Spatial Distribution Of Soil Texture Research Articles

Predicting the Surface Soil Texture of Cultivated Land via Hyperspectral Remote Sensing and Machine Learning: A Case Study in Jianghuai Hilly Area

Soil reflectance spectra and hyperspectral images have great potential to monitor and evaluate soil texture in large-scale scenarios. In hilly areas, sand, clay, and silt have similar spectral characteristics in visible, near-infrared, and short-wave infrared (VNIR-SWIR) reflection spectra. Soil texture spectra belong to mixed spectra despite some differences in particle size, mineral composition, and water content, making their distinction difficult. The accurate identification of the content within different particle sizes is difficult as it involves capturing spectral reflection features. Therefore, this study aimed to predict soil texture content through machine learning and unmixing the soil texture’s spectra while also comparing their respective modelling performances. Taking typical cultivated land in the Jianghuai hills as an example, the GaoFen-5 Advanced Hyperspectral Imaging (GF-5 AHSI) laboratory spectra of soil samples were used to predict sand, silt, and clay particle contents using partial least squares regression (PLSR) and convolutional neural networks (CNNs). The entire spectra of VNIR-SWIR regions were smoothed, and the dimensions were reduced via principal component analysis (PCA). The prediction models of sand, silt, and clay particle content were constructed, and inversion maps were generated using AHSI. The results showed that the PCA-CNN model achieved a higher prediction precision than the PCA-PLSR in both ASD and GF-5 data. Clay content exhibited the highest predictive performance with a coefficient of determination (R2) of 0.948 and 0.908 and a root mean square error (RMSE) of 26.51 g/kg and 31.24 g/kg, respectively, which represented a 39.0% and 79.8% increase in R2 and a 57% and 57.1% decrease in RMSE compared to that of the PCA-PLSR. This method indicates that the PCA-CNN model can effectively achieve nonlinear interactions between multiple spectral components and better model and fit spectral mixing processes; moreover, it provides an alternative method for investigating the spatial distribution of soil texture.

Colombian soil texture: building a spatial ensemble model

Abstract. Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km2 of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, Varón-Ramírez and Araujo-Carrillo, 2022, https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, Varón-Ramírez et al., 2022) provide new insights to solve some of the aforementioned issues.

Soil Texture and Its Relationship with Environmental Factors on the Qinghai–Tibet Plateau

Soil texture data are the basic input parameters for many Earth System Models. As the largest middle–low altitude permafrost regions on the planet, the land surface processes on the Qinghai–Tibet Plateau can affect regional and even global water and energy cycles. However, the spatial distribution of soil texture data on the plateau is largely unavailable due to the difficulty of obtaining field data. Based on collection data from field surveys and environmental factors, we predicted the spatial distribution of clay, silt, and sand contents at a 1 km resolution, from 0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm soil depth layers. The random forest models were constructed to predict the soil texture according to the relationships between environmental factors and soil texture data. The results showed that the soil particles of the QTP are dominated by sand, which accounts for more than 70% of the total particles. As for the spatial distribution, silt and clay contents are high in the southeast plateau, and low values of silt and clay mainly appeared in the northwest plateau. Climate and NDVI values are the most important factors that affect the spatial distribution of soil texture on the QTP. The results of this study provide the soil texture data at different depths for the whole plateau at a spatial resolution of 1 km, and the dataset can be used as an input parameter for many Earth System Models.

Limited role of soil texture in mediating natural vegetation response to rainfall anomalies

Evidence suggests that the response of rainfed crops to dry or wet years is modulated by soil texture. This is a central tenet for certain agronomic operations in water-limited regions that rely on spatial distribution of soil texture for guiding precision agriculture. In contrast, natural vegetation in climatic equilibrium evolves to form a dynamic assemblage of traits and species adapted to local climatic conditions, primarily precipitation in water-limited regions. For undisturbed landscapes, we hypothesize that natural vegetation responds to rainfall anomalies irrespectively of local soil texture whereas rainfed crops are expected to respond to texture-mediated plant available water. Earth system models (ESMs) often quantify vegetation response to drought and water stress based on traditional agronomic concepts despite fundamental differences in composition and traits of natural vegetation and crops. We seek to test the hypothesis above at local and regional scales to differentiate natural vegetation and rainfed crops response to rainfall anomalies across soil types and better link them to water and carbon cycles. We employed field observations and remote sensing data to systematically examine the response of natural and rainfed cropped vegetation across biomes and scales. At local scales (field to ∼0.1 km), we used crop yields from literature data and natural vegetation productivity as gross primary productivity (GPP) from adjacent FLUXNET sites. At regional scales (∼102 km), we rely exclusively on remote-sensing-based GPP. Results confirm a lack of response of natural vegetation productivity to soil texture across biomes and rainfall anomalies at all scales. In contrast, crop yields at field scale exhibit correlation with soil texture in dry years (in agreement with conventional agronomic practices). These results support the hypothesis that natural vegetation is decoupled from soil texture, whereas rainfed crops retain dependency on soil texture in dry years. However, the observed correlation of crops with soil texture becomes obscured at larger scales by spatial variation of topography, rainfall, and uncertainty in soil texture and GPP values. The study provides new insights into what natural vegetation’s climatic equilibrium might mean and reveals the role of scale in expressing such sensitivities in ESMs.

Soil Texture Distribution for East Wasit Province, Iraq

Soil texture affects many physical and chemical properties of soil. Knowledge of soil texture is essential for all water and soil studies. The aim of the research is to draw a map of the spatial distribution of soil texture in the region of eastern Wasit province and know the relationship of texture to the soil’s hydrological groups. Laboratory tests were conducted on 25 soil samples. With a depth of 50-75 cm, were selected from locations that represent the study area. According to the unified classification system, The results showed that the soil texture for the samples locations was 40% sand, 16% for both silt loam and sandy loam, 12% for loamy sand, 8% for both sandy clay loam and sandy loam. A soil texture classification map was produced for the study area. The first soil texture map for the area differs significantly from the World Food and Agriculture Organization soil texture classification map. It adopts signed tests of the site. The statistical analysis showed that the per cent sand’s standard deviation was 22.65%, silt 19.247%, and 6.416% clay. It turns out that 52% of the soil models from hydrologic group A, 24% from hydrologic group B and 24% from hydrologic group C, Arc GIS software was used to produce maps.

Soil texture prediction through stratification of a regional soil spectral library

Knowing the spatial distribution of soil texture, which is a physical property, is essential to support agricultural and environmental decision making. Soil texture can be estimated using visible, near infrared, and shortwave infrared (Vis-NIR-SWIR) spectroscopy. However, the performance of spectroscopic models is variable because of soil heterogeneity. Currently, few studies address the effects of soil sample variability on the performance of the models, especially for larger spectral libraries that include soils that are more heterogeneous. Therefore, the objectives of this study were to: i) apply Vis-based color parameters on the stratification of a regional soil spectral library; ii) evaluate the performance of the predictive models generated from the spectral library stratification; iii) compare the performance of stratified models (SMs) and the model without stratification (WSM), and iv) explain possible changes in prediction accuracy based on the SMs. Thus, a regional soil spectral library with 1 535 samples from the State of Santa Catarina, Brazil was used. Soil reflectance data were obtained by Vis-NIR-SWIR spectroscopy in the laboratory using a spectroradiometer covering the 350–2 500 nm spectral range. Sand, silt, and clay fractions were determined using the pipette method. Twenty-two components of color parameters were derived from the Vis spectrum using the colorimetric models. A cubist regression algorithm was used to assess the accuracy of the applicability of the initial models (SMs and WSM) and of the validation between the clusters. Fractional order derivatives (FODs) at 0.5, 1.5, and 2 intervals were used to explain possible changes in the performance of the SMs. The SMs with higher contents of clay and iron oxides obtained the highest accuracy, and the most important spectral bands were identified, mainly in the 480–550 and 850–900 nm ranges and the 1 400, 1 900, and 2 200 nm bands. Therefore, stratification of soil spectral libraries is a good strategy to improve regional assessments of soil resources, reducing prediction errors in the qualitative determination of soil properties.

Modeling soil landscapes and soil textures using hyperscale terrain attributes

Digital soil mapping using hyperscale modeling of topographic parameters is a promising approach to capture the multiscale relationship between soil properties and the landscape. Hyperscale analysis sampled and calculated topographic parameters at more than one resolution. This study aims to develop prediction models for estimating the soil particle size fraction by using a hyperscale terrain analysis. We collected 407 topsoil samples from a watershed of 236 km2 near the city of Malang, Indonesia. We related the observations to hyperscale terrain parameters in a multiple linear regression framework as the model’s parameters allow direct geomorphological process interpretations. We found that the accuracy of the model increased when the watershed area was divided in two based on its parent material. The local morphometric variable parameters included in the models provided an overview of the scale of topography parameters that influence the hydrology and soil forming factors. Our models indicated that, besides the parent material, the flow direction in the landscape had a large controlling effect on the spatial distribution of soil texture. The proposed model using simple linear relationships had a high accuracy in prediction and can be used to reveal soil formation processes.

Digital mapping of soil texture classes using Random Forest classification algorithm

AbstractSoil texture is the most important soil physical property that determines water holding capacity, nutrient availability and crop growth. Spatial distribution of soil texture at a higher spatial resolution at regional and national level is essential for crop planning and management. In the present study, we mapped the soil textural classes over 16.2 M ha area of Andhra Pradesh state, India, at 250 m spatial resolution up to 2 m depth using the digital soil mapping approach. A total of 2,272 profile observations were used for the prediction of soil textural classes using the Random Forest (RF) classification algorithm. To estimate textural classes at six standard soil depth intervals (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm), we used continuous depth function of texture distribution using average sand, silt and clay content of different textural classes. Depth‐wise spline outputs were then transformed into textural classes as per USDA textural classification. Sixteen environmental variables including Landsat‐8 data, digital elevation model attributes and climatic variables were used for modelling. For model building, 75% of data was used and 25% of data was used for validation. Overall classification accuracy index and kappa index were calculated for validation data sets using 100 RF models. We recorded overall accuracy of 50%–65% and kappa index of 35%–47% for various depths. We found that equal‐area quadratic splines of average sand, silt and clay are useful for soil profile depth harmonization of soil textural classes and random forest classification algorithm is a promising tool for spatial prediction of texture classes at the regional level. The present high‐resolution (250 m) maps of soil texture classes are useful for different hydrological studies and preparation of proper land‐use plans.

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.

Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran

Soil texture is a physical property of soil and knowledge on its spatial distribution is essential for many agricultural and environmental activities especially in alluvial plains. This study aimed to predict the spatial distribution of soil fractions (i.e. percentages of sand, silt, and clay) in Sistan floodplain on a regional scale (area: 1500 km2). Random forest (RF), regression kriging-neural network residual kriging (RKNNRK), neural network residual kriging (NNRK), regression kriging (RK), and cokriging (COK) methods were used to map soil texture components over the region. Soil fractions were measured for 160 soil samples taken from the surface layer (0–30 cm) of various soil series in agriculture land of Sistan floodplain. The additive log-ratio (alr) transformation was applied to transform texture components prior to prediction. Remote sensing data including Landsat 8′ Bands (1–8), Band 4 to Band 8 ratio, Band 4 to Band 3 ratio, NDVI, GSI, Brightness Index, and Clay Index were used as auxiliary variables for interpolation of soil fractions. 80% of actual data was used for prediction and 20% of data was used for validation. The performance of methods used was evaluated using RMSE, ME and MAE criteria. The results showed that the RKNNRK model has the highest accuracy for prediction of sand (RMSE = 15.04%) and clay (RMSE = 8.77%) contents while the most accurate model for predicting silt content is NNRK (RMSE = 12.68%). RK and COK performed worse than NNRK and RKNNRK. A relatively high value of RMSE obtained for sand (17.89%), silt (14.15%), and clay (8.89%) contents by the RF model. This could be due to the low relief of study area, regional scale effects, low density of sampling points and high heterogeneity of soil texture components in floodplains. Our findings revealed that RKNNRK and NNRK models, when combined with remote sensing data, produce more accurate results and therefore can be used for appropriate mapping of soil fractions in floodplains and on a regional scale.

Predictive mapping of surface soil texture in a semiarid region of India through geostatistical modeling

The accurate and complete spatial distribution of soil texture is required for proper land use planning, soil management practices and other interventions linked to environmental protection. In the present study, we spatially predicted sand, silt and clay content in top soil (0–15 cm) in parts of Gadag district in Karnataka, India using satellite-based indices and geostatistical modeling. The study was conducted to evaluate the Landsat 8 Operational Land Imager (OLI) remote sensing data as auxiliary variable along with CartoDEM for spatial prediction of sand, silt and clay content with a total of 57 number of field observations. The bands 2, 3, 4, 5, 6 and 7, Normalized Difference Vegetation Index (NDVI), The Grain Size Index (GSI) and the relationship between band 4 and 3, 4 and 7 and 6 and 7 of Landsat 8 OLI and slope, elevation of CartoDEM were used as covariates for modeling. Among these covariates NDVI and relationship between band 4 and band 7 were not significantly correlated with the sand, silt and clay content. However digital number (DN) of band 2, 3, 4, 5, 6, 7, GSI and relationship between band 4 to band 3 were significantly correlated with the surface soil sand, silt and clay content. The DN of band 2, 3, 4, 5, 6, 7, GSI and relationship between band 4 to band 3 were selected as auxiliary data for the estimation of sand and silt content, whereas the DN of band 5 and 7 explained most of the variability of soil clay. The Regression Kriging (RK) and Multiple Linear Regression (MLR) were employed to predict soil texture and compared for better accuracy of estimation. The greater spatial variability (58.8, 48.2 and 33.2%) of sand, silt and clay content respectively were predicted with lower estimation errors (RMSE = 7.41, 4.68 and 7.64, R2 = 0.95, 0.55 and 0.91 respectively) in RK as compared to MLR method (RMSE = 9.18, 5.65 and 11.13, R2 = 0.90, 0.38 and 0.80 respectively). The average decrease of 31.3, 19.2 and 17.5% in the prediction error was observed in RK over MLR approach in clay, sand and silt prediction respectively. The uncertainty of the prediction was calculated by 95% confidence intervals, which showed 7.51, 43.5 and 49.6% for sand, silt and clay prediction respectively. The current study showed that RK approach can be useful to predict sand, silt and clay content over MLR approach.

Soil Texture and Organic Carbon Fractions Predicted from Near‐Infrared Spectroscopy and Geostatistics

Core Ideas Near‐infrared spectroscopy (NIRS) was appropriate for predicting soil texture and C. Both linear and nonlinear multivariate models could be used for NIRS calibration. Soil texture was predicted with greater precision than organic C fractions. Near‐infrared spectroscopy and kriging were a useful combination for assessing spatial variation. Near‐infrared spectroscopy (NIRS) and geostatistics are relatively unexplored tools that could reduce the time, labor, and costs of soil analysis. Our objective was to efficiently determine lateral and vertical distributions of soil texture and soil organic C (SOC) fractions in an agroforestry system (a 7‐ha field) on a Coastal Plain site in North Carolina. To predict selected properties from a large number of soil samples collected from this field, NIRS was calibrated against laboratory‐determined properties. Support vector machines was a multivariate model that performed better than partial least squares to obtain greater precision with NIRS for all soil properties. To predict soil properties with precision across the field, geostatistical modeling with maximum likelihood and ordinary kriging was used. When we combined the two modeling processes, the root mean square error (RMSE) and the RMSE relative to the dataset mean (%RMSE) were 67 g kg‐1 for sand (9.3% RMSE), 34 g kg‐1 for clay (22.7% RMSE), 1.63 g kg‐1 for total organic C (26.7% RMSE), 0.67 g kg‐1 for particulate organic C (36.1% RMSE), and 24 mg CO2–C kg‐1 3 d‐1 for the flush of CO2 (29% RMSE). We conclude that the combination of NIRS and kriging produced acceptable errors and therefore could be used to predict the spatial distribution of soil texture and SOC fractions in this agroforestry system to allow efficient assessment of management changes with time and better predict small‐scale input requirements.

Mapping Soil Texture Using Geostatistical Interpolation Combined With Electromagnetic Induction Measurements

ABSTRACTSoil texture influences many physical and chemical properties that affect fertility and productivity. Assessing the spatial distribution of soil texture is necessary to implement management practices that avoid soil degradation. The objective of this study was to evaluate the usefulness of s

Mapping the Soil Texture in the Heihe River Basin Based on Fuzzy Logic and Data Fusion

Mapping soil texture in a river basin is critically important for eco-hydrological studies and water resource management at the watershed scale. However, due to the scarcity of in situ observation of soil texture, it is very difficult to map the soil texture in high resolution using traditional methods. Here, we used an integrated method based on fuzzy logic theory and data fusion to map the soil texture in the Heihe River basin in an arid region of Northwest China, by combining in situ soil texture measurement data, environmental factors, a previous soil texture map, and other thematic maps. Considering the different landscape characteristics over the whole Heihe River basin, different mapping schemes have been used to extract the soil texture in the upstream, middle, and downstream areas of the Heihe River basin, respectively. The validation results indicate that the soil texture map achieved an accuracy of 69% for test data from the midstream area of the Heihe River basin, which represents a much higher accuracy than that of another existing soil map in the Heihe River basin. In addition, compared with the time-consuming and expensive traditional soil mapping method, this new method could ensure greater efficiency and a better representation of the explicitly spatial distribution of soil texture and can, therefore, satisfy the requirements of regional modeling.

Assessment of Spatial Variability and Mapping of Soil Texture in Western of Syria using Geographic Information System Techniques (GIS)

The study aimed to study the spatial variability of soil texture for soils around AL-Basel Lake in Safita Region which located in Tartous Governorate west of Syrian Arab Republic. Spatial maps of soil properties are invaluable in agricultural Production for assessing soil quality, planning land use and determining the suitability of cropping patterns. Geostatistics has been extensively used for quantifying the spatial pattern of soil properties and Inverse Distance Weight technique are proving sufficiently robust for estimating values at unsampled locations. The experiment was conducted on the soils of villages around AL-Basel lake The aim of this work is to study the spatial variability of soil texture. For this purpose ,90 Samples were collected. Results of Laboratory analysis of studied indexes were imported into ArcGis9.3 software and presented in form of Digital Maps that show the spatial Distribution of soil texture using Geostatistical Analyst and Inverse Distance Weight method was used in the Spatial Interpolation. Results of texture analysis showed that the soils have a texture between Loam and Silty Loam. Geostatistical analyst was used for unsampled points. Since 70.94 % of study area has Sand percentage (30-40) %, for Silt 57.73 % of study area has Silt percentage (40-50)% ,for Clay 71.97% of study area has Clay percentage (10-20)%,81.76% of study area has loam texture. This study thus provided a methodology that can help improve the accuracy and efficiency of soil texture mapping in areas using.

Performance of Frequently Used Interpolation Methods to Predict Spatial Distribution of Selected Soil Properties in an Agricultural Watershed in Ethiopia

A soil map of a watershed provides a wealth of knowledge and can be vital for implementing site-specific soil managements. Hence, watershed-based soil assessment was conducted to select an optimum spatial interpolation method, while aiming for sustainable soil managements. Intensive soil sampling was undertaken to investigate the performance of ordinary kriging (OK), inverse distance weighting (IDW), and radial basis functions (RBF) for predicting the spatial distribution of soil texture, pH, soil organic carbon (SOC), and available phosphorus (AP). The 72 ha study area was divided into a 100 x 100 m grid and approximately at the center of each grid, topsoil samples (roughly from 10-25cm depth) were collected from over 75 locations across the entire watershed. The exponential and Gaussian models were best fitted in the semivariogram of measured soil. The performance of each interpolation method was assessed quantitatively in terms of Nash-Sutcliffe efficiency (E), coefficient of determination (R2), and index of agreement (d). The interpolated maps generated based on the highest value of E displayed OK was best performed for SOC and sand. RBF was most suitable for mapping of AP and clay, while IDW gave better results when applied to pH. The highest value of R2, E, and d (0.51, 0.51, and 0.83, respectively) resulted from the spatial interpolation of AP. Overall, the cross-validation statistics for each interpolation method showed there was no single method that significantly outperformed the others. Therefore, one of the interpolation methods could be applied for surface map generation in future studies of similar regions.

Ground radiometric investigation of natural radiation levels and their radiological effects in Akpabuyo, Nigeria

Ground investigations of the activity concentrations from primordial radionuclides (238U, 232Th and 40K) were conducted in Akpabuyo, southeastern Nigeria. These investigations were aimed at assessing the magnitude and spatial distribution of activity concentrations from primordial radionuclides. Also, radiological hazard assessment and their associated risk to both human environmental healths and suitability of soils in the area for constructing dwellings places will be made. Instrument used for the investigations, which were conducted both randomly and along 6 profiles with inter-profile distance of 100 m, was a potable GRS-2 model of a Pico Envirotec spectrometer. Activity concentrations in the area, which vary with spatial distribution of soil texture, lithology, land use and topography, range between 2.22 and 116.09 Bq kg−1 (mean of 34.67 Bq kg−1) for 238U, 3.65–87.41 Bq kg−1 (mean of 38.59 Bq kg−1) for 232Th and 6.26–384.99 Bq kg−1 (mean of 114.66 Bq kg−1) for 40K. The mean activity concentrations of 238U and 232Th are marginally higher than world averages of 30 and 39 Bq kg−1 respectively. However, the activity concentration of 40K, which is the most abundant radionuclide (60.02%), is less than the world average of 400 Bq kg−1. Results obtained from skew and kurtosis analyses of the activity concentration data show that the distribution of 238U and 232Th radionuclides in the soils is nearly symmetrical. The radiological hazard indicators computed from the activity concentrations of the radionuclides are all below maximum permissible limits. For instance, values of radium equivalent, which vary from 41.72 to 171.02 Bq kg−1 (average of 98.68 Bq kg−1), are below the permissible limit of 370 Bq kg−1. External and internal hazard indices vary between 0.11 and 0.46 Bq kg−1 (mean of 0.27 Bq kg−1) and 0.14–0.72 Bq kg−1 (mean of 0.36 Bq kg−1) respectively. These results are below the 1 Bq kg−1 benchmark required for materials to be safe for use in constructing residential buildings. Absorbed dose, indoor and outdoor annual effective dose rates vary from 19.37 to 76.72 nGy h−1 (mean of 44.11 nGy h−1), 0.10–0.38 mSv yr−1 (mean of 0.22 mSv yr−1) and 0.02–0.09 (mean of 0.05 mSv yr−1) respectively. The mean absorbed dose rate is almost equal to the world average of 44 nGy hr−1, but the means of the indoor and outdoor annual effective doses are below the 0.50 mSv yr−1 limit set for materials to be safe. Thus, the soils in Akpabuyo are safe for use in constructing dwelling places for the population. However, periodical monitoring of the radionuclides and radioisotopes should be performed.

Prediction of soil texture using descriptive statistics and area-to-point kriging in Region Centre (France)

The French soil-test database (Base de Données d'Analyses de Terre: BDAT) is populated with analytical results of agricultural topsoil samples requested by farmers for fertilization planning. The coordinates of the farms are unknown due to data confidentiality policies, and the best available georeference is at level of municipality. We compared four approaches for mapping soil texture of agricultural land in Region Centre (France) using BDAT data: 1) a reference approach of mapping the mean of the aggregated data by municipality, 2) a boosted regression tree (BRT) model fitted with the municipality-averaged data, 3) area-to-point cokriging (AToP CK), and 4) a regression kriging version of this (AToP RCK, for which the BRT predictions were used to give the trend). Specifically, parameters for these last two approaches were fitted through the summary statistics approach to AToP kriging, which accounts for the full set of municipality summary statistics data (i.e. the mean, variance and number of measurements from each municipality). We could thus determine whether more complex and statistically-challenging approaches improve our knowledge on the spatial distribution of soil texture compared with maps of data aggregated by municipality. Texture data from 105 sites form the French soil monitoring network (Réseau de Mesures de la Qualité des Sols: RMQS) were used for independent validation. In general, the R2 was greater for sand (average R2=0.69) and silt (average R2=0.72) than for clay (average R2=0.40). The three methods for disaggregating the summary statistics data (BRT, AToP CK, and AToP RCK) showed similar prediction accuracies—although BRT predictions showed the greatest bias—and were better than the BDAT reference approach. AToP RCK was able to give similar prediction accuracy to BRT modelling alone, reduced the bias considerably, and gave a reasonable (although slightly conservative) assessment of prediction uncertainty. The results indicate that geostatistical methods for change of support expand the utility of aggregated data from soil-test databases.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Error analysis of spatial interpolation of soil texture under different sampling schemes

为研究土壤质地的合理取样方式, 进而研究其空间变异情况, 为田间施肥及灌溉提供依据, 本试验利用地统计学方法和GIS技术, 在重庆市彭水县重庆烟草试验站, 利用289个表层土样, 研究了16 m间距的栅格取样法(对照, 253个土样, 扣除36个验证样点)、34 m间距的栅格取样法(115个土样)和随机取样法(115个土样)3种取样方式下土壤质地的空间插值精度。3种土壤颗粒指标中粉粒占68.43%, 砂粒含量最少, 占12.68%, 黏粒含量略高于砂粒。砂粒和黏粒具有中等强度的变异性, 粉粒具弱变异性, 且数据符合正态分布。地统计分析显示, 在分析该区域土壤质地时, 采用栅格取样方法应适当增大取样间距, 而采用随机取样方法可适当缩小取样间距。交叉检验显示, 土壤质地成分在3种取样方式下的插值精度均以对照最大, 栅格取样次之, 随机取样最小。综合考虑插值误差、样品采集和分析成本及时效性等因素, 本研究建议在该区域进行土壤质地空间变异规律分析为生产服务时应采用随机取样。