Topsoil Clay Content Research Articles

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model’s output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model’s predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model’s output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data

The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction (Rval2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions.

Driving factors of tree biomass and soil carbon pool in xerophytic forests of northeastern Argentina

BackgroundThe conversion of forests into agricultural lands can be a threat because the forests carbon stored could be a source of emissions. The capacity to improve the predictions on the consequences of land use change depends on the identification of factors that influence carbon pools. We investigated the key driving factors of tree biomass and soil carbon pools in xerophytic forests in northeastern Argentina. Based on analyses of forest structure variables and abiotic factors (topography and soil properties) from 18 mature forests, we evaluated carbon pools using uni- and multivariate (redundancy analysis) methods.ResultsThe total carbon pool was estimated at 102.4 ± 24.0 Mg ha−1. Soil organic carbon storage is the single largest carbon pool relative to tree biomass, representing 73.1% of total carbon. Tree canopy cover and basal area were positively correlated with biomass carbon pool (r = 0.77 and r = 0.73, p < 0.001, respectively), proving to be significant drivers of carbon storage in this compartment. Slope, soil clay content and cation-exchange capacity had a better explanation for the variability in soil carbon pools, and all showed significant positive correlations with soil carbon pools (r = 0.64, 0.60 and 0.50; p < 0.05, respectively). The vertisols showed a 27.8% higher soil carbon stock than alfisols.ConclusionsThe relevance of our study stems from a dearth of information on carbon pools and their drivers in xerophytic forests, and in particular, the importance of this ecosystems’ type for Argentina, because they cover 81.9% of native forest area. Basal area and tree canopy cover exert a strong effect on the carbon pool in tree biomass but not in the soil. The results suggests that there is a potentially major SOC accumulation in forests located in slightly sloping areas and soils with higher topsoil clay content, such as vertisols. This could provide an important reference for implementing forestry carbon sink projects.

Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent-boosted regression tree.

Carbon sequestration in earth surface is higher than the atmosphere, and the amount of carbon stored in wetlands is much greater than all other land surfaces. The purpose of this study was to estimate soil organic carbon stocks (SOCS) and investigate spatial distribution pattern of Yuksekova wetlands and surrounding lands in Hakkari province of Turkey using machine learning and remote sensing data. Disturbed and undisturbed soil samples were collected from 10-cm depth in 50 locations differed with land use and land cover. Vegetation, soil, and moisture indices were calculated using Sentinel 2 Multispectral Sensor Instrument (MSI) data. Significant correlations (p≤0.01) were obtained between the indices and SOCS; thus, the remote sensing indices (ARVI 0.43, BI -0.43, GSI -0.39, GNDI 0.44, NDVI 0.44, NDWI 0.38, and SRCI 0.51) were used as covariates in multi-layer perceptron neural network (MLP) and gradient descent-boosted regression tree (GBDT) machine learning models. Mean absolute error, root mean square error, and mean absolute percentage error were 3.94 (Mg C ha -1), 6.64 (Mg C ha-1), and 9.97%, respectively. The simple ratio clay index (SRCI), which represents the soil texture, was the most important factor in the SOCS estimation variance. In addition, the relationship between SRCI and Topsoil Grain Size Index revealed that topsoil clay content is a highly important parameter in spatial variation of SOCS. The spatial SOCS values obtained using the GBDT model and the mean SOCS values of the CORINE land cover classes were significantly different. The land cover has a significant effect on SOC in Yuksekova plain. The mean SOCS for continuously ponded fields was 45.58 Mg C ha-1, which was significantly different from the mean SOCS of arable lands. The mean SOCS in arable lands, with significant areas of natural vegetation, was 50.22 Mg C ha-1 and this amount was significantly higher from the SOCS of other land covers (p<0.01). The wetlands had the highest SOCS (61.46 Mg C ha-1), followed by the lands principally occupied by natural vegetation and used as rangelands around the wetland (50.22 Mg C ha-1). Environmental conditions had significant effect on SOCS in the study area. The use of remote sensing indices instead of using single bands as estimators in the GBDT algorithm minimized radiometric errors, and reliable spatial SOCS information was obtained by using the estimators. Therefore, the spatial estimation of SOCS can be successfully determined with up-to-date machine learning algorithms only using remote sensing predictor variables. Reliable estimation of SOCS in wetlands and surrounding lands can help understand policy and decision makers the importance of wetlands in mitigating the negative impacts of global warming.

Effects of material migration on the spatial distribution of topsoil moisture at the slope scale

Soil moisture is strongly spatially variable and scale dependent, but there is a lack of research focusing on factors that link soil actual evaporation to the spatial distribution of topsoil moisture at the slope scale. External factors (e.g., meteorological) have similar effects across entire slopes; hence, this paper instead explores the internal factors controlling differential soil actual evaporation. We consider the effects of historical rainfall on soil erosion, and adopt a combined approach encompassing slope investigations, a slope rainfall simulation experiment, slope erosion analyzed by 3D laser terrain scanning, and evaporation experiments. Along the runoff direction, we show that the relative topsoil clay content increases, but slope surface deformation and failure lead to a decrease in the relative content of clay in deposits located on the lower of slope. The original thickness of slope surface decreases in the upper part of the slope, due to particle migration, while the slope surface relatively far away from the upper part of the slope tends to be thicker, due to the accumulation of slope deposits. The actual evaporation of the topsoil relatively far from the upper part of the slope is significantly weaker (63%–72% probability), which may be related to the high clay content, dense soil structure, and high content of clay minerals. The topsoil evaporation also tends to weaken as overburden thickness increases. Here, we suggest that marked differences in the original thickness of the slope surface and in the properties of the topsoil caused by the migration and deposition of soil particles under successive years of rainfall erosion, are important factors controlling differences in topsoil actual evaporation in different parts of the slope. This understanding lays the foundation for the analysis of hydrological processes in different parts of a slope, which is crucial for the study of shallow slope mechanical behavior.

Topsoil clay content mapping in croplands from Sentinel-2 data: Influence of atmospheric correction methods across a season time series

Recent studies demonstrated the capability of Sentinel-2 (S2) data to estimate topsoil properties and highlighted the sensitivity of these estimations to soil surface conditions depending on the S2 acquisition date. These estimations are based on Bottom of Atmosphere (BOA) reflectance images, obtained from Top of Atmosphere (TOA) reflectance values using Atmospheric Correction (AC) methods. AC of optical satellite imagery is an important pre-processing stage before estimating biophysical variables, and several AC methods are currently operational to perform such conversion. This study aims at evaluating the sensitivity of topsoil clay content estimation to atmospheric corrections along an S2 time series. Three AC methods were tested (MAJA, Sen2Cor, and LaSRC) on a time series of eleven Sentinel-2 images acquired over a cultivated region in India (Karnataka State) from February 2017 to June 2017. Multiple Linear Regression models were built using clay content analyzed from topsoil samples collected over bare soil pixels and corresponding BOA reflectance data. The influence of AC methods was also analysed depending on bare soil pixels selections based on two spectral indices and several thresholds: the normalized difference vegetation index (NDVI below 0.25, 0.3 and 0.35) and the combination of NDVI (below 0.3) and Normalized Burned Ratio 2 index (NBR2 below 0.09, 0.12 and 0.15) for masking green vegetation, crop residues and soil moisture.First, this work highlighted that regression models were more sensitive to acquisition date than to AC method, suggesting that soil surface conditions were more influent on clay content estimation models than variability among atmospheric corrections. Secondly, no AC method outperformed other methods for clay content estimation, and the performances of regression models varied mostly depending on the bare soil pixels selection used to calibrate the regression models. Finally, differences in BOA reflectance among AC methods for the same acquisition date led to differences in NDVI and NBR2, and hence in bare soil coverage identification and subsequent topsoil clay content mapping coverage. Thus, selecting S2 images with respect to the acquisition date appears to be a more critical step than selecting an AC method, to ensure optimal retrieval accuracy when mapping topsoil properties assumed to be relatively stable over time.

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

Analysing the impact of soil spatial sampling on the performances of Digital Soil Mapping models and their evaluation: A numerical experiment on Quantile Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral imagery

It has long been acknowledged that the soil spatial samplings used as inputs to DSM models are strong drivers – and often limiting factors – of the performances of such models. However, few studies have focused on evaluating this impact and identifying the related spatial sampling characteristics. In this study, a numerical experiment was conducted on this topic using the pseudo values of topsoil clay content obtained from an airborne Visible Near InfraRed-Short Wave InfraRed (Vis-NIR-SWIR) hyperspectral image in the Cap Bon region (Tunisia) as the source of the spatial sampling.Twelve thousand DSM models were built by running a Random Forest algorithm from soil spatial sampling of different sizes and average spacings (from 200 m to 2000 m) and different spatial distributions (from clustered to regularly distributed), aiming to mimic the various situations encountered when handling legacy data. These DSM models were evaluated with regard to both their prediction performances and their ability to estimate their overall and local uncertainties. Three evaluation methods were applied: a model-based one, a classical model-free one using 25% of the sites removed from the initial soil data, and a reference one using a set of 100,000 independent sites selected by stratified random sampling over the entire region.The results showed that: 1) While, as expected, the performances of the DSM models increased when the spacing of the sample increased, this increase was diminished for the smallest spacing as soon as 50% of the spatially structured variance was captured by the sampling, 2) Sampling that provided complete and even distributions in the geographical space and had as great spread of the target soil property as possible increased the DSM performances, while complete and even sampling distributions in the covariate space had less impacts, 3) Systematic underestimations of the overall uncertainty of DSM models were observed, that were all the more important that the sparse samplings poorly covered the real distribution of the target soil property and that the dense sampling were unevenly distributed in the geographical space, 4) The local uncertainties were underestimated for sparse sampling and over-estimated for dense sampling while being sensitive to the same sampling characteristics as overall uncertainty.Such finding have practical outcomes on sampling strategies and DSM model evaluation that are discussed.

How far can the uncertainty on a Digital Soil Map be known?: A numerical experiment using pseudo values of clay content obtained from Vis-SWIR hyperspectral imagery

Digital Soil Map uncertainty is usually evaluated from a set of independent soil observations that are used to determine various uncertainty indicators. However, the number and locations of the sites that constitute these evaluations may impact the value of these indicators.In this paper, a numerical experiment on uncertainty indicators was performed using the pseudo values of topsoil clay content obtained from an airborne hyperspectral image in the Cap Bon region (Tunisia). These pseudo values form a soil pattern with a large extent (46% of 300 km2), high resolution (5 m) and good accuracy (R2val = 0.75) while being free of visible artefacts and pedologically plausible. Therefore, the dataset was considered a fair representation of reality while providing a quasi-unlimited choice of sites.The numerical experiment considered three Quantile Regression Forests as examples of DSM models by using inputs from relief soil covariates and geographical locations that were calibrated from 200, 2000 and 100,000 individuals respectively (low, medium and high quality models). Their uncertainty indicators were first evaluated by calculating four uncertainty indicators (ME, MSE, SSMSE and PICP) from a large independent validation set of 100,000 sites. These uncertainty indicators were then computed from independent evaluation sets of different sizes (from 50 to 500 sites) and from different locations (500 evaluation sets of each size). The independent evaluation sets were selected following a stratified random sampling using compact geographical strata.The numerical experiment showed that the values of the uncertainty indicators were highly variable across numbers and locations of sites. The largest variations were observed for evaluation sets with fewer than 100 sites, but non-negligible variations remained for larger evaluation datasets. This result suggested that evaluations from independent sets convey a non-negligible error on the uncertainty indicators, which increases as the number of sites decrease.Evaluations of DSM models from independent evaluation sets should be interpreted with care and uncertainty on validation results should be systematically estimated. For that, numerical experiments for benchmarking DSM models on known soil patterns across the world would be a valuable complement to the analytical expressions for the uncertainty indicators and the many DSM applications for which these analytical expressions are not valid. This would serve also to improve the sampling techniques for the calibration and evaluation datasets to reduce the error when estimating the uncertainty of a DSM product.

Machine‐Learning Variables at Different Scales vs. Knowledge‐based Variables for Mapping Multiple Soil Properties

Core IdeasExplored the impact of variables selected by different means on mapping at a watershed scale area.Identified influential topographic variables with appropriate scales for mapping soil properties.Finding the appropriate scales of local attributes is helpful for mapping soil properties accurately.Local attribute scale helped map accuracy vs. use of local and regional attributes at single scale.The influential environmental variables and their appropriate scales for mapping different soil properties are usually different. Comparisons between variables selected using machine learning and knowledge‐based approaches and their impacts for soil mapping are necessary to provide guidelines on selecting environmental variables. This study compared topographic variables with single and multiple scales selected for five soil properties (topsoil clay content, sand content, silt content, topsoil organic matter content (SOM), and soil depth) using a recursive feature elimination (RFE) algorithm and variables selected based on expert knowledge on mapping. One hundred and seventy‐three variables were generated including five single‐scale variables derived with a 3 × 3 neighborhood size and seven multi‐scale variables with various neighborhood sizes. Subsets 1 and 2 were selected from single‐scale variables (Pool 1) and all variables (Pool 2), respectively. A reference Subset 3 was selected based on soil pedogenesis knowledge. Results showed that variables in Subset 1 included both local and regional terrain attributes, but were considerably different to the reference variables. When considering scales, local variables at specific scales became the most important variables. Mapping accuracies using Subset 2 were the highest for all soil properties. This indicates that finding the appropriate scales of local attributes and using common local attributes with appropriate scales is more helpful than using both local and regional attributes at single scale for mapping soil properties accurately. The RFE approach is efficient for selection of variables. Also, the input environmental variables for selection determine whether the selected variables perform better than all input variables without selection for predicting soil properties.

Comparing traditional and digital soil mapping at a district scale using residual maximum likelihood analysis

Conventional soil mapping uses field morphological observations to classify soil profiles into predefined classification systems and extrapolates the classified soils to make a map based on aerial photographs and the experience of the surveyor. A criticism of this approach is that the subjectivity of the surveyor leads to non-reproducible maps. Advances in computing and statistical analysis, and an increased availability of ancillary data have cumulatively led to an alternative, referred to as digital soil mapping (DSM). In this research, two agriculturally productive areas (i.e. Warren and Trangie) located in central New South Wales, Australia, were considered to evaluate whether pedoderms and soil profile classes defined according to the traditional approach can also be recognised and mapped using a DSM approach. First, we performed a fuzzy k-means analysis to look for clusters in the ancillary data, which include data from remote-sensed gamma-ray (γ-ray) spectrometry and proximal-sensed electromagnetic (EM) induction. We used the residual maximum likelihood method to evaluate the maps for various numbers of classes (k = 2–10) to minimise the mean square prediction error (σ2p,C) of soil physical (i.e. clay content, field capacity (FC), permanent wilting point (PWP) and available water content (AWC)) and chemical (pH, EC of 1 : 5 soil water extract (EC1:5) and cation exchange capacity (CEC)) properties of topsoil (0–0.3 m) and subsoil (0.6–0.9 m). In terms of prediction, the calculated σ2p,C was locally minimised for k = 8 when accounting for topsoil clay, FC, PWP, pH and CEC, and subsoil FC, EC1:5 and CEC. A comparison of σ2p,C of the traditional (seven pedoderm components) and DSM approach (k = 8) indicated that only topsoil EC1:5 and subsoil pH was better accounted for by the traditional approach, whereas topsoil clay content, and CEC and subsoil clay, EC1:5 and CEC were better resolved using the DSM approach. The produced DSM maps (e.g. k = 3, 6 and 8) also reflected the pedoderm components identified using the traditional approach. We concluded that the DSM maps with k = 8 classes reflected the soil profile classes identified within the pedoderms and that soil maps of similar accuracy could be developed from the EM data independently.

Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus

Whereas contemporary land use and land management planning require specific and quantitative georeferenced soil information, often only general-purpose and qualitative soil maps are available. With a view to fill this gap for topsoil clay and organic carbon content in the central plateaus of Burundi, we tested for a representative 15km2 hilly landscape, six types of SCORPAN models. The SCORPAN models were first applied as standalone trend models and next extended with a component accounting for the spatial autocorrelation of the residuals from the trend. Various sets of predictors, including class variables derived from the available soil map and continuous derivatives from a Digital Elevation Model (DEM) and from Landsat-imagery were incorporated. For clay, the best prediction method was a Residual Kriging (RK) using a Generalized Additive Model (GAM) as trend built with only DEM derivatives and spectral normalized difference vegetation index (NDVI). Furthermore, the classical and simplest RK, i.e. using a Least Squares Linear Regression (LR) trend built with only continuous covariates, outperformed all standalone trend models. For organic carbon, residuals from the trend models were not significantly auto-correlated, making RK meaningless. In this case the best model was a GAM combining lithologic units with DEM derivatives and NDVI. Overall, the contribution of soil map–derived predictors to the model performance was rather weak. It was concluded that, for prediction of specific soil characteristics in the study area, a SCORPAN approach is preferred the more as the performance can be boosted by kriging of trend residuals if auto-correlated.

Local adaptation of a national digital soil map for use in precision agriculture

The publicly available Digital Soil Map of Sweden (DSMS) contains topsoil clay content information in a 50 m×50 m grid, and can be used as decision support in precision agriculture. However, it is also common that farmers have undertaken their own soil sampling (one sample per hectare with texture analysed in every third sample). In the present study, such soil samples from 403 farms were used to validate topsoil clay content information derived from 1) DSMS, 2) DSMS locally adapted by residual kriging, 3) DSMS locally adapted by regression kriging and 4) inverse distance weighting interpolation of the soil sample data without using DSMS. The latter has been common practice until now. The best method differed depending on the local accuracy of DSMS, the quality of the soil sampling and the spatial variation structure of the topsoil texture. The ‘Best method’ strategy, which meant to apply all the above methods and choose the one that performed best at each farm, significantly reduced the mean absolute error. We recommend using this strategy to locally adapt regional digital soil maps to derive accurate decision support for use in precision agriculture.

Response of Vertisols, Andosols, and Alisols to paddy management

Interchanging submergence and drainage in paddy soils induce alternating redox conditions. It is known that this causes changes in organic carbon stocks, in amounts and crystallinity of Fe oxides as well as transformation of clay minerals and subsequent changes in cation exchange capacity (CEC). However, the influence of the initial soil type on the extent of these changes is not yet well understood. Therefore, we studied paddy soils that derived from three different soil types (Vertisols, Andosols, Alisols) on volcanic parent material in Java (Indonesia). To account for the variability in parent materials, we additionally sampled sandstone-derived Alisols in China. Adjacent non-paddy soils were sampled as references. Samples were analyzed for texture, bulk density, clay mineral composition, total element content, pH, CEC, phosphorus retention, organic carbon (OC), and acid oxalate- (Feox) and dithionite–citrate–bicarbonate-extractable Fe (FeDCB).Only the Alisol-derived paddy soil in China showed textural changes, compared to the non-paddy soil. Evidence for paddy management induced ferrolysis was not found. The smaller topsoil clay content in the paddy soil is most probable caused by clay migration. Only minor differences in clay minerals were found; topsoils of Andosol-derived paddy soils, however, tend to be less desilicated, indicating phytolith accumulation. Except for Vertisols, paddy management caused significant depletion in Fe oxides in the topsoils (puddled layer and plow pan) due to redox processes. The extent to which the reduced Fe was leached or re-oxidized as short range-ordered Fe oxides depended on the soil texture. Andosols and sandy Alisols facilitated leaching, clayey Alisols re-oxidation. In either case, the stocks of crystalline Fe oxides diminished, causing increased proportions of short range-ordered Fe oxides. Retention of phosphorus was directly related to changes in the absolute amounts of short range-ordered Fe oxides. An accumulation of Fe oxides in paddy subsoils was not found. Lateral transport with drainage water might be a reason. In highly permeable soils with large vertical water fluxes (e.g., Andosols under paddy management), colloidal transport might also play a role. Despite losses in potential OC storage capacity (i.e., Fe oxides, clay minerals, allophane), paddy soils derived from Andosols and sandy Alisols in China had larger OC concentrations in the puddled topsoil, whereby the other soils showed no increase in OC under paddy management. Therefore, paddy management does not necessarily enhance carbon sequestration. Rather, differences in organic matter input between non-paddy and paddy soils seem to determine whether OC is accumulated under paddy management or not. Effects of paddy management on CEC were little and mainly due to OC accumulation and Fe oxide coating removal from clay minerals.Overall, paddy management-induced changes were partly influenced by the original soil and the parent material. In turn, the main characteristics of the initial soil type were preserved and not overridden by paddy management.

Soil Clay Content Mapping Using a Time Series of Landsat TM Data in Semi-Arid Lands

Clay content (fraction &lt; 2 µm) is one of the most important soil properties. It controls soil hydraulic properties like wilting point, field capacity and saturated hydraulic conductivity, which in turn control the various fluxes of water in the unsaturated zone. In our study site, the Kairouan plain in central Tunisia, existing soil maps are neither exhaustive nor sufficiently precise for water balance modeling or thematic mapping. The aim of this work was to produce a clay-content map at fine spatial resolution over the Kairouan plain using a time series of Landsat Thematic Mapper images and to validate the produced map using independent soil samples, existing soil map and clay content produced by TerraSAR-X radar data. Our study was based on 100 soil samples and on a dataset of four Landsat TM data acquired during the summer season. Relationships between textural indices (MID-Infrared) and topsoil clay content were studied for each selected image and were used to produce clay content maps at a spatial resolution of 30 m. Cokriging was used to fill in the gaps created by green vegetation and crop residues masks and to predict clay content of each pixel of the image at 100 m grid spatial resolution. Results showed that mapping clay content using a time series of Landsat TM data is possible and that the produced clay content map presents a reasonable accuracy (R2 = 0.65, RMSE = 100 g/kg). The produced clay content map is consistent with existing soil map of the studied region. Comparison with clay content map generated from TerraSAR-X radar data on a small area with no calibration point revealed similarities in topsoil clay content over the largest part of this extract, but significant differences for several areas. In-situ observations at those locations showed that the Landsat TM mapping was more consistent with observations than the TerraSAR-X mapping.

Hyper-scale digital soil mapping and soil formation analysis

Landscape characteristics show local, regional and supra-regional components. As a result pedogenesis and the spatial distribution of soil properties are both influenced by features emerging at multiple scales. To account for this effect in a predictive model, descriptors of the geomorphic signature are required at multiple scales. In this study, we present a new hyper-scale terrain analysis approach, referred to as Contextual Statistical Mapping (ConStat), which is based on statistical neighborhood measures derived for growing sparse circular neighborhoods. The statistical measures tested comprise basic descriptors such as the minimum, maximum, mean, standard deviation, and skewness, as well as statistical terrain attributes and directional components. We propose a data mining framework to determine the relevant statistical measures at the relevant scales to analyze and interpret the influence of these statistical measures and to map the geomorphic structures influencing soil formation and the regions where a statistical measure shows influence. We introduce ConStat on two landscape-scale DSM examples with different soil genesis regimes where the ConStat terrain features serve as proxies for multi-scale variations of climate and parent material conditions. The results show that ConStat provides high predictive power. The cross-validated R2 values range from 0.63 for predicting topsoil clay content in the Piracicaba area (Brazil) to 0.68 for topsoil silt content in the Rhine-Hesse area (Germany). The results obtained from data mining analysis allow for interpretations beyond conventional concepts and approaches to explain soil formation. As such it overcomes the trade-off between accuracy and interpretability of soil property predictions.

Sensor data fusion for topsoil clay mapping

The study investigated proximal sensor data fusion for topsoil clay mapping on a 22hectare agricultural field in southwest Sweden. Eight different predictor sets and two different prediction methods were tested in an orthogonal design. The predictor sets were different combinations of proximally measured gamma (γ) ray spectrometry and apparent electrical conductivity (ECa), four terrain attributes (elevation, slope and the cosine and the sine of the aspect) and the digital numbers (DNs) of an aerial photo. The two prediction methods were partial least squares regression (PLS-R) and k nearest neighbor prediction (kNN). It was found that the γ ray spectrometry variables (232Th, 40K and total count of decays) were good predictors of topsoil clay content (mean absolute error of about 1.5% clay) and predictions were neither much improved nor deteriorated by addition of any of the other predictors. The ECa measurements, which are affected also by the subsoil, did not perform as well. Predictions were improved when the ECa data were integrated with the aerial photo DN but were deteriorated by addition of elevation data. The kNN method yielded slightly better predictions than the PLS-R method but overall it was more important which input data were used than how the predictions were made. It was observed that even though dense soil sampling was used for calibration (three samples per hectare), use of proximal soil sensor data was almost always better than mere interpolation of the calibration samples.

Extracting Topsoil Information from EM38DD Sensor Data using a Neural Network Approach

Electromagnetic induction soil sensors are an increasingly important source of secondary information to predict soil texture. In a 10.5‐ha polder field, an EM38DD survey was performed with a resolution of 2 by 2 m and 78 soil samples were analyzed for sub‐ and topsoil texture. Due to the presence of former water channels in the subsoil, the coefficient of variation of the subsoil clay content (45%) was much larger compared with the topsoil (13%). The horizontal (ECa–H) and vertical (ECa–V) electrical conductivity measurements displayed a similar pattern, indicating a dominant influence of the subsoil features on both signals. To extract topsoil textural information from the depth‐weighted EM38DD signals we turned to artificial neural networks (ANNs). We evaluated the effect of different input layers on the ability to predict the topsoil clay content. To identify the response of the topsoil, both EM38DD orientations were used. To examine the influence of the local neighborhood, contextual ECa information by means of a window around each soil sample was added to the input. The best ANN model used both ECa–H and ECa–V data but no contextual information: a mean squared estimation error of 2.83%2 was achieved, explaining 65.5% of the topsoil clay variability with a variance of 0.052%2 So, with the help of ANNs, the prediction of the topsoil clay content was optimized through an integrated use of the two EM38DD signals.

Modeling water flow and bacterial transport in undisturbed lysimeters under irrigations of dairy shed effluent and water using HYDRUS-1D

HYDRUS-1D was used to simulate water flow and leaching of fecal coliforms and bromide (Br) through six undisturbed soil lysimeters (70 cm depth by 50 cm diameter) under field conditions. Dairy shed effluent (DSE) spiked with Br was applied to the lysimeters, which contained fine sandy loam layers. This application was followed by fortnightly spray or flood water irrigation. Soil water contents were measured at four soil depths over 171 days, and leachate was collected from the bottom. The post-DSE period simulations yielded a generally decreased saturated water content compared to the pre-DSE period, and an increased saturated hydraulic conductivity and air-entry index, suggesting that changes in soil hydraulic properties (e.g. via changes in structure) can be induced by irrigation and seasonal effects. The single-porosity flow model was successful in simulating water flow under natural climatic conditions and spray irrigation. However, for lysimeters under flood irrigation, when the effect of preferential flow paths becomes more significant, the good agreement between predicted and observed water contents could only be achieved by using a dual-porosity flow model. Results derived from a mobile-immobile transport model suggest that compared to Br, bacteria were transported through a narrower pore-network with less mass exchange between mobile and immobile water zones. Our study suggests that soils with higher topsoil clay content and soils under flood irrigation are at a high risk of bacteria leaching through preferential flow paths. Irrigation management strategies must minimize the effect of preferential flow to reduce bacterial leaching from land applications of effluent.

Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators

Sustainable land management and land use planning require reliable information about the spatial distribution of the physical and chemical soil properties affecting both landscape processes and services. Although many studies have been conducted to identify the spatial patterns of soil property distribution on various scales and in various landscapes, only little is known about the relationships underlying the spatial distribution of soil properties in intensively used, finely structured paddy soil landscapes in the southeastern part of China. In order to provide adequate soil information for the modelling of landscape processes, such as soil water movement, nutrient leaching, soil erosion and plant growth, this study investigates to what extent cheap and readily available ancillary information derived from digital elevation models and remote sensing data can be used to support soil mapping and to indicate soil characteristics on the landscape scale. This article focuses on the spatial prediction of the total carbon and nitrogen content and of physical soil properties such as topsoil silt, sand and clay content, topsoil depth and plough pan thickness. Correlation analyses indicate that, on the one side, the distribution of C, N and silt contents is quite closely related to the NDVI of vegetated surfaces and that, on the other side, it corresponds significantly to terrain attributes such as relative elevation, elevation above nearest drainage channel and topographical wetness index. Geostatistical analyses furthermore reflect a moderately structured spatial correlation of these soil variables. The combined use of the above mentioned terrain variables and the NDVI in a multiple linear regression accounted for 29% (silt) to 41% (total C) of the variance of these soil properties. In order to select the best prediction method to accurately map soil property distribution, we compared the performance of different regionalization techniques, such as multi-linear regression, simple kriging, inverse distance to a power, ordinary kriging and regression kriging. Except for the prediction of topsoil clay content, in all cases regression kriging model “C” performed best. Compared to simple kriging, the spatial prediction was improved by up to 14% (total C), 13% (total N) and 10% (silt). Since the autocorrelation lengths of these spatially well correlated soil variables range between three and five times the soil sampling density, we consider regression kriging model “C” to be a suitable method for reducing the soil sampling density. It should help to save time and costs when doing soil mapping on the landscape scale, even in intensively used paddy soil landscapes.