg C yr–1) and in cropland (0.016 Pg C yr–1) account for ∼17.6%–24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland, respectively. Nitrogen deposition and climate change have profound influences on SIC cycling. We estimate that ∼19.12%–19.47% of SIC stocks will be further lost by 2100. The consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration, which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

Nitrate (NO3−) loss and enrichment in water bodies caused by fertilization are a major environmental problem in agricultural areas. However, the quantitative contribution of different NO3− sources, especially chemical fertilizers (CF) and soil organic nitrogen (SON), to NO3− runoff loss remains unclear. In this study, a systematic investigation of NO3− runoff and its sources was conducted in a subtropical agricultural watershed located in Yujiang County, Jiangxi Province, China. A semi-monthly sampling was performed at the inlet and outlet from March 2018 to February 2019. Hydrochemical and dual NO3− isotope (15 N and 18O) approaches were combined to estimate the NO3− runoff loss and quantify the contribution of different sources with a Bayesian isotope mixing model. Source apportionment by Stable Isotope Analysis in R (SIAR) suggested that NO3− in runoff was mainly derived from nitrification of ammonium (NH4+) mineralized from SON (37–52%) and manure/sewage (M&S) (25–47%), while the contribution of CF was relatively small (14–25%). The contribution of various sources showed seasonal variations, with a greater contribution of CF in the wet growing season (March to August). Compared with the inlet which contributed 37–40% to runoff NO3−, SON contributed more at the outlet (49–52%). Denitrification in the runoff was small and appeared to be confined to the dry season (September to February), with an estimated NO3− loss of 2.73 kg N ha−1. The net NO3− runoff loss of the watershed was 34.5 kg N ha−1 yr−1, accounting for 15% of the annual fertilization rate (229 kg N ha−1 yr−1). Besides M&S (22%), fertilization and remineralization of SON (CF + SON) were the main sources for the NO3− runoff loss (78%), suggesting accelerated nitrification of NH4+ from CF (24%) and SON mineralization (54%). Our study indicates that NO3− runoff loss in subtropical agricultural watersheds is dominated by nonpoint source pollution from fertilization. SON played a more important role than CF. Besides, the contribution of sewage should not be neglected. Our data suggest that a combination of more rational fertilizer N application (CF), better management of SON, and better treatment of domestic sewage could alleviate NO3− pollution in subtropical China.

Plant root-derived carbon (C) inputs (Iroot) are the primary source of C in mineral bulk soil. However, a fraction of Iroot may lose directly (Iloss, e.g., via rhizosphere microbial respiration, leaching and fauna feeding) without contributing to bulk soil C pool. This loss has never been quantified, particularly at global scale, inhibiting reliable estimation of soil C dynamics. Here we integrate three observational global datasets including radiocarbon content, allocation of photosynthetically assimilated C, and root biomass distribution in 2,034 soil profiles to quantify Iroot and its contribution to the bulk soil C pool. We show that global average Iroot in the 0-200 cm soil profile is 3.5 Mg ha-1 yr-1, ~80% of which (i.e., Iloss) is lost rather than entering bulk soil. If ignoring Iloss, bulk soil C turnover will be incorrectly estimated to be four times faster. This can explain why Earth system models (in which all Iroot enters bulk soil C pools) predict much faster soil C turnover than radiocarbon-constrained estimates. Iroot decreases exponentially with soil depth, and the top 20 cm soil contains >60% of total Iroot. Actual C input to bulk soil (i.e., Iroot – Iloss) shows a similar depth distribution to Iroot. We also map Iloss and its depth distribution across the globe. Our results demonstrate the global significance of direct C losses which limit the contribution of Iroot to bulk soil C storage; and provide spatially explicit data to facilitate reliable soil C predictions via separating direct C losses from total root-derived C inputs.

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

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.

Nitrate (NO3−) leaching in agroecosystems has caused much concern worldwide due to its negative environmental and health impacts. To evaluate the effect of fertilization on NO3− leaching and soil N accumulation, a two-year 15N tracing study was conducted in subtropical China with field lysimeters packed with non-destructive sampling red soil. 200 kg N ha−1 yr−1 urea (15N abundance of 10%) was applied for maize crops. Fertilization promoted NO3− leaching by 91.5 ± 6.1 and 57.9 ± 15.2 kg N ha−1 yr−1 at 20 and 100 cm depth, respectively. Soil organic nitrogen (SON) pool was the main NO3− source (>60%), especially at surface soil. Fertilizer contributed 19.4 ± 2.0 and 32.8 ± 1.9% to NO3− leaching. At 20 cm depth, besides NO3− leaching accelerated by fertilization during the crop growth period (51.8%), mineralization of SON also resulted in abundant NO3− leaching during the fallow period (48.2%). At 100 cm depth, NO3− leaching significantly increased with the fertilization year due to the continuous NO3− leaching and the delay of NO3− leaching by soil NO3− adsorption. After two-year fertilization, 55.3 ± 2.2% of the applied N accumulated in the soil, leading to a soil N pool increase of 110.0 ± 10.1 kg N ha−1 yr−1. If the fertilization was maintained, the continuous N accumulation poses a potential threat to groundwater quality or drinking water safety. In subtropical red soil regions, to reduce NO3− leaching, NO3− leaching during both the crop growth and fallow period should be taken seriously, and effective management practices, such as cover crops and intensive tile drainage systems, should be carried out to reduce the fertilizer N accumulation and the contribution of SON.

Soil’s hydrological and ecological functions depend largely on soil water retention, which is affected by both structural and compositional soil properties. Studies have shown that alpine soils derived from sediments with different origins in the northeast Qinghai-Tibetan Plateau differ significantly in terms of particle size distribution, bulk density and soil organic carbon content. However, the variation pattern and its drivers of soil water retention in this region remain unclear. In this study, soil water retention characteristics (saturated soil water capacity, field capacity, wilting point and plant available water capacity) from different geomorphic settings (i.e., hillslopes, alluvial fans and floodplains) of an alpine catchment in the western part of the Qilian Mountains were analyzed in relation to sediment types and soil properties. Results show that soil water retention varies significantly with sediment types. Soils with an aeolian origin have a higher capacity to retain water compared with soils developed on fluvial sediments. Variations in soil water retention are mainly controlled by particle size distribution for fluvial sediments and by soil organic carbon content for aeolian sediments. The accumulation of soil organic carbon enhances plant available water capacity for sandy fluvial sediments but not for silty fluvial and aeolian sediments. The critical eco-hydrological functions of the mattic surface soil horizon in relation to water retention, runoff regulation and erosion control were highlighted for the alpine ecosystems. By deciphering sedimentological and pedological controls of soil water retention, this study improves the understanding of the evolution and functioning of alpine soils, which has further implications for other sedimentary ecosystems.

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.

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.

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.

Soil properties are often assumed to be static over time in hydrological studies, especially in hydrological modelling. Although it is well appreciated that soil structure and its impact on hydraulic properties are time-variable, particularly on cultivated land, very few studies have focused on quantifying the influence of such changes on soil hydrology, especially at the short term (i.e. seasonal). This study explored the value of incorporating such short-term time-variable soil properties in hydrological models. It is based on soil hydraulic properties from temporal field data under no-till done by direct seeding and under conventional cultivation done by ploughing to 0.2 m and harrowing. It uses a controlled tillage experiment in Scotland, on a soil with very good structural stability that experiences gentle rainfall in a temperate oceanic climate (Koppen Cfb). Water retention data were collected from intact soil cores sampled at 0.025, 0.095 and 0.275 m depth at three times between April and August 2013; (i) immediately following tillage, (ii) at barley crop establishment 1 month later and (iii) after harvest. Soil structure varied over time, with no-till soils gaining porosity and ploughed soils losing porosity. We hypothesised that no-till soils would have less seasonal temporal variability, but found it to be comparable to ploughed soils, albeit with pore structure changes following different trends. These changes were reflected in Van Genuchten fitting parameters, which if accounted for in 1-D HYDRUS modelling, had a marked impact on modelled soil water content over time if contrasted to predictions assuming a static pore structure. Using data from multiple sampling events, as opposed to one sampling event, resulted in up to a 44 % difference in soil water content predictions and increased the temporal variability by a factor of 1.5. Hence, our results have demonstrated that it is important to account for short-term temporal variability in soil physical properties in soil water modelling studies, and should not be ignored as a default, particularly on cultivated agricultural soils.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The accuracy and precision of predictions of soil texture based on visible–near-infrared (Vis-NIR) spectra are affected by the soil moisture content at the time of measurement. This study aimed to quantify these effects. We also developed a method to improve the accuracy of soil texture prediction when the difference in moisture content is large and unknown, as is usually the case in field measurements. Reflection spectra (380–2400 nm) of 89 soil samples were obtained in the laboratory under nine moisture conditions. Prediction models for each moisture condition were built separately using partial least-squares (PLS) regression. Each model was applied to independent validation sets under the same moisture condition as the calibration sets, and then the model based on air-dried soils (dry model) was applied to the other eight moisture conditions to quantify the effect of soil moisture on the prediction accuracy. Finally, the perpendicular drought index (PDI) was used as an indicator of soil moisture, and the nine moisture conditions were regrouped to four groups using the PDI. Prediction models for each group with the same PDI were built and evaluated. The results show that Vis-NIR spectra can be directly applied to predict the soil texture of soils in different known moisture states. When the moisture state is unknown, the models based on PDI grouping markedly improve prediction accuracy. The RMSEₚ of prediction from PDI-grouped models of prediction of the content of clay and sand separates were between 1 and 2% and between 8 and11%, respectively, for the different moisture classes. This method has potential for direct application in the field with on-the-go sensors.

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.

There is evidence that high levels of nitrate are stored in Earth’s Critical Zone between the land surface and impermeable bedrock. While nitrate in the upper layers of the soil ( 1 m). To study controls on nitrate accumulation in a subtropical monsoon area, a total of 728 regolith samples, taken from land surface to fresh bedrock, were collected from 21 drillings in a typical red soil Critical Zone in southern China. We characterized the variation of nitrate in these deep horizons and investigated the effect of land use, slope position and regolith physiochemical properties. The results showed that the amount of nitrate at 240-380 cm depth was more stable than at 100-240 cm or 380-500 cm depth, and accumulated at that depth in the upland and orchard regoliths. Nitrate contents in the deep horizons of upland and orchard regoliths were significantly larger than that in the paddy fields or woodland (p

Numerous pedotransfer functions (PTFs) have been developed to predict the soil properties of interest from other soil properties and, less commonly, from environmental variables. However, only a few PTFs have been developed to predict soil nutrients using environmental variables and to extrapolate them to characterize spatial soil variations at a regional scale. In this study, we attempted to develop PTFs for the total nitrogen (TN), total phosphorus (TP) and total potassium (TK) concentrations in three typical pedo-climatic areas of China (Fujian Province, Jiangsu Province and Qilian Mountains) with diverse climate, terrain and soil types. A series of linear PTFs were developed to quantify the effect of terrain and climate on the predictive relations between the soil nutrients and other measured soil properties and environmental variables. In addition, digital soil mapping (DSM) based on the random forest (RF) technique was performed to test the hypothesis that the best-fit PTFs could be extrapolated, based on soil maps and environmental variables, to describe regional soil variations in the soil nutrients. The root mean square errors (RMSEs) of the best-fit PTFs for TN, TP and TK ranged from 0.21 to 0.79 g kg−1, 0.20 to 0.58 g kg−1, and 3.68 to 5.00 g kg−1, respectively. Different RMSEs were produced by DSM, namely 0.37-1.89 g kg−1, 0.19−0.56 g kg−1 and 3.79-4.83 g kg−1 for TN, TP and TK, respectively. PTFs provided a sound basis for database compilation if the soil properties were highly correlated. However, the extrapolation of best-fit PTFs to regional scales yielded greater errors than those produced by DSM. The comparison results reveal the limitations of PTFs and suggest that their performance could be improved by using environmental covariates or by fitting data in areas with relatively homogeneous soil landscapes. The DSM techniques may provide satisfactory alternatives to predict soil data at both regional and plot scales.

Over the last three decades, both the crop yields and population in China have steadily increased due to continued agricultural investments. K fertilizer and the return of straw have played vital roles in the yield and quality of crops. However, the effects of long-term fertilization on soil K to a soil depth of 1 m have received little focus, and unbalanced K fertilization frequently occurs because local farmers lack sufficient information about soil K. In this study, the spatial distribution of available potassium (AK) in East China was predicted using kriging with external drift (KED) and random forest (RF) techniques. A comparison of the trends in the 1980s and 2010s showed that the mean AK values in East China generally increased. The mean AK values at soil depths of 0–20 cm, 20–50 cm and 50–100 cm increased by 17%, 12% and 23%, respectively, and the AK stocks increased from 0.14 Pg to 0.17 Pg. However, the spatial patterns of AK at the regional scale suggested that approximately 68.12% of the study area lacked AK. AK deficiency was found in Northeast and South China. The ratios of AK concentrations in the subsoil to those in the topsoil suggested that less K was stored in the subsoil than in the topsoil. Relatively more AK was found in the subsoil (20–50 cm) of paddy fields in the middle of East China than in the other areas. The maps produced in this study could be used to support the decisions of regional agriculture management, and our study demonstrates that effective K fertilizer application in East China should be taken into consideration.

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.

Ground-penetrating radar (GPR) is a convenient tool for volumetric soil water content (VSWC) estimation in hydrological and agricultural studies. Although case studies have been widely carried out, little attention has been paid to subsoil moisture estimates. In this research, we investigated three-dimensional soil moisture variation down to a depth of 1 m and the effect of rainfall events on spatial soil moisture dynamics. GPR surveying lines were conducted both before and after a heavy rainfall event to map the VSWC. Soil sampling and time domain reflectometry (TDR) probe data at different depths (20, 40, 60, 80, and 100 cm) were acquired. Our results demonstrated that there was a significant correlation between the dielectric constants and VSWCs at all depths. The established relationships for the different depth ranges had a low VSWC discrepancy when the dielectric constants ranged from 10 to 15. The effective range of each variogram was larger than 20 m, except for that of the 0–100 cm VSWC map after rainfall. In addition, the validation diagrams using corrected TDR values demonstrated relatively reliable VSWC maps. Approximately 89% of the variation in VSWC could be explained by the dielectric constants in the depth range of 0–40 cm, and VSWC predictions at this soil depth outperformed those at other depth ranges, with an overall RMSE of 0.027 m3 m−3 and R2 of 0.725. Furthermore, we also monitored the effect of precipitation on the accuracy of the VSWC prediction on shallow surfaces. Our study shows that three-dimensional soil moisture dynamics can be accurately estimated at the field scale by integrating GPR interpretation and spatial extrapolation methods.

The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245–370% relative to the linkage maps and 83–112% relative to the SoilGrids250m product with regard to the R2, and it was around 24–26% and 14–19% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional 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.

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg−1 and 26.6 g kg−1, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.

Summary The rapid developments in the acquisition of data on soil should enable pedologists to update existing digital soil maps readily. The methods by which that is done must take into account temporal change in soil properties and local differences in spatial variation. The common mapping techniques will have to be modified to make full use of digital data. We show what can be achieved with a case study on updating maps of soil organic matter (SOM) in Jiangsu Province, China, with three sets of soil data collected in the 1980s, 2000 and 2006. Our results showed that temporal changes in SOM between the three sampling periods occurred in only very small parts of the regions. Models of spatial variation of SOM based on the data collected in the 1980s and 2006 for the whole region differed somewhat, whereas models based on the data collected in the 1980s, 2000 and 2006 for the Taihu region (south Jiangsu) were significantly different. As updating with Bayesian maximum entropy continued, the accuracy of prediction increased and that of the prediction variance decreased. Finally, our study leads us to suggest improved technologies for updating digital soil maps with new data.

Soil organic matter (SOM) is a key property determining soil functions and a major form of carbon stored in soil. Understanding the spatial and temporal variability of SOM and the driving forces responsible for spatial and temporal changes is important to assess regional soil quality and carbon sequestration potential and, particularly, to establish better practices for land use and management. We evaluated the spatio-temporal change in SOM content from 1979–1982 to 2006 and its driving forces in Jiangsu Province, East China, using geostatistics. The results showed that mean SOM content increased from 16.60 ± 8.50 to 18.31 ± 8.32 g/kg over a 26-yr period. The maps of SOM generated by ordinary kriging represented the increasing trend from north to south across the province in the two periods. The level of SOM in 1979–1982 affected the pattern of change: the SOM increasing in areas initially with a small content while decreasing in areas having a large content. The map of SOM change showed that the rate of increase decreased from north to south within the province. Increased fertilizer application promoted crop production with more residual biomass being retained in the soil, which resulted in increased SOM content. Land use changes to paddy, upland or forest improved SOM content, whereas abandoning land reduced SOM content.