Spatial Distribution Of Soil Organic Matter Research Articles

Aluminum substitution stabilizes organic matter in ferrihydrite transforming into hematite: A molecular analysis

The association of soil organic matter (SOM) with iron (Fe) oxyhydroxides, particularly ferrihydrite, plays a pivotal role in the biogeochemical cycling of carbon (C) in both terrestrial and aquatic environment. The aging of ferrihydrite to more crystalline phases can impact the stability of associated organic C, a process potentially influenced by aluminum (Al) substitution due to its abundance. However, the molecular mechanisms governing the temporal and spatial distribution of SOM during the aging process of Al-substituted Fe oxyhydroxides remain unclear. This study aims to bridge this knowledge gap through a comprehensive approach, utilizing batch experiments, solid characterization techniques, and atomic force microscopy (AFM) based peak-force quantitative nanomechanical mapping (PF-QNM). Batch experiments revealed that humic acid (HA) was released into the aqueous phase during aging, with Al inhibiting this release. Various solid characterization methods collectively suggested that Al hindered the crystalline transformation of ferrihydrite and significantly preserved HA on the surface of newly formed hematite, rather than it being occluded within the interior of the new minerals. Results from 3-Dimensional fluorescence spectroscopy (3D-EEM) and Fourier-transform infrared spectroscopy (FTIR) indicated that the structure of HA remained constant, with the carboxyl-rich and hydroxyl-rich portions of HA fixed at the mineral interface during the aging period. Furthermore, we developed AFM-based PF-QNM to both quantify and visualize the interactions between Fe oxyhydroxides and HA, demonstrating variations in HA affinity among different Fe oxyhydroxides and highlighting the influence of the Al substitution rate.

Mapping Soil Organic Matter Using Different Modeling Techniques in the Dryland Agroecosystem of Huang-Huai-Hai Plain, Eastern China

Accurately mapping the spatial distribution and variation of soil organic matter (SOM) is of great significance for guiding regional soil management. However, the applicability and prediction performance of machine learning techniques in dryland agroecosystems still needs to be further studied. In this study, we collected a total of 733 topsoil samples from the farmland in Xiao County, Anhui Province, which is a typical dryland agroecosystem in the Huang-Huai-Hai Plain. Then, the environmental covariates were selected, and the ordinary kriging (OK), multiple linear stepwise regression (MLR), regression kriging (RK), radial basis function neural network (RBFNN), and random forest (RF) models were conducted to map the SOM content, and the optimal model was ascertained. The results demonstrated that the alkali-hydrolyzable nitrogen (26.11%), available potassium (17.73%), mean annual precipitation (13.26%), and pH (11.80%) were the main controlling factors affecting the spatial distribution of SOM in the study area. Meanwhile, the introduction of environmental covariates can effectively improve the SOM prediction accuracy, and the RF model (R2 = 0.48, MAE = 2.38 g kg−1, MRE = 12.99%, RMSE = 3.14 g kg−1) has a better performance than the RFBNN, MLR, RK, and OK methods. Although there are local differences in the spatial distribution of SOM predicted by the five methods, the overall spatial distribution of SOM was characterized by the low concentration area (13.44–20.00 g kg−1) distributed in the central and northwest of study area, and the high concentration area (24.00–28.95 g kg−1) distributed in the southeast. Overall, our study demonstrated that machine learning-based models could accurately predict the SOM content in dryland agroecosystem, and the produced maps function as baseline maps for sustainable agricultural management.

Mapping the soil organic matter content in a typical black-soil area using optical data, radar data and environmental covariates

Soil organic matter (SOM) plays an extremely important role in soil formation, soil fertility, environmental protection and the sustainable development of agriculture and forestry, especially in areas with fertile black soil. Remote sensing technology has unique advantages in SOM mapping and can quickly and accurately obtain SOM spatial distribution maps at a low cost. However, the current methods available for fusing radar data, optical data and environmental covariates for use in SOM mapping are far from sufficient. The study area is Youyi Farm, a typical black-soil area in Northeast China. Multiyear monthly composites of the bare-soil periods (April and May) from 2019 to 2022 in the study area were created in the Google Earth Engine (GEE), including VH-polarized and VV-polarized Sentinel-1 radar data, 10 spectral bands of Sentinel-2 data, elevation and slope terms representing terrain covariates, and multiyear average precipitation and temperature data representing climate covariates. SOM was mapped in the study area based on 188 cultivated-layer (0–20 cm) sampling points combined with a random forest regression algorithm, and the SOM mapping accuracies of different strategies were evaluated. The results show that 1) the SOM mapping accuracy obtained using Sentinel-1 data combined with Sentinel-2 data was improved slightly compared to that obtained using Sentinel-2 data only, with improvements observed mainly in the prediction accuracies of areas with SOM contents below 5 %; 2) the highest accuracy was obtained by using the May composite image, while the April and May composite images reduced the accuracy; 3) adding environmental covariates greatly improved the SOM mapping accuracy, with the highest R2 being 0.696 and the lowest RMSE being 0.712 %; and 4) the importance of climate covariates was found to be higher than that of terrain covariates. This study expands the existing SOM mapping research using radar and optical data and evaluates the effects of different environmental covariates on the SOM mapping results.

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R2 = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R2 = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques.

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Soil organic matter (SOM) scale effects are critical for crop growth and food security, especially in coal–grain complexes. However, few studies describe the spatial variation in SOM and its influencing factors at different sampling scales. Here, geostatistical theory and mathematical statistical methods were adopted to analyze the spatial variation characteristics of and structural differences in SOM in the coal mining subsidence area at Zhaogu No. 2 Mine at different sampling scales. The results showed that SOM varied spatially at large, medium, and small scales, and the coefficients of variation were 28.07%, 14.93%, and 14.31%, respectively, which are moderate values. The characteristic functions of the SOM content at different sampling scales differed, and the spatial structure scale effect was obvious. The spatial distribution of the SOM content fitted by the multiscale fitting model method was generally the same as the spatial distribution law of the SOM content fitted by the single scale kriging interpolation method; however, in terms of the detailed expression and spatial distribution of small-scale SOM content, the fitting model method was more accurate, and the accuracy increased by 36%. At the different sampling scales, sample size and soil type had specific effects on the SOM spatial distribution. These results provide research concepts and technical countermeasures for improving food security and the ecological environment in the coal–grain complex and help ensure sustainable agricultural lands.

Effects of Long-Term Straw Return and Environmental Factors on the Spatiotemporal Variability of Soil Organic Matter in the Black Soil Region: A Case Study

Exploring the effects of straw return and environmental factors on the spatiotemporal variation of soil organic matter (SOM) in black soil regions is essential for soil carbon sequestration research. However, studies seldom quantified the effects of long-term straw return on a long-term SOM variation at a regional scale in typical black soil areas. The case was conducted in one of the three major black soil regions in the Northern Hemisphere, where the straw return policy has been implemented for a long time. The study obtained the SOM spatial distribution in 2007, 2009, 2012, 2015, and 2018 with approximately 9000 samples and analyzed the effects of soil types, texture, elevation, and human management on the spatiotemporal variation. The results indicated that from the 1980s to 2007, before the straw return policy implementation, the mean SOM content decreased from 24.38 g kg−1 to 18.94 g kg−1. In contrast, the mean SOM content gradually increased from 2007 to 2018 after implementing straw return practices. In addition, the area of SOM within 20–30 g kg−1 increased gradually, with 32.2%, 40.5%, 50.2%, 49.4%, and 60.5% in 2007, 2009, 2012, 2015, and 2018, respectively. Surprisingly, the SOM within 30–40 g kg−1 emerged in 2018. The results indicated that returning straw to the field might promote SOM accumulation. However, the SOM contents in Phaezems (19.25–21.82 g kg−1) were lower than that in natural Phaezems (40–60 g kg−1), indicating severe degradation. The clay content positively correlated to SOM and was a major explanatory variable for the response of SOM to straw return. Straw return practices are promising measures in the black soil region and are worth exploring more effective approaches to allow straw return to play a better role.

Precise prediction of soil organic matter in soils planted with a variety of crops through hybrid methods

Determining the spatial distribution of soil organic matter (SOM) of cultivated lands efficiently and quickly using available data is crucial for the development of smart agriculture with low consumption and high efficiency. In this study, a total of 205 black soil samples from Hulunbuir, China, planted with a variety of crops were investigated for multiple soil nutrients. The SOM content in the studied black soil was rich, ranging from 32.1 to 62.8 g/kg. In addition, machine learning models based on spectral data and environmental variables were established to predict the overall spatial distribution of SOM. The prediction effect of the Random Forest (RF) model was the best (R2 = 0.84, CCC = 0.872, RMSE = 3.31, RPD = 2.19), and the environmental variables (mainly clay index) containing more information in recursive feature elimination had the greatest influence on the prediction accuracy of SOM. Further, to reduce the dependence on predictive indicators, this study quantitatively analyzed the disturbance factors (crop types) of cultivated land using Bayesian Maximum Entropy (BME), and the BME-RF model was established to combine crop types with prediction indicators to further optimize SOM prediction accuracy. The prediction accuracy of the model was improved to varying degrees in multiple groups of experiments by adding several groups of soft data to compare and analyze the effect of the BME-RF model, and it was the highest (R2 = 0.89, RI = 15.5%) when 1200 soft data were used. Crop types have certain influence on the pattern of SOM content. The SOM content was relatively low in rapeseed and barley planting areas, but was relatively high in potato and beet growing areas. In this paper, BME-RF method is innovated to combine historical data with as little experimental data as possible, which greatly improved the accuracy of digital map of soil nutrients. This study can provide a scheme for the development of smart agriculture and promote the sustainability of cultivated land environment.

Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis

A model incorporating geo-detector analysis and geographically weighted principal component analysis into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The regression accuracy and residuals from GWPCA-MGWR were compared to those of the classical Geographically Weighted regression (GWR), Multi-scale Geographically Weighted regression (MGWR), and GWPCA-GWR. Our results revealed that local multi-collinearity on model fitting negatively affects the results to different degrees. Additionally, compared to other models, GWPCA-MGWR provided the lowest MAE (0.001) and little-to-no residual spatial autocorrelation and is the best model for regression for SOM spatial distribution and identification of dominant driving factors. GWPCA-MGWR produced spatial non-stationary SOM that was variably affected by soil nutrient content, soil type, and human activity, and was geomorphic in the second place. In conclusion, the spatial information obtained from GWPCA-MGWR provides a valuable reference for understanding the factors that influence SOM variation.

Diachronic Mapping of Soil Organic Matter in Eastern Croatia Croplands

The spatiotemporal analysis and mapping of soil organic matter (SOM) play a pivotal role for evaluating soil health and for implementing preservation and restoration actions. In this context, the first aim of the study is to furnish a high-resolution mapping of current SOM content in eastern Croatia. The second aim is to perform a diachronic analysis of SOM content, comparing two datasets characterized by an extreme data imbalance. The more recent dataset (SOM2010), representative of 2010s, comprises 19,386 samples and the older dataset (SOM1970), representative of the 1970s, comprises 152 samples. The marked data imbalance and the different modalities in soil sampling and laboratory analysis of the two datasets are taken into consideration in performing the comparison. The study reveals a general depletion trend of SOM from the 1970s to the 2010s, more evident in with regard to Fluvisols and Gleysols. At a regional scale, the SOM2010 is characterized by lower variability compared to SOM1970, indicating a process of homogenization of SOM spatial distribution in recent years. Considering the local scale, there is limited information for the 1970s; for the 2010s the SOM spatial distribution is characterized by a high short-range spatial variability, with a characteristic spotty appearance, likely related to agricultural practices.

Mapping Regional Soil Organic Matter Based on Sentinel-2A and MODIS Imagery Using Machine Learning Algorithms and Google Earth Engine

Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-precision and high-resolution SOM maps remains a challenge due to the difficulty of selecting appropriate satellite data sources and prediction algorithms. This study aimed to investigate the influence of different remotely sensed images and machine learning algorithms on SOM prediction. We constructed two comparative experiments, i.e., full-band and common-band variable datasets of Sentinel-2A and MODIS images using Google Earth Engine (GEE). The predictive performances of random forest (RF), artificial neural network (ANN), and support vector regression (SVR) algorithms were evaluated, and the SOM map was generated for the Songnen Plain. Results showed that the model based on the full-band Sentinel-2A dataset achieved the best performance. The application of Sentinel-2A data resulted in mean relative improvements (RIs) of 7.67% and 5.87%, respectively. The RF achieved a lower root mean squared error (RMSE = 0.68%) and a higher coefficient of determination (R2 = 0.67) in all of the predicted scenarios than ANN and SVR. The resultant SOM map accurately characterized the SOM spatial distribution. Therefore, the Sentinel-2A data have obvious advantages over MODIS due to their higher spectral and spatial resolutions, and the combination of the RF algorithm and GEE is an effective approach to SOM mapping.

A comparison of importance of modelling method and sample size for mapping soil organic matter in Guangdong, China

Digital soil mapping (DSM) is the most widely used method for producing spatial information of soil organic matter (SOM). Accuracy of the information is generally determined by modelling methods and sample sizes used for DSM. However, different studies present different importance of modelling method and sample size on accuracy of DSM, while they do not explore various combinations of modelling method and sample size. Based on the studies, it is supposed that there exists an optimal combination of modelling method and sample size for producing information of SOM accurately and economically. With SOM data of 1861 soil samples collected in Guangdong, China, the present study first assessed importance of modelling method and sample size and then examined if an optimal combination of modelling method and sample size existed for the area. Six modelling methods were explored, while 12 sample sizes were used, ranging from 100 to 1200 with an interval of 100. For each size, 10 repeated samples were randomly taken from a data of 1311 samples which were randomly selected from all the 1861 soil samples based on the probability distribution of the SOM data. The results showed that, for small sample sizes, the modelling methods have a greater impact on accuracy of DSM. However, for large sample sizes, e.g., more than 1000, the sample sizes have a much greater impact. Due to the varying importance of modelling method and sample size, there exists an optimal combination of modelling method and sample size for spatial prediction of SOM in the area, i.e., the combination of regression kriging and a sample size of 800. Thus, for economically producing detailed and accurate information on spatial distribution of SOM, it is recommended that a series of modelling methods and sample sizes are tried to identify an optimal combination of modelling method and sample size.

Spatial variability of soil chemical properties of Moso bamboo forests of China

This study investigates the spatial variability of soil organic matter (SOM), soil organic carbon (SOC) and pH in the upper 20-cm layer and 20–40 cm layer in Moso bamboo (Phyllostachys pubescens Pradelle) forests using a geostatistics model. Interpolation maps of SOM, SOC, and pH were developed using ordinary kriging (OK) and inverse distance weighted (IDW) methods. The pH, SOC, and SOM of the two soil layers ranged from 4.6 to 4.7, from 1.5 to 2.7 g kg−1 and from 20.3 to 22.4 g kg−1, respectively. The coefficient of variation for SOM and SOC was 29.9–43.3% while a weak variability was found for pH. Gaussian and exponential models performed well in describing the spatial variability of SOC contents with R2 varying from 0.95 to 0.90. The nugget/sill values of pH are less than 25%, which indicates a strong spatial correlation, while the nugget/sill values of SOC and SOM fall under moderate spatial correlation. Interpolation using ordinary kriging and inverse distance weighted methods revealed that the spatial distribution of SOM, SOC, and pH was inconsistent due to external and internal factors across the plots. Regarding the cross-validation results, the ordinary kriging method performed better than inverse distance weighted method for selected soil properties. This study suggests that the spatial variability of soil chemical properties revealed by geostatistics modeling will help decision-makers improve the management of soil properties.

Spatial variability of soil organic matter in a gravel-sand mulched jujube orchard at field scale

In agricultural ecosystems, soil organic matter (SOM) is a major determinant and indicator of soil fertility and quality. The objectives of this study were to understand the spatial distribution of SOM and the accuracy of two interpolation methods evaluated: Kriging and inverse distance weighting (IDW) in a gravel-sand mulch of northwest China. We are measuring SOM in 256 soil samples collected from 0 to 10, 10–20, 20–30, and 30–50 cm layers at five sampling scales, 32 × 32, 28 × 28, 24 × 24, 20 × 20, and 16 × 16 m, and three sampling spacings, 4, 8, and 12 m. SOM content decreased with depth in each scale, varying from 2.41 to 5.75 g/kg. The SOM was weakly to moderately variable and has a strong spatial autocorrelation. The standard deviation was small for each soil layer, and the variability was low or weakly moderate, indicating that two interpolation methods were applicable to the entire data set. Kriging interpolation was more accurate than IDW. The distribution of SOM differed in the surface layers at the five sampling scales, which was more uniform as the sampling scale decreased. Eight meter is a reasonable sampling spacing. Through the scales effect and spacing change on SOM for fixed-point monitoring, combing the estimate of SOM to reduce the sampling workload will aid the supply of SOM in gravel-sand mulched fields in arid regions.

Spatial distribution of soil organic matter in a coal mining subsidence area

ABSTRACTUnderground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the three-dimensional (3D) spatial distribution of soil nutrients in mining area farmland is crucial for agricultural production and environmental management. However, few studies have reported the 3D spatial distribution of soil organic matter (SOM) in coal mining subsidence area. In our study, a sequential Gaussian simulation (SGS) algorithm was used to analyse the spatial distribution of SOM based on observations of 180 soil samples in the Zhaogu mine in China. The results showed that the SOM content had considerable variation in spatial distribution at different soil depths (0–20, 20–40, 40–60 cm) and decreased with the increase in soil depth. The spatial variability of surface organic matter was the largest, and the coefficient of variation was 29.38%, which was moderately mutated. The spatial distribution of SOM also varied among slope locations. The SOM content was higher upslope and downslope than on the middle slope. In addition, given a threshold, SGS can be used to calculate the probability that the organic matter content at any position is lower or higher than the given value. The research results provide a reference for land reclamation and precision agriculture.

Spatial Distribution of Soil Organic Matter and Soil Organic Carbon Stocks in Semi-Arid Area of Northeastern Syria

Although soil organic matter (SOM) forms a small portion of the soil body. Nevertheless, it is the most important component of the soil ecosystem, as well as of the carbon global cycle. In the semi-arid environment, there has been little research on the spatial distribution of SOM and soil organic carbon (SOC) stock. In this study, stratified random samples of total 30 soils were collected from two different soil depth (topsoil, subsoil) of Al Balikh plain and used for mapping the spatial variability of SOC and to estimating the SOC stock. The result showed that the values were relatively homogenate, with the normal decreasing trend with increasing the depth. The standard deviation (Std. D) for both SOC and SOC stock indicates homogeneous and absence of outliers values, whereas the coefficient of variation (C.V) indicates non-dispersion and clustering of values around the average. SOC was 0.38%, 0.17% in topsoil and subsoil respectively; the corresponding averages of SOC stock were 1.23 kg·m-2 and 1.14 kg·m-2 respectively, these values reflecting typical characteristics of poor SOC semi-arid soil. The correlation between SOC and SOC stock was (R2 = 0.996, p < 0.001) in topsoil and it was (R2 = 0.941, p < 0.001) for subsoil. The semivariograms were indicated that both SOC and SOC stock were best fitted to the exponential model. Nugget, range, and sill were equal to 0.002, 0.036, and 0.044, respectively for SOC in topsoil, and 0.014, 0.071, and 0.081, for SOC in the subsoil. For SOC stock, it was 0.0, 0.036, and 0.0508, respectively in topsoil. In the subsoil, the values were 0.1899, 0.086, and 4.159, respectively. SOC and SCO stock in both two layers are shown a strong spatial dependence, for which were 4.3, 17.2 for SOC in topsoil and subsoil respectively, and 0.0, 4.5 for SOC stock in topsoil and subsoil respectively, thus, which can be attributed to intrinsic factors.

Spatial variability of selected soil properties and its impact on the grain yield of oats (Avena sativa L.) in small fields

To investigate spatial variability in topsoil (0–20 cm) pH, available phosphorus (P), potassium (K), total nitrogen (N), and soil organic matter (SOM) of small fields (∼2 ha), and to determine the impact of soil heterogeneity on the spatial variability of crop yield two fields were cropped with spring oats and one with winter wheat under humid-temperate conditions. In the two oat fields, some of the measured soil properties (P, K) and the grain yield varied considerably, and strong spatial trends were recorded for most of the soil traits. In the third field, soil properties showed only a moderate spatial variation, and no spatial trends were found. The spatial distribution of SOM and total N in the topsoil had some influence on the spatial pattern of the oat grain yield in the field of Gränichen; however, spatial relationships between soil chemical properties and grain yield were rather weak in our study.

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

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

Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

An accurate estimation of soil organic matter (SOM) content for spatial non-point prediction is an important driving force for the agricultural carbon cycle and sustainable productivity. This study proposed a hybrid geostatistical method of extreme learning machine-ordinary kriging (ELMOK), to predict the spatial variability of the SOM content. To assess the feasibility of ELMOK, a case study was conducted in a regional scale study area in Shaanxi Province, China. A total of 472 topsoil (0–20 cm) samples were collected. A total of 14 auxiliary variables (predictors) were obtained from remote sensing data and environmental factors. The proposed method was compared with the ability of traditional geostatistical methods such as simple kriging (SK) and ordinary kriging (OK), in addition to hybrid geostatistical methods such as regression-ordinary kriging (ROK) and artificial neural network-ordinary kriging (ANNOK). The results showed that the extreme learning machines (ELM) model used principal components (PCs) as input variables, and performed better than both multiple linear regression (MLR) and artificial neural network (ANN) models. Compared with geostatistical and hybrid geostatistical prediction methods of SOM spatial distribution, the ELMOK model had the highest coefficient of determination (R2 = 0.671) and ratio of performance to deviation (RPD = 2.05), as well as the lowest root mean square error (RMSE = 1.402 g kg−1). In conclusion, the application of remote sensing imagery and environmental factors has a deeper driven significance of a non-linear and multi-dimensional hierarchy relationship for explaining the spatial variability of SOM, tracing local carbon sink and high quality SOM maps. More importantly, it is possibly concluded that the sustainable monitoring of SOM is a significant process through the pixel-based revisit sampling, an analysis of the mapping results of SOM, and methodological integration, which is the primary step in spatial variations and time series. The proposed ELMOK methodology is a promising and effective approach which can play a vital role in predicting the spatial variability of SOM at a regional scale.

Spatial Variability of Soil Organic Matter and Cation Exchange Capacity in an Oxisol under Different Land Uses

ABSTRACTSoil properties may exhibit large spatial variability. Frequently this variability is auto-correlated at a certain scale. In addition to soil-forming factors, soil management, land cover, and agricultural system may affect the spatial variability of agricultural soils. Soil organic matter (OM) is an important soil property contributing toward soil fertility and a key attribute in assessing soil quality. Increasing soil OM increases cation exchange capacity (CEC) and enhances soil fertility. We analyzed the impact of land use on the spatial variability of OM and CEC in a tropical soil, an Oxisol, within São Paulo state, Brazil. Land uses were prairie, maize, and mango. Soil samples were taken at 0–10 and 10–20 cm depths at 84 points within 1-ha plots, i.e., 100 m × 100 m. Statistical variability was higher for soil OM than for CEC. The mango plot contained the highest soil OM, whereas prairie the lowest. Also, soil OM and CEC were significantly related at all land use treatments and depths studied. All soil OM data sets and most of the CEC data sets (with two exceptions) exhibited spatial dependence. When spatial variability was present, the semivariograms showed a nugget effect plus a spherical or an exponential structure. Patterns of soil OM and CEC spatial variability (i.e., model type, ranges of spatial dependence, and nugget effects) were different between land uses and soil depths. In general, CEC exhibited a lower spatial autocorrelation and a weaker spatial structure than soil OM. Moreover, soil OM displayed a higher autocorrelation and was more strongly structured at the 0–10 cm depth than at the 10–20 cm depth. Interpolation by kriging or inverse distance weighting (IDW) allowed to illustrate how the spatial variability of soil OM and CEC differed due to land cover and sampling depth. Modeling and mapping the spatial distribution of soil OM and CEC provided a framework for spatially implicit comparisons of these soil properties, which may be useful for practical applications.

Spatial variability of soil organic matter using remote sensing data

Estimation of soil organic matter (SOM) at unsampled locations is crucial in agronomical and environmental studies. In this study, the ability of geostatistical methods such as ordinary kriging (OK), simple kriging (SK) and cokriging (CK) and hybrid geostatistical methods such as regression-simple kriging (RSK)/-ordinary kriging (ROK) and artificial neural network-simple kriging (ANNSK)/-ordinary kriging (ANNOK) was evaluated to predict SOM content. To this end, a set of 100 soil samples were collected from 0 to 15cm depth of agricultural soils in Selin plain, northwest of Iran. The organic carbon was measured using Walkley–Black method. An auxiliary variable was provided by remote sensing data (Landsat 7 ETM+). Three performance criteria including mean error (ME), root mean square error (RMSE) and coefficient of determination (R2) were used to evaluate the performance of the derived models. The results showed that the ANN model that used principal components (PCs) as input variables, performed better than the multiple linear regression (MLR) model. The hybrid geostatistical methods, which include ANNOK, ANNSK, ROK and RSK provided more reliable predictions than the geostatistical methods, which include SK, OK and CK. In general, the best prediction method for the estimation of SOM spatial distribution was the ANNOK model, which had the smallest RMSE (0.271%) and the highest R2 (0.633). It was concluded that information from Landsat ETM+ imagery is potential auxiliary variables for improving spatial prediction, monitoring SOM and development of high quality SOM maps, which is the primary step in site-specific soil management.