Topsoil Texture Research Articles

How Accurately Is Topsoil Texture Shown on Agricultural Soil Maps? A Case Study of Eleven Fields Located in Poland

How Accurately Is Topsoil Texture Shown on Agricultural Soil Maps? A Case Study of Eleven Fields Located in Poland

Investigating environmental and geographical factors affecting iodine concentrations in Australian wheat (Triticum aestivum L.) grain

Iodine is an essential micronutrient for human nutrition, though it is found in relatively low concentrations in many important crop species. Wheat (Triticum aestivum L.) is a common staple crop worldwide, and as such could be an important source of dietary iodine due to its widespread consumption. However, little is known about iodine concentrations in wheat grain grown under rainfed field conditions, nor the impact of growing region or environment on these concentrations. Therefore, this paper had three objectives; (1) quantify the iodine concentration in a popular variety of wheat cultivated across the wheat belt of three Australian States (Western Australia, South Australia and Victoria) over two winter seasons (2) determine the influence of distance from the coast, rainfall, elevation, soil type and pH and grain yield on wheat grain iodine concentrations and (3) identify geographical areas where iodine concentrations of wheat grains are low enough that biofortification with iodine would be advantageous for human health outcomes. We sampled iodine concentrations of a single cv. Scepter at 125 sites from the winter season 2020 (65 sites) and 2021(60 sites), to investigate environmental and geographical effects on wheat grain iodine concentrations. Iodine concentrations were measured using triple quadrupole inductively coupled plasma mass spectrometer (ICP MS/MS). We found that the elevation and the region (State) of growing sites were the most significant predictors of iodine concentration, along with the interaction between rainfall and topsoil texture. However, very low concentrations of iodine (5–24 μg/kg) were detected in all samples tested, indicating that even wheat grown under advantageous environmental and geographic conditions in southern Australia would be unlikely to represent an important source of dietary iodine. This emphasises the need to consider biofortification strategies to improve iodine concentrations in Australian grown wheat to improve the dietary uptake of this essential micronutrient by human consumers.

Geospatial evaluation of the agricultural suitability and land use compatibility in Europe's temperate continental climate region

Land suitability assessment is used in conjunction with geographic information systems to spatially model diverse aspects of soil functions, having the potential to facilitate a sustainable increase in agricultural production, reduce land degradation, or aid humans in adapting to climate change. Compared to the existing datasets, this study provides a new higher resolution geospatial assessment of the agricultural land suitability for several crops and land uses in the temperate continental climate across Europe. To model the land suitability we used geospatial data depicting seventeen eco-pedological indicators (e.g. soil texture, pH, porosity, temperature, precipitation, slope). To evaluate how the land is utilized, the suitability maps have been spatially cross-tabulated with a crop map. Over the entire study area, wheat and barley showed significant suitable land in the southern part, potatoes, and sugar beet exhibited the highest extent of suitable land in the northern parts, while corn and sunflower exhibited a much lower extent of suitable land. Water table depth, precipitation, temperature, terrain slope, soil porosity, SOC, and topsoil texture emerged as the limiting factors for agricultural suitability in the study area. Our results show that the suitable arable land does not have space left for the expansion of crops, however, we have identified regions with extensive cultivation of wheat and corn on unsuitable land with the potential for cultivation of more suitable crops such as barley, sunflower, sugar beet, and potato. It seems that one action that can enhance agricultural practices in the study area is to better allocate each cultivated crop across more suitable lands.

Exploring the untapped potential of hand-feel soil texture data for enhancing digital soil mapping: Revealing hidden spatial patterns from field observations

Digital soil mapping (DSM) is commonly conducted using input soil attributes derived from laboratory analyses of geographically referenced samples. Field observations are often abundant and can offer a dense source of soil data that has the potential to enhance DSM predictions. However, they are not widely used due to to concerns about subjectivity and data quality. This study investigates the usefulness of hand-feel soil texture (HFST) data for DSM. We processed HFST data obtained from forest soils in France from two inventory campaigns: (i) HFST determination from systematic 1 km2 grid observations in France utilizing a specialized soil texture triangle, and (ii) HFST observations from soil survey samples, using a different texture triangle. Both sets of HFST data were used as input soil variables, with the same covariates, for predicting topsoil texture In a sizable, forested area through a DSM method. By employing independent sampling and laboratory soil analyses in selected areas, we uncovered measurement bias in one of the datasets. However, intriguingly, these biased observations identified subtle yet highly specific and unexpected patterns of sands in terraces due to alluvial deposits along small rivers. Thus, field soil observations, even if they are biased, should not be dismissed solely based on their overall predictive performance. It is essential to carefully examine predicted maps and covariates to determine whether patterns may have pedological and/or lithological origins and if they are pertinent for enhancing DSM predictions, enhancing soil process understanding, and meeting the requirements of end users. Numerous HFST are available worldwide, these datasets are usually disregarded for DSM. Here we contend that efforts should be put in recovering these data, and their potential for enhancing DSM and deepening our understanding of soil processes.

Regional topsoil organic carbon content in the agricultural soils of Slovakia and its drivers, as revealed by the most recent national soil monitoring data

Soil organic carbon (SOC) is a primary constituent of soil organic matter and plays an important role in the regulation of many soil processes, including greenhouse gas emissions. Recently, SOC also became an indicator for monitoring climate change mitigation policies in the agricultural sector. The availability of up-to-date SOC inventories is thus crucial in terms of supporting SOC–related actions at country or sub-country scales. Currently, the National Monitoring System of the Agricultural Soils of Slovakia (CMS-P), whose network of 318 monitoring sites was last surveyed in 2018, is the only available source of up-to-date topsoil SOC data for agricultural land in Slovakia. Although very useful at the national scale, the number of CMS-P observations it contains is too limited for much needed sub-national SOC inventories. We hypothesized that with the aid of well-chosen macro-scale drivers of topsoil SOC accumulation in agricultural land in Slovakia, and by mapping those drivers geographically, we could upscale the CMS-P observations and produce a regional estimate of topsoil SOC. Altitude, land cover, topsoil texture, and soil type were assumed to be the key factors controlling topsoil SOC accumulation in Slovakia, and based on these, the country was classified into 14 macro-scale geographical regions. Typical ranges and mid-class values of 0–30cm topsoil SOC concentrations (%) and stocks (t ha−1) were calculated for each macro-scale region from CMS-P data. The average topsoil SOC content in agricultural land was estimated to be 2.13% (72.9 t ha−1). The highest topsoil SOC stock (> 90 t ha−1) was estimated for the lowlands of Slovakia, and the lowest (< 50 t ha−1) for the shallow and stony soils of mountain regions. When aggregated to 78 administrative regions at LAU1 level, the area-weighted averages ranged between 39.20 t ha−1 and 80.0 t ha−1, with the highest values (> 65 t ha−1) being in LAU1 regions in the south-west, south-east, and north of Slovakia where arable land is most prevalent. Total SOC storage in 0–30cm topsoil of agricultural land in Slovakia was estimated at 118.39 Mt, with two-thirds of this amount stored in arable soils in 33 south-west, south-east, and south LAU1 administrative regions. As there is no alternative and up-to-date dataset on topsoil SOC content in Slovakia, the upscaling algorithm presented in this study is an important step toward utilizing CMS-P data for sub-national SOC inventories. It may also offer a new way of providing inputs to help predict future or alternative regional topsoil SOC accumulation trajectories in Slovakian agricultural land using process-based or statistical models.

Search for the Substantiation of Reasonable Native Elemental Background Values and Reference Variables in Topsoil on Glaciogenic and Postglacial Deposits in a Vilnius Peri-Urban Area

Geochemical indices used to identify the emerging anomalies of potentially harmful elements in topsoil depend on background values (BVs). For urban sites, it is reasonable to estimate native BVs through the targeted selection of peri-urban sampling sites or by distinguishing a useful background subset (BS) within the peri-urban dataset. Here, the goals were to examine the influence of Quaternary deposits on various types of topsoil variables, identify the variables most helpful for cluster analysis intended for the choice of background subset (BS), and compare background values (BVs) based on different background subsets. Composite topsoil samples from a peri-urban area were used for the determination of the following variables: contents of 26 elements and components of the bulk mineralogical composition, as well as the sand, silt, and clay fractions and loss-on-ignition (LOI) at 550 °C and at 950 °C. Although Quaternary lithology influences topsoil elemental contents or granulometric fractions, percentages of illite, kaolinite, orthoclase, quartz, albite, dolomite, and LOI at 550 °C, the choice of BS, according to it, is not recommended, as BVs based on topsoil texture are superior. However, cluster analysis using topsoil fractions &lt; 2, &lt;63, and &gt;63 μm or the contents of Al, Fe, K, Ti, Ga, Nb, Rb, and Si are preferable. It is recommended to use these reference variables for the selection of BS.

Hyperspectral Mixture Models in the CHIME Mission Implementation for Topsoil Texture Retrieval

AbstractThis study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 &gt; 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes.

Termite mound impacts on hydrology vary with herbaceous vegetation and topsoil texture

There are many termite mounds in African savannas, where their interactions with vegetation and their potential role in redistributing water are of significant interest for ecosystem function and services. Here we focus on termite mounds as structural components of the environment and sample both bare and grassy termite mounds across a topsoil texture gradient to assess mound hydrological properties and to explore the potential for termite mound-mediated water redistribution to affect vegetation growth in water-limited savanna systems. We found mound vegetation cover has distinct and opposing effects on water infiltration rates; bare mounds consistently slow infiltration rates across the topsoil texture gradient, whereas infiltration on grassy mounds is faster and more variable, particularly in finer-textured topsoil. Given the consistently slow infiltration rates on bare mounds, we infer that bare termite mounds have the potential to redistribute water locally during rainfall events, and suggest this may be partially responsible for inhibiting vegetation colonization of the mound, while having potentially beneficial consequences for mound-adjacent vegetation.

Effects of soil and water conservation practices and landscape position on soil physicochemical properties in Anuwa watershed, Southern Ethiopia

Ethiopia's main environmental problem has been soil erosion. To control and reverse the problem, soil and water conservation (SWC) techniques have been deployed over the past two to three decades. The purpose of this study is to evaluate the effect of implemented soil and water conservation measures on soil physicochemical properties in the Anuwa watershed, southern Ethiopia, under various conservation structures (Soil bund, Fanya Juu, Stone bund and Micro Basin) and topographic positions (upper, middle and lower) of conserved plots. The soil samples were collected from 45 locations to analyze topsoil texture, bulk density (ρd), pH, soil organic carbon (SOC), total nitrogen (TN), available phosphorus (AP), CEC, exchangeable Na+, K+, Ca2+, and Mg2+. All soil samples were analyzed using standard and recommended methods. Using the SPSS computer program 21, the effect of independent variables (SWC practices and landscape position) on the dependent variables (soil properties) was statistically tested. The significance test was performed using two-way ANOVA. The findings indicated that the SWC measure significantly decreases the percent values of sandy soil texture, while it increased the mean values of organic carbon, available phosphorus, exchangeable Na+, K+, Ca2+, Mg2+, and CEC (P < 0.05). The sand content, the bulk density, the SOC, and the total nitrogen showed statistically significant variations between the conserved and non-conserved upper topography (P < 0.05). The effects of conservation measures were significant in almost all soil physicochemical properties at P < 0.01 except for the silt content, bulk density, and Ca2+ in the lower topography. The study findings revealed that Fanya Juu SWC measures are by far the most efficient, and recommended method to minimizing soil erosion, and improving selected soil physical and chemical quality in the Anuwa watershed. Local governments and other stakeholders should work cooperatively to strengthen and sustain the implementation process.

Particle Size Distribution and Depth to Bedrock of Chinese Cultivated Soils: Implications for Soil Classification and Management

Although a number of studies have provided information on soil texture, soil classification, and depth to bedrock throughout China, few studies have combined this information, which is the basis for agricultural field management. A total of 81% of China’s cultivated lands are distributed among the Middle–Lower Yangtze Plain (18.2%), arid and semiarid North China Plain (18.2%), Northeast Plain (17%), Huang-Huai-Hai Plain (16.1%), and Yunnan–Guizhou Plateau (11.6%). The Huang-Huai-Hai Plain has the highest density of agricultural land (58.5%) and the greatest depth to bedrock of cultivated land (243–402 m). The lowest cultivated depth to bedrock (4–84 m) is concentrated in the Sichuan Basin and its surrounding regions. The main cultivated soil types are Anthrosols, Fluvisols, Cambisols, Phaeozems, Luvisols, Kastanozems, Leptosols, and Acrisols, under the main topsoil texture classes of loam, clay loam, silty clay loam, silt loam, sandy loam, and clay. The Fluvisols had the largest depth to bedrock (156 m) on the Middle–Lower Yangtze Plain and Huang-Huai-Hai Plain, with the highest silt soil distributions but comparable lower sand contents. The Yunnan–Guizhou Plateau had the highest clay soil content. The cultivation under Kastanozems and Leptosols on the Qinghai–Tibet Plateau and in arid and semiarid North China and under Phaeozems on the Northeast Plain should be restricted and managed very cautiously facing erosion risk. The higher percentages of Anthrosols are on the Middle–Lower Yangtze Plain (37%), in Southern China (32%), and on the Yunnan–Guizhou Plateau (26%). The same cultivation aim (i.e., more crop 0production) has produced a similar range of properties over time among the soils developed on agricultural fields, which are classified as Anthrosols. However, various soil types can still be found in agroecosystems because of the variations in climate and topography. Our results highlight that the agriculture-based soil climate and topography shape the interaction of the soil development and not only the pedogenic history of the soil development under variations in the soil depth to bedrock but also the cultivation of distinct pedogenic features. This study provides cultivated soil information on the depth to bedrock, soil classification, and soil texture in China, as well as instructions for field strategies for sustainable agricultural development.

Groundwater potential mapping in Jashore, Bangladesh

The global groundwater crisis is a perplexing issue, and for its resolution, it is of the utmost importance for delineating groundwater potential zones. This research aims to create a precise groundwater potential map for Bangladesh's Jashore district by combining geospatial approach and an analytical hierarchy process. Fourteen parameters, namely, lineament density, drainage density, land use and land cover, slope, curvature, topographic position index, topographic wetness index, rainfall, geology, roughness, fractional impervious surface, topsoil texture, soil permeability, and general soil types, were considered for the study after an extensive literature review. The weights of these parameters were determined using an analytical hierarchy process, and the scores of each sub-parameter were assigned based on published literature. The final groundwater potential map was then generated using the weighted overlay analysis tool in ArcGIS 10.3 and categorized into five classes. The analysis reveals that very high, high, moderate, low, and very low groundwater potential zones cover 3.96 km2 (0.16%), 444.75 km2 (17.72%), 1615.51 km2 (64.37%), 441.79 km2 (17.60%), and 3.59 km2 (0.14%) of the study area, respectively. The map removal sensitivity analysis shows that geology is the most significant element in groundwater potential zoning, followed by land use and land cover (LULC), slope, and topsoil texture as moderately sensitive elements. Since the groundwater potentiality zones of the study region are clearly delineated, this research may be valuable for implementing an appropriate water resource management strategy.

Errors in soil maps: The need for better on-site estimates and soil map predictions.

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.

Digital Mapping of Topsoil Texture Classes Using a Hybridized Classical Statistics–Artificial Neural Networks Approach and Relief Data

The demand for high quality and low-cost spatial distribution information of soil texture classes (STCs) is of great necessity in developing countries. This paper explored digital mapping of topsoil STCs using soil fractions, terrain attributes and artificial neural network (ANN) algorithms. The 4493 soil samples covering 10 out of 12 STCs were collected from the rice fields of the Guilan Province of Northern Iran. Nearly 75% of the dataset was used to train the ANN algorithm and the remaining 25% to apply a repeated 10-fold cross-validation. Spatial prediction of soil texture fractions was carried out via geostatistics and then a pixel-based approach with an ANN algorithm was performed to predict STCs. The ANN presented reasonable accuracy in estimating USDA STCs with a kappa coefficient of 0.38 and pixel classification accuracy percentage of 52%. Hybridizing soil particles with relief covariates yielded better estimates for coarse- and medium-STCs. The results also showed that clay particle and terrain attributes are more important covariates than plant indices in areas under single crop cultivation. However, it is recommended to examine the approach in areas with diverse vegetation cover.

Digital mapping for soil texture class prediction in northwestern Türkiye by different machine learning algorithms

Soil texture classes (STCs) influence the physical, chemical and biological properties of the soil, and accurate spatial predictions of STCs are essential for agro-ecological modeling. The purpose of this study was to assess the capabilities of environmental covariates derived from Landsat 8 OLI Science products and a digital elevation model (DEM), as well as three machine learning methods, to provide an accurate and reliable classification of soil texture classes. Estimation efficiency of soil texture classes was investigated using Decision tree (DT), Random forest (RF), and Support vector machine (SVM) algorithms in an area with Fluvisols and Vertisols as predominant reference soil groups in Northwestern Türkiye (Lake of Manyas). The models were validated using the leave-one-out, cross-validation technique. The best of the three machine learning models for soil texture classification was RF, with an overall accuracy of 0.63 and a kappa index value of 0.14, according to the accuracy evaluation. The RF algorithm generated a map whose findings were more consistent with the real environment using the confusion index (RF:0.30) and abundance index (RF:1.0) values as uncertainty criteria. The most important predictor of soil texture classes was the topographic wetness index (mean decrease accuracy: 3%) for tree-based learning algorithms, followed by other indexes based on satellite data. These findings will contribute to support sustainable soil management techniques in a location with a large degree of topsoil texture diversity.

Topsoil Texture Regionalization for Agricultural Soils in Germany—An Iterative Approach to Advance Model Interpretation

Site-specific spatially continuous soil texture data is required for many purposes such as the simulation of carbon dynamics, the estimation of drought impact on agriculture, or the modeling of water erosion rates. At large scales, there are often only conventional polygon-based soil texture maps, which are hardly reproducible, contain abrupt changes at polygon borders, and therefore are not suitable for most quantitative applications. Digital soil mapping methods can provide the required soil texture information in form of reproducible site-specific predictions with associated uncertainties. Machine learning models were trained in a nested cross-validation approach to predict the spatial distribution of the topsoil (0–30 cm) clay, silt, and sand contents in 100 m resolution. The differential evolution algorithm was applied to optimize the model parameters. High-quality nation-wide soil texture data of 2,991 soil profiles was obtained from the first German agricultural soil inventory. We tested an iterative approach by training models on predictor datasets of increasing size, which contained up to 50 variables. The best results were achieved when training the models on the complete predictor dataset. They explained about 59% of the variance in clay, 75% of the variance in silt, and 77% of the variance in sand content. The RMSE values ranged between approximately 8.2 wt.% (clay), 11.8 wt.% (silt), and 15.0 wt.% (sand). Due to their high performance, models were able to predict the spatial texture distribution. They captured the high importance of the soil forming factors parent material and relief. Our results demonstrate the high predictive power of machine learning in predicting soil texture at large scales. The iterative approach enhanced model interpretability. It revealed that the incorporated soil maps partly substituted the relief and parent material predictors. Overall, the spatially continuous soil texture predictions provide valuable input for many quantitative applications on agricultural topsoils in Germany.

Effects of soil properties on vegetation community composition across a semiarid ecosystem

Identifying environmental factors that influence the vegetation community formation of various habitats in semiarid regions is imperative for managing these fragile ecosystems. The objective of this study was to quantify the relationships between selected environmental factors and vegetation community composition (VCC) in meadow and sand dune habitats using a structural equation model (SEM). Ecological data from 48 sample sites in semiarid sandy land were analyzed. The selected latent environmental variables included soil water (SW), topsoil salinity and sodicity, topsoil nutrients, topsoil texture (TT) and topography feature (TF). SW was the most influential factor for the Phragmites communis, Agriophyllum squarrosum, and Caragana microphylla communities composition. TT was the most influential factor for the Artemisia halodendron and Artemisia frigida - Agropyron desertorum communities composition. TF was the most influential factor for the Carex duriuscula and Phragmites communis - Bolboschoenus yagara communities composition. Considering the direct and indirect impact relationship, for the meadow habitat, TF was the primary factor causing the community distribution. For the sand dune habitat, TT was the primary factor causing the community distribution. As expected, the quantitative relationships between VCC and influencing factors differ depending on the ecological scales of interest.

Spatial modeling of radon potential mapping using deep learning algorithms

Radon potential mapping is challenging due to the limited availability of information. In this study, a new modeling process using deep learning models based on convolution neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) is presented to predict radon potential in the northwestern part of Gangwon Province, South Korea. The used data in this study are in two sets of dependent variables (measured soil gas radon concentrations) and independent variables (radon conditioning factors: lithology; distance from lineament; mean soil calcium oxide [Cao], potassium oxide [K2O], and ferric oxide [Fe2O3] concentrations; effective soil depth; topsoil texture; and soil drainage). The models were validated based on the area under the receiver operating curve (), mean squared error (), root mean square error (), and standard deviation (). The CNN model with values of 0.906 and 0.905 in the learning and testing stages, respectively, is introduced as the optimal model. The lowest and values were from the CNN, LSTM, and RNN models, respectively. Our results show that the use of deep learning models to generate radon potential maps is promising and reliable.

Revised map of European aeolian deposits derived from soil texture data

European aeolian deposits have been mapped using the LUCAS topsoil texture database and the location, thickness and grain size mode of loess sections described in the literature have been compiled. The map, in fairly good agreement with those based on a classical geological approach, has the advantage of being of equal precision for the whole study area and makes it possible to highlight grain size gradients. It shows three main types of aeolian systems, i.e. systems associated with ice sheets (FIS, BIIS, AIS) and the rivers they feed, continental systems related to the erosion of sedimentary rocks, and coastal systems. Each aeolian system is comprised of a band of coversands of varying extent close to the sources and bands of sandy loess and loess away from the sources. The thickness of loess is highly variable throughout Europe. The areas of greatest accumulation are associated with the rivers draining the AIS (Rhine, Danube, Rhône). The Middle Danube, which combines sources of glacial origin (AIS) and local sources, constitutes the main accumulation area in Europe. Conversely, continental and coastal systems generated only limited loess cover. The latitudinal gradient of vegetation probably played an important role in loess sedimentation during the LGM. Weak interference between particle transport and vegetation (periglacial desert in the sand belt, cryptogamic crusts and steppe in the north European loess belt) allowed for particle sorting through aeolian transport to develop. The loess band is extensive in low-relief areas and is typified by a low sand content. In southern Europe, on the other hand, the capture of saltating and suspended particles by the shrubby steppe vegetation led to the dominant accumulation of sandy loess near sources.

Paleocryogenesis as a Factor of Heterogeneity of Agro-Gray Soil

Delineation of the features of paleocryogenic microtopography by zero-curvature morphoisographs on a test plot in the south of Moscow oblast enabled us to reveal the polygonal-blocky microrelief (microelevations, microdepressions, slopes, and runoff strips), forming eluvial, trans-eluvial, and trans-accumulative agrolandscapes. The soil cover pattern of eluvial agrolandscape includes elementary soil areas of noneroded, slightly eroded, moderately eroded, and aggraded soils. The agrolandscape is dominated by noneroded soils. The soil cover pattern of trans-eluvial agrolandscapes with microdepressions includes elementary soil areas of slightly, moderately, and strongly eroded soils, as well as eroded–aggraded and aggraded soils, with the dominations of slightly eroded soils. The soil cover pattern of trans-accumulative agrolandscapes also includes elementary soil areas of slightly, moderately, and strongly eroded soils; eroded–aggraded soils; and aggraded soils with the domination of strongly eroded soils. The paleocryogenic microrelief became the initial factor of the formation of elementary soil areas with different erosion rates during the long-term (200 years) agricultural use of these lands. Elementary soil areas with different erosion rates of the upper horizons are characterized by outcropping of underlying horizons of different textures. We have identified silt loam, light clay, and clay loam exposed to the surface by erosion on the studied plot. As a result, a complex soil cover pattern with differentiation of soils at the level of varieties (in Russian soil classification system, a taxonomic category based on the topsoil texture) was formed.

The Generation of Soil Spectral Dynamic Feedback Using Landsat 8 Data for Digital Soil Mapping

The soil spectral dynamic feedback captured from high temporal resolution remote sensing data, such as MODIS, during the soil drying process after a rainfall could assist with digital soil mapping. However, this method is ineffective in utilizing the images with a relatively high spatial resolution. There are an insufficient number of images in the soil drying process since those high spatial resolution images tend to have a low temporal resolution. This study is aimed at generating soil spectral dynamic feedback by integrating the feedback captured from the images with a high spatial resolution during the process of multiple drying after different rainfall events. The Landsat 8 data with a temporal resolution of 16 day was exemplified. Each single spectral feedback obtained from Landsat 8 was first adjusted to eliminate the impact of different rainfall magnitudes. Then, the soil spectral dynamic feedback was reorganized and generated based on the adjusted feedback. Finally, the soil spectral dynamic feedback generated based on Landsat 8 was used for mapping topsoil texture and compared with the mapping results based on the MODIS data and the fusion data of MODIS and Landsat 8. As revealed by the results, not only could the generated soil spectral dynamic feedback based on Landsat 8 data improve the details of the spatial distribution of soil texture, but it also enhances the accuracy of mapping. The mapping accuracy based on Landsat 8 data is higher than that based on the MODIS data and fusion data. The improvements of accuracy are more obvious in the areas with more complex surface conditions. This study widens the scope of application for soil spectral dynamic feedback and provides support for large-scale and high-precision digital soil mapping.