Soil Mapping Units Research Articles

Identifying farmland of local importance in Berkeley and Jefferson Counties

The determination of farmland of local importance is a critical component in protecting local agricultural land. However, the initial responsibility for identifying this land falls upon local units of government which are given limited guidance, direction, and support. As such, this can be a highly subjective process. The objective of this study was to apply a previously-described approach to systematically identify potential farmland of local importance in Berkeley and Jefferson Counties of West Virginia, thus facilitating a more informed and less subjective determination. To this end, this study examined definitions of other important soils, developed local criteria based on our specific regional agricultural community, and used a geographic information system (GIS) to identify potential farmland of local importance. Three map units in each county were identified for further consideration. Following a closer examination of their extents, descriptions, and patterns of use, the appropriate local unit of government was provided with the rationale for considering these soils. With this information, the local unit of government was then able to evaluate the data objectively and make a more informed, less subjective decision about potential soil map units of local importance in Berkeley and Jefferson Counties.

Comparing soil carbon estimates in glaciated soils at a farm scale using geospatial analysis of field and SSURGO data

Soil carbon is a key soil property related to ecosystem services and it is often used in soil carbon content estimates at various scales. Uncertainties in soil carbon estimates often arise from variability in field, laboratory, and/or geospatial data at a farm scale. The objectives of this study were to quantify and compare levels of soil organic carbon (SOC), soil inorganic carbon (SIC), and total soil carbon (TC) for a 147-hectare field site in upstate New York based on three alternative analysis procedures: a) using carbon concentrations reported by the Soil Survey Geographic (SSURGO) spatial databases for each soil map unit (SMU) present at the field site and applying that value across each SMU; b) averaging the carbon contents of soil cores collected within a specific SMU boundary and applying the averaged value across each SMU; and c) interpolating carbon contents across the field site based on the individual soil cores. Maps of SOC, SIC, and TC contents based on the interpolated core samples were different from maps created by applying averaged core results or SSURGO values across the SMUs. Differences in the magnitudes and spatial distributions of carbon can be attributed to several factors. For example, SSURGO soil carbon values are frequently measured for a selected pedon(s) from a “type location” and not from the actual study location. These “type locations” can be located far from study sites and even in different states. Also, SSURGO soil carbon values may overestimate the actual contents when compared to systematic field measurements because the SSURGO values at lower depths are often extrapolated from upper soil horizons. Such extrapolations affect inorganic carbon to a much greater extent than organic carbon, therefore better agreement is observed in the present study between SOC estimates from the SSURGO database and field measurements. Because regional and/or global carbon estimates are rarely made with detailed field data due to the high costs of field and laboratory measurements, additional field sampling is needed to constrain and improve these estimates and also to assess the potential variability present.

Approximating the variance of estimated means for systematic random sampling, illustrated with data of the French Soil Monitoring Network

In France like in many other countries, the soil is monitored at the locations of a regular, square grid thus forming a systematic sample (SY). This sampling design leads to good spatial coverage, enhancing the precision of design-based estimates of spatial means and totals. Design-based estimation of the mean or total from SY samples is straightforward. However, an unbiased estimator of the sampling variance of the estimated mean or total does not exist. This paper compares five variance approximations, being the simple random (SI), stratified simple random (STSI), Geary's spatial autocorrelation C index (Geary), Moran's I index (Moran), and the model-based (MB) approximation in a simulation study and a real-world case study. In a simulation study the model distribution of the conditional bias (conditioned on a simulated reality) of the variance approximations is estimated for various variograms and two sample sizes. In the case study the data of the first campaign of the French Soil Monitoring Network are used to estimate the spatial means of six soil variables (C, Tl, Cd, Ni, K, Mn) for aggregated soil map units of France, and to approximate their sampling variances. The bias in the approximated variances is explored with MODIS-NDVI data. With variograms with no or a small relative nugget variance approximation STSI and MB are the best choices. In situations with large relative nugget STSI is to be preferred over MB as MB then may somewhat underestimate the variance. Moran and SI should be avoided as approximation methods, as they seriously underestimate (Moran) and overestimate (SI) the variance in many cases. The approximated standard error of total soil organic carbon stock in France as obtained with MB was 0.0335 Pg, which was small compared to the estimated stock of 3.580 Pg.

Land resource assessment for agricultural development in Seoni district (Madhya Pradesh), India

The objective of land resource assessment for rainfed agridevelopment in tribal dominated Seoni ditrict, Madhya Pradesh was to assess the suitability of thrity soil mapping units for sorghum-cotton based systems in relation to fertility constraints and for enhancing crop productivity. The arability and suitability analysis showed that fifty six per cent of arable land is suitable for eleven land use systems. The twenty two per cent of arable basaltic lands in northen plateaus were evaluated as suitable for for citrus, sorghum and soybean cropping systems with limitations of low available nitrogen, phosphorus and zinc whereas in southern precipitous zone , fifteen per cent of granitic lands were evaluated as suitable for sorghum and cotton with limitations of stoniness, low water holding capacity, low status of available nitrogen, phosphorus, potassium and zinc and eighteen per cent of area in sagar and hirvi river valleys for rice, citrus, maize, sunflower and vegetables. Out of 44.6 per cent of nonarable land, thirty seven per cent of land was evaluated as suitable for forestry / grazing and 6.8 per cent for wild life.The study advocates agroecological zonation for maximum utilization of land resources for potential cropping systems in improving produvtivity and fertility management.

Soil suitability evaluation for rain-fed maize production at Gabari District Zaria Kaduna State, Nigeria

The need to generate adequate information for optimal and sustainable exploitation of the soils of Gabari District Zaria is imperative. The FAO land suitability evaluation was used to evaluate the soils for rain-fed maize cultivation. The total land area surveyed was 70 ha. Three soil units was mapped and designated as GBI 12.3 ha, GBII 38.8 ha and GBIII 18.9 ha. Generally, the surface texture was loam. The soil reactions were slightly acidic to neutral, organic carbon, available phosphorus and total nitrogen was rated low. The CEC (NH4OAc) were medium to high and Base saturation was rated medium while EC and ESP were low. Generally, there were no significant differences in the physical and chemical properties within the three mapping units. Soil mapping units GBI and GBIII were marginally suitable (S3) while GBII was currently not suitable (N1) for maize production due to limitation imposed by soil chemical properties and slope. With proper soil fertility management, the potentials of these soils can be increased to moderately suitable (S2) for rain-fed maize and other arable crops if recommended fertilization and uses of organic manure to improve the soils physical and chemical constraints as well as construction of contour ploughing in unit GBII to reduce the rate of erosion.Keywords: Soil, Suitability Evaluation, Agricultural potential, Gabari District

Digital soil mapping at local scale using a multi-depth Vis–NIR spectral library and terrain attributes

Conventional soil mapping is costly and time consuming. Therefore, the development of quick, cheap, but accurate methods is required. Several studies highlight the importance of developing regional soil spectral libraries for digital soil mapping, but few studies report on the use of these libraries to aid digital mapping of soil types. This study aims to produce a digital soil map using as training set Visible and Near Infra-Red (Vis–NIR) spectra from local soil samples, a regional spectral library and terrain attributes. The soils were sampled in 162 locations on a 270-ha farm in the municipality of Piracicaba, São Paulo, Brazil. Spectra from topsoil and subsoil were measured in laboratory (400–2500nm) and arranged as multi-depth spectra. Information was summarized by principal component analysis. Regression tree models were calibrated to predict principal components (PC) scores based on terrain attributes. After calibration, the models were applied to the entire study site, resulting in PC score maps. Fuzzy c-means and PC maps were used to define the soil mapping units (MU). Based on fuzzy centroids, representative samples (RS) were defined to the MU. Munsell soil color and soil order were predicted from soil spectra and used to characterize the MU. The regression tree model had a good fit for PC1, with an r2 of 0.92, and a satisfactory r2 for PC3, PC4, and PC5, respectively 0.58, 0.66 and 0.53. The fuzzy clustering defined seven MU. The R2 for Munsell color predictions were 0.94 (hue), 0.96 (value) and 0.73 (chroma). Soil order had good agreement in validation, with kappa coefficient of 0.41. The methodology indicates the potential of Vis–NIR spectra to improve soil mapping campaigns and consequently provides a product similar to a conventional soil map.

Predicting USCS soil classification from soil property variables using Random Forest

Soil classification systems are widely used for quickly and easily summarizing soil properties and provide a shorthand method of communication between scientists, engineers, and end-users. Two of the most widely used soil classification systems are the United States Department of Agriculture (USDA) textural soil classification system and the Unified Soil Classification System (USCS). Unfortunately, not all soil map units are classified according to the USDA or USCS systems, and previous attempts to provide a crosswalk table have been inconsistent. Random Forest machine learning model was used to create a USCS prediction model using USDA soil property variables. Important variables for predicting USCS code from available soil properties were USDA soil textures, percent organic material, and available water storage. Prediction error rates less than 2% were achieved compared to error rates of approximately 40% using crosswalk methods.

Identifying Field Attributes that Predict Soybean Yield Using Random Forest Analysis

Until recently, soybean [Glycine max (L.) Merr.] fields were often seeded at a single rate. Advances in GPS and variable rate technology (VRT) are allowing growers to use variable rate planting prescriptions to optimize yields and input costs. This study was conducted to find the key predictors for characterizing soybean seed yield from commonly collected precision agriculture data layers. Research was conducted in 11 unique fields both in 2013 and 2014 in Wisconsin and all 22 site‐years were following corn [Zea mays (L.)]. Seeding rate, soil sampling, yield, and soil survey data were gathered from each site for analysis. A statistical procedure used in the determination of key prediction parameters, random forest analysis, was used and identified soil map unit as the most important variable when predicting soybean seed yield for the pooled data sets in both 2013 and 2014. The next most important factors were, in order of importance, soil P, soil organic matter, soil available water supply in the upper 100 cm, soil K, and elevation in 2013 and soil P, elevation, soil K, soil organic matter, and soil available water supply in the upper 150 cm during 2014. Individual field random forest analyses determined elevation was the most important predictor of soybean yield, on average, for 2013 and 2014 followed by organic matter, K, P, and pH in 2013 and pH, K, organic matter, and P in 2014.Core Ideas Commonly accessed soybean yield predictors can vary depending on scale. Soil pH, organic matter, P, and K are good predictors of soybean yield in Wisconsin. Random forest and decision tree analyses are useful and accurate analysis tools.

Assessing Spatial Variation of Corn Response to Irrigation Using a Bayesian Semiparametric Model

<abstract> <b><sc>Abstract.</sc></b> Spatial irrigation of agricultural crops using site-specific variable-rate irrigation (VRI) systems is beginning to have widespread acceptance. However, optimizing the management of these VRI systems to conserve natural resources and increase profitability requires an understanding of the spatial crop responses. In this research, we utilize a recently developed spatially explicit analysis model to reanalyze spatial corn yield data. The specific objectives of this research were to (1) calculate a suite of estimates (estimated yield, rainfed yield, maximum yield, and irrigation at maximum yield) and provide credible intervals (measures of uncertainty) around these estimates for comparing with the previous analysis, and (2) examine whether the conclusions from this rigorous re-analysis were different from the prior analysis and if the results would force any modifications to the conclusions obtained with the prior analyses. The spatially explicit analysis was achieved using a mixed model formulation of bivariate penalized smoothing splines and was implemented in a Bayesian framework. This model simultaneously accounted for spatial correlation as well as relationships within the treatments and had the ability to contribute information to nearby neighbors. The model-based yield estimates were in excellent agreement with the observed spatial corn yields and were able to estimate the high and low yields more accurately than the previous analysis. Credible intervals were calculated around the estimates, and the majority encompassed the observed yields. After calculating estimates of yield, we then calculated estimates of other response variables, such as rainfed yield, maximum yield, and irrigation at maximum yield. These estimated response variables were then compared with previous results from a classical statistical analysis. Our conclusions supported the original analysis in identifying significant spatial differences in crop responses across and within soil map units. These spatial differences were great enough to be considered in irrigation system design and management. The major improvement in the 2014 re-analysis is that the model explicitly considered spatial dependence in calculating the estimated yields and other variables and thus should provide improved estimates of the impact of spatial differences for use in irrigation system design and management.

Geospatial Soil and Plant Tissue Analysis of Sericea Lespedeza (Lespedeza cuneata) Invasion around Lake Issaqueena, South Carolina

ABSTRACTThis study was conducted to determine the chemical composition of sericea lespedeza (Lespedeza cuneata) as well as soil and landscape characteristics that correlate with its invasion. Geographic Information Systems (GIS) and Light Detection and Ranging (LiDAR) were used to determine the pattern of invasion and to quantify landscape metrics. Sericea lespedeza was distributed on both sides of the lake in Pacolet and Madison soil map units (Fine, kaolinitic, thermic Typic Kanhapludults) on average slopes of 17.5%. It was common along roads and trails with a preferred mean canopy cover of <50%. Plant tissue analysis revealed statistically higher concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca) and sulfur (S) and selected micronutrients in leaves compared to stems and roots. Micronutrients copper (Cu), manganese (Mn) and iron (Fe) were significantly higher in stems and roots. Soil analysis results showed no statistical difference between control and invaded plots for measured soil chemistry parameters.

Soil Classification and Potentiality Assessment for Some Rainfed Areas at West of Matrouh, Northwestern Coast of Egypt

Desert development is the key role of enhancing agricultural production in Egypt. The northwestern coastis one of the most attractive desert regions for sustainable agrarian development due to its abundant qualifications which have not sufficiently used yet. This investigation was carried out in El-Qasr - Um El-Rakham area as a model for rainfed pattern at the northwestern coast, to assess both of types and capability of the soils representing dominant land forms in the area. Studied area extends over about 159603 feddan including 12 drainage basins.The extent of landforms was spatially identified using geological and topographic maps in integration with DEM. The area represented by 6 main land forms; (1) Coastal plain (17.8%) which include: Alluvial fans (9.1%), Lagoonal depression and salt marsh (3.5%), Oolitic longitudinal sand dunes (2.2%) and Oolitic sand beach (2.8%) – (2) Alluvial terraces and Interfluves wadis (9.7%) – (3) Wadi course (2.7%) – (4) Escarpment (4.3%) – (5) Piedmont plain (13.6%) – (6) Plateau (52.1%) which include: Lower plateau (18.6%) and Higher Libyan plateau (33.5%).Soils of the area were surveyed through 261 soil profiles which spatially distributed upon ETM+ LANDSAT 7 image classification to represent achieved landforms. Twenty four representative soil profiles wereselected in this study. A digital vector database was created using GIS, consequently, soil and evaluation maps were generated. Results indicated that ten soil mapping units were differentiated according to differences in profile depth, texture and topography properties in the area under study. Further, data emphasized that 13.6% and 19.6% of the total area, respectively have deep and moderately deep profile depth. Coarse to moderately coarse texture classes dominated whole soils. Almost flat topography to gently undulated conquered the area over 69.3%.Based on field check and laboratory analysis, soils were classified into 10 family classes, while at sub greatgroup level seven classes were differentiated. These are Typic Torrifluvents (24259 F – 15.2%), TypicTorripsamments (28728 F – 18.0%), Lithic Torripsamments (36070 F – 22.6%), Lithic Torriorthents (56978 F – 35.7%),oolitic Torripsamments (7980 F - 5.0%), Typic Aquisalids (5586 F - 3.5%), and Typic Haplosalids (260 F -0.15%).Soils of oolitic sand beach were classified as Oolitic Torripsamments, while soils of the lagoon depressions andsalt marsh were classified as Typic Aquisalids. Soils of alluvial fans were classified as Typic Torrifluvents. As well, soils of the alluvial terraces and interfluves wadis, piedmont plain and wadi courses are generally belong to the subgroups Typic and lithic Torripsamments and Torriorthents, except small areas in the alluvial terraces and interfluves wadis and the lower portions of wadi courses were classified as Typic Torrifluvents and Typic Haplosalids. Generally, soils of plateau are classified as lithic Torriorthents.The study concluded that, soil potentiality classified as Good over 13.6% of the area, including soils of alluvial fans, partially alluvial terraces and down streams of wadi courses landforms, where the potentiality index varied between 60.9 and 68.1%. Meanwhile, the potentiality was classified as Fair on 19.6% of the area, representing some soils of alluvial fans, alluvial terraces, wadi middle streams and piedmont plain landforms, where the potentiality index ranged from 42.5 to 57.0%. Poor potentiality unit extend over 22.6% of the area occupying some soils of alluvial terraces, wadi upper streams and piedmont plain landforms, as the potentiality index ranged from 30.5 to 36.8%. Potentiality indices ranged between1.2-11.5% indicating Very poor and Non agricultural units over the rest of the area, which may be relevant to other land utilization types. Low soil potentiality associated with limitations related to; limited soil profile depth, poor drainage, coarse texture, low clay content, high salinity, high lime content and low fertility status.

Amik Ovası Yaygın Toprak Serilerinin Mikrobiyal Aktivitelerinin Haritalanması

In this study, it was aimed to determine and mapping of soil microbial activities in widespread soil series of Amik Plain. For this purpose, soil samples were taken from 11 soil series (Asagioba, Karasu, Topbogazi, Acarkoy, Comba, Reyhanli, Sazyurdu, Asi, Kecebek, Arpahan, Akkerpic) in the autumn period. Each will represent a series of five sampling points and 0-30 cm depth of soil samples taken from the three paralleled without waiting is subjected to analysis. Coordinates of sampling points were recorded with the help of handheld GPS. Soil respiration (CO2), dehydrogenase enzyme activity (DHA) and microbial biomass carbon (MBC) analyzes were done for the purpose of determined of microbial activity. Thematic maps were produced as a result of the analysis of the values placed basis of soil mapping units of related series. According to research results, the microbial point of the highest CO2, DHA and MBC values were determined as 29.0 mg CO2 100g.ds/24 h. in Acarkoy Series, 1119 I¼g TPF 10 g.ds/24h. in Reyhanli Series and 256 mg C/kg.ds. in Asi Series' soils respectively. Belong to the same parameters of the lowest values were determined as 11.4 mg CO2 100 g.ds/24sa. in Asagioba Series, 392 I¼g TPF 10 g.ds/24h. in Acarkoy Series and 121 mg C/kg.ds. in Reyhanli Series' soils respectively.

Creative Inquiry in Soil Science: Soil Inventory of Private Lands

Creative Inquiry (CI) projects provide unique learning opportunities in soil science education. Clemson University’s CI program combines engaged learning and undergraduate research. We have developed a highly effective hands-on CI project involving soil inventory of private lands using the Web Soil Survey to identify soil map units, collected soil samples in the field, analyzed them in the Clemson University Agricultural Service Laboratory, and interpreted the soil testing results for management recommendations. Clemson University students from various fields (forestry, wildlife biology, environmental science, agricultural education, etc.) have conducted soil inventories of private lands in the Forestry and Natural Resources (FNR) class FNR 4700: GIS and SIS Applications course taught from 2007 to 2015 (60 students total). Students were asked to fill out a survey (2013, 2014, and 2015) consisting of questions related to their learning experience and the usefulness of their CI project, as well as to their overall satisfaction with the CI program. Responses to the survey questions indicated that the CI project improved the student’s confidence in successfully performing various research tasks. Students strongly agreed that teaching undergraduates how to conduct research should be an important goal of the university. Overall, student responses to the questionnaire about their CI project experience were positive, indicating that incorporating project-based learning can enhance soil science education. Impact Statement Creative inquiry (CI) projects provide unique learning opportunities in soil science. The Clemson University CI program combines engaged learning and undergraduate research. This article describes an example of creative inquiry–based project in undergraduate soil science education. A post-project survey of the students indicated that students were very confident to successfully perform various research tasks, and they strongly agreed that teaching undergraduates how to conduct research should be an important goal of the university.

An approach to land capability evaluation for agriculture using remote sensing and GIS in Barberspan, North West Province of South Africa

To guarantee food security, job creation and graduate small-scale farmers to commercial farmers, unproductive farms in the North West Province that have resulted from the land reform programme need to be restructured and evaluated for agricultural productivity. This study aimed to assess the land capability of redistributed land using remote sensing and GIS in spatial planning for arable agriculture in a communal land use setting. The primary data were obtained from soil samples collected from 0–100 cm depths. The secondary information was acquired from the remotely sensed data (SPOT 5). Soil properties such as soil texture, soil depth, pH and drainage condition were stored as attributes in a geographical soil database and linked with the soil map units. The physical characteristics and environmental parameters of Barberspan farm were integrated using spatial analyses function in ArcGIS to produce a land capability map. This land capability study was intended for post-settlement support to land reform beneficiaries and other small-scale farmers who are generally left to look out for themselves after transfer of ownership.

Instance selection in digital soil mapping: a study case in Rio Grande do Sul, Brazil

&lt;p&gt;A critical issue in digital soil mapping (DSM) is the selection of data sampling method for model training. One emerging approach applies instance selection to reduce the size of the dataset by drawing only relevant samples in order to obtain a representative subset that is still large enough to preserve relevant information, but small enough to be easily handled by learning algorithms. Although there are suggestions to distribute data sampling as a function of the soil map unit (MU) boundaries location, there are still contradictions among research recommendations for locating samples either closer or more distant from soil MU boundaries. A study was conducted to evaluate instance selection methods based on spatially-explicit data collection using location in relation to soil MU boundaries as the main criterion. Decision tree analysis was performed for modeling digital soil class mapping using two different sampling schemes: a) selecting sampling points located outside buffers near soil MU boundaries, and b) selecting sampling points located within buffers near soil MU boundaries. Data was prepared for generating classification trees to include only data points located within or outside buffers with widths of 60, 120, 240, 360, 480, and 600m near MU boundaries. Instance selection methods using both spatial selection of methods was effective for reduced size of the dataset used for calibrating classification tree models, but failed to provide advantages to digital soil mapping because of potential reduction in the accuracy of classification tree models.&lt;/p&gt;

Validating digital soil maps using soil taxonomic distance: A case study of Ireland

Recently, there has been a marked increase across the world in the demand for digital soil information, in which digital soil mapping research plays a key role. Methods to validate these digital soil maps are needed. Soil maps, and in particular soil taxonomic maps contain embedded information that represents an understanding of the functioning of the soil within its landscape and the contributing soil forming factors. These cannot be easily validated by a straight point-to-polygon comparison. Furthermore, the uncertainty associated with a misclassification is not binary, but rather a more complex measure that accounts for the degree of divergence between the point observation and map unit that takes into account these underlying relationships between soils, landscape and function. Here we present a map validation approach based on the soil taxonomic divergence and compare this to the outcome from validation based on a straight binary presence/absence evaluation of the map units. We do so for the newly generated soils map of Ireland at a scale of 1:250,000. We find that the overall accuracy calculated through the presence absence method was 69% accurate, whereas the minimum taxonomic distance concept, has an overall accuracy of 90.1%. In particular, soil map units with large spatial coverage tended to be assessed as being very uncertain using the presence/absence method, the confidence around these map units was significantly improved using the minimum taxonomic distance approach. Where large differences were observed between field observations and mapped soil units, we found the taxonomic distance measure a more informative diagnostic as why these differences were observed.

Land resources assessment of El-Galaba basin, South Egypt for the potentiality of agriculture expansion using remote sensing and GIS techniques

The socio-economic development in Egypt is based on land resources. Recently, the Egyptian government is interested in developing low desert zone areas which are located between the recent Nile flood plain and the limestone plateau, from the east and west sides, and represent an important source of aggregate materials. Therefore, this study was carried out to investigate the potentiality of El-Galaba basin soils which are located in the western part of the Aswan Governorate and are characterized by Wadi El-Kubbaniya for the horizontal agricultural expansion and their optimum agricultural use. The investigated area was remotely sensed to identify the landscape and its land resources. Terrain units were identified using draped Landsat 8 satellite image over Digital Terrain Model (DTM) to express the landscape and the associated soil mapping units. Fifteen mapping units were identified and grouped. Land capability evaluation was performed using Cervatana capability model. The results of capability modeling revealed about 3.33% of land with good use capability, 76.06% land with moderate use capability, and 0.08% marginal or non-productive land. The main capability limitations were soil and erosion risks. The Almagra model was used to produce the optimum cropping pattern and limitations of soil units. Matching the crop requirements with soil characteristics, optimum cropping pattern was obtained for wheat, corn, melon, potatoes, sunflower, sugar beet, Alfalfa, peach, citrus, and olive. The results of the study revealed the potentiality of El-Galaba basin for agricultural uses.

Detecting and quantifying field-related spatial variation of soil organic carbon using mixed-effect models and airborne imagery

Evidences exist that the spatial variability of soil organic carbon (SOC) in cropland is partially controlled by environmental or human factors acting on a field basis (e.g., agricultural management, landuse history, landscape structure). However, few studies have quantified the relative importance of the fields-related variability at the regional scale. Recent airborne hyperspectral imagery methods provide SOC estimates at high resolution and over large surfaces. They may be used to quantify and explain the spatial variation of SOC. In this study we used a SOC hyperspectral image over Luxembourg to separate SOC variation in three components: the effect of the texture class (as defined by a texture map), the effect of fields (as defined by a cadastral map) and spatially dependent residuals. The relative variance of these components and the spatial structure of the residuals were rigorously assessed by restricted maximum likelihood (REML). Results indicated that 65.7±0.3% of the variance of SOC in the study area was explained by texture classes. The intensity of the field effect was largely dependent on the location. In some sub-areas of homogeneous texture class, no significant effect could be observed while in others, field explained up to 68.8±12.0% of the variance. In contrast with other methods like ANOVA, the method developed here measures the variation related to spatial units (soil map units or fields) while taking explicitly into account spatial dependencies. As soon as the boundaries of these spatial units and a spatially extensive (or at least very dense) knowledge of a soil property over a region of interest are available, it allows rigorously determining if the soil property depends on these spatial units. In the present application, findings pointed out the importance of considering fields-related variability in SOC modeling and mapping studies.

A Faster and Economical Approach to Floodplain Mapping Using Soil Information

AbstractFlood inundation maps play a key role in assessment and mitigation of potential flood hazards. However, owing to high costs associated with the conventional flood mapping methods, many communities in the United States lack flood inundation maps. The objective of this study is to develop and examine an economical alternative approach to floodplain mapping using widely available soil survey geographic (SSURGO) database. In this study, floodplain maps are developed for the entire state of Indiana, and some counties in Minnesota, Wisconsin, and Washington states by identifying flood‐prone soil map units based on their attributes. For validation, the flood extents obtained from SSURGO database are compared with the extents from other floodplain maps such as the Federal Emergency Management Agency issued flood insurance rate maps (FIRMs), flood extents observed during past floods, and flood maps derived using digital elevation models. In general, SSURGO‐based floodplain maps (SFMs) are largely in agreement with other flood inundation maps. Specifically, the floodplain extents from SFMs cover 78‐95% area compared to FIRMs and observed flood extents. Thus, albeit with a slight loss in accuracy, the SSURGO approach offers an economical and fast alternative for floodplain mapping. In particular, it has potentially high utility in areas where no detailed flood studies have been conducted.

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0–30 and the 0–100cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80km2 and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100cm soil depth were 3.28 and 7.32kg C m−2, respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock.