Showing papers on "Digital soil mapping published in 2011"

Factors of Soil Formation: A System of Quantitative Pedology

Abstract: Factors of soil formation : a system of quantitative pedology / Hans Jenny ; foreword by Ronald Amundson. p. cm. Originally published: New York : McGraw-Hill, 1941. With new foreword. Includes bibliographical references and index. not include the modern Dover Press Forward, which is not public domain material.

Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem

Abstract: Spatial prediction of soil organic matter is a global challenge and of particular importance for regions with intensive land use and where availability of soil data is limited. This study evaluated a Digital Soil Mapping (DSM) approach to model the spatial distribution of stocks of soil organic carbon (SOC), total carbon (Ctot), total nitrogen (Ntot) and total sulphur (Stot) for a data-sparse, semi-arid catchment in Inner Mongolia, Northern China. Random Forest (RF) was used as a new modeling tool for soil properties and Classification and Regression Trees (CART) as an additional method for the analysis of variable importance. At 120 locations soil profiles to 1 m depth were analyzed for soil texture, SOC, Ctot, Ntot, Stot, bulk density (BD) and pH. On the basis of a digital elevation model, the catchment was divided into pixels of 90 m × 90 m and for each cell, predictor variables were determined: land use unit, Reference Soil Group (RSG), geological unit and 12 topography-related variables. Prediction maps showed that the highest amounts of SOC, Ctot, Ntot and Stot stocks are stored under marshland, steppes and mountain meadows. River-like structures of very high elemental stocks in valleys within the steppes are partly responsible for the high amounts of SOC for grasslands (81–84% of total catchment stocks). Analysis of variable importance showed that land use, RSG and geology are the most important variables influencing SOC storage. Prediction accuracy of the RF modeling and the generated maps was acceptable and explained variances of 42 to 62% and 66 to 75%, respectively. A decline of up to 70% in elemental stocks was calculated after conversion of steppe to arable land confirming the risk of rapid soil degradation if steppes are cultivated. Thus their suitability for agricultural use is limited.

Sampling for validation of digital soil maps

Abstract: The increase in digital soil mapping around the world means that appropriate and efficient sampling strategies are needed for validation. Data used for calibrating a digital soil mapping model typically are non-random samples. In such a case we recommend collection of additional independent data and validation of the soil map by a design-based sampling strategy involving probability sampling and design-based estimation of quality measures. An important advantage over validation by data-splitting or cross-validation is that model-free estimates of the quality measures and their standard errors can be obtained, and thus no assumptions on the spatial auto-correlation of prediction errors need to be made. The quality of quantitative soil maps can be quantified by the spatial cumulative distribution function (SCDF) of the prediction errors, whereas for categorical soil maps the overall purity and the map unit purities (user's accuracies) and soil class representation (producer's accuracies) are suitable quality measures. The suitability of five basic types of random sampling design for soil map validation was evaluated: simple, stratified simple, systematic, cluster and two-stage random sampling. Stratified simple random sampling is generally a good choice: it is simple to implement, estimation of the quality measures and their precision is straightforward, it gives relatively precise estimates, and no assumptions are needed in quantifying the standard error of the estimated quality measures. Validation by probability sampling is illustrated with two case studies. A categorical soil map on point support depicting soil classes in the province of Drenthe of the Netherlands (268 000 ha) was validated by stratified simple random sampling. Sub-areas with different expected purities were used as strata. The estimated overall purity was 58% with a standard error of 4%. This was 9% smaller than the theoretical purity computed with the model. Map unit purities and class representations were estimated by the ratio estimator. A quantitative soil map, depicting the average soil organic carbon (SOC) contents of pixels in an area of 81 600 ha in Senegal, was validated by random transect sampling. SOC predictions were seriously biased, and the random error was considerable. Both case studies underpin the importance of independent validation of soil maps by probability sampling, to avoid unfounded trust in visually attractive maps produced by advanced pedometric techniques

Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues

Abstract: 1201 Soil Sci. Soc. Am. J. 75:1201-1213 Posted online 23 June 2011 doi:10.2136/sssaj2011.0025 Received 20 Jan. 2011. *Corresponding author (sabgru@ufl .edu). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. Digital Soil Mapping and Modeling at Continental Scales: Finding Solutions for Global Issues SSSA 75th Anniversary Paper

Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra

Abstract: [1] Clay minerals are the most reactive inorganic components of soils. They help to determine soil properties and largely govern their behaviors and functions. Clay minerals also play important roles in biogeochemical cycling and interact with the environment to affect geomorphic processes such as weathering, erosion and deposition. This paper provides new spatially explicit clay mineralogy information for Australia that will help to improve our understanding of soils and their role in the functioning of landscapes and ecosystems. I measured the abundances of kaolinite, illite and smectite in Australian soils using near infrared (NIR) spectroscopy. Using a model-tree algorithm, I built rule-based models for each mineral at two depths (0–20 cm, 60–80 cm) as a function of predictors that represent the soil-forming factors (climate, parent material, relief, vegetation and time), their processes and the scales at which they vary. The results show that climate, parent material and soil type exert the largest influence on the abundance and spatial distribution of the clay minerals; relief and vegetation have more local effects. I digitally mapped each mineral on a 3 arc-second grid. The maps show the relative abundances and distributions of kaolinite, illite and smectite in Australian soils. Kaolinite occurs in a range of climates but dominates in deeply weathered soils, in soils of higher landscapes and in regions with more rain. Illite is present in varied landscapes and may be representative of colder, more arid climates, but may also be present in warmer and wetter soil environments. Smectite is often an authigenic mineral, formed from the weathering of basalt, but it also occurs on sediments and calcareous substrates. It occurs predominantly in drier climates and in landscapes with low relief. These new clay mineral maps fill a significant gap in the availability of soil mineralogical information. They provide data to for example, assist with research into soil fertility and food production, carbon sequestration, land degradation, dust and climate modeling and paleoclimatic change.

Soil pH mapping with an on-the-go sensor.

Abstract: Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager™, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager™ under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager™ were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r(2)) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany.

Updating Conventional Soil Maps through Digital Soil Mapping

Abstract: Conventional soil maps, as the major data source for information on the spatial variation of soil, are limited in terms of both the level of spatial detail and the accuracy of soil attributes. These soil maps, however, contain valuable knowledge on soil-environment relationships. Such knowledge can be extracted for updating conventional soil maps through the use of available high-quality data on environmental variables and data analysis techniques. We developed a method to update conventional soil maps using digital soil mapping techniques without additional field work, which can be used in situations where the study area contains no or few soil profile descriptions at points. The basis of the method is that soil polygons on a conventional soil map correspond to landscape units, which can be considered as combinations of environmental factors. Such environmental combinations were approximated through fuzzy clustering on the environmental factors. We extracted the knowledge on soil-environment relationships by relating the environmental combinations to the mapped soil types. The extracted knowledge was then used for soil mapping using the Soil Land Inference Model (SoLIM) framework. This method was demonstrated through a case study for updating a conventional 1:20,000 soil map of Wakefield, NB, Canada. The case study showed that the updated digital soil map contained much greater spatial detail than the conventional soil map. Field validation indicated that the accuracy of the updated soil map was much higher than the conventional soil map at the level of soil associations with drainage classes, indicating that the proposed method is an effective approach to updating conventional soil maps.

Using apparent soil electrical conductivity (ECa) to characterize vineyard soils of high clay content

Abstract: The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (ECa) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil ECa, three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil ECa and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best ECa linear regressions. Using ECa measurements, soil properties like extractable Na+ and Mg2+, clay and sand content were well estimated, while best estimates were obtained for extractable Na+ (r 2 = 0.770) and clay content (r 2 = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r 2 = 0.449). The methods described above provided maps of soil properties estimated by ECa in a GIS framework, and could save time and resources during vineyard establishment and management.

An error budget for different sources of error in digital soil mapping

Abstract: Summary Digital soil mapping is gathering momentum across the globe, driven by a need for soil information and made possible by the increasingly widespread availability of spatial data that can be used to represent Jenny’s (1941) soil forming factors. Much of the focus is on the predicted values of soil properties but it is equally important to quantify associated prediction errors. Previous studies have considered individual sources of error in a digital soil map but none have considered the combined effect of all sources. In this study, we develop an error budget procedure to quantify the relative contributions that positional, analytical, covariate and model error make to the prediction error of a digital soil map of clay content. We consider four scenarios corresponding to typical levels of data quality: (i) good, (ii) legacy, (iii) spectroscopic and (iv) poor quality data. The error budget procedure uses both geostatistical and Monte-Carlo simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models are used to construct the digital soil map. In this implementation we consider the error associated with the measurement of the clay content; the location of the survey sites; the interpolation of the covariate (ECa); and the estimation of the fixed effects, random effects and interpolation from the linear mixed model. For all data quality scenarios, the error from the model dominated the prediction error (mean square error of 67.24–72.41% Clay 2 ). Where the analytical error was small, the error attributed to the covariate was greater than the analytical error. This relationship was reversed in the data quality scenarios where the analytical error was large. Under all data quality scenarios, the effect of positional error was negligible.

Mapping Field-Scale Soil Moisture With L-Band Radiometer and Ground-Penetrating Radar Over Bare Soil

Abstract: Accurate estimates of surface soil moisture are essential in many research fields, including agriculture, hydrology, and meteorology. The objective of this study was to evaluate two remote-sensing methods for mapping the soil moisture of a bare soil, namely, L-band radiometry using brightness temperature and ground-penetrating radar (GPR) using surface reflection inversion. Invasive time-domain reflectometry (TDR) measurements were used as a reference. A field experiment was performed in which these three methods were used to map soil moisture after controlled heterogeneous irrigation that ensured a wide range of water content. The heterogeneous irrigation pattern was reasonably well reproduced by both remote-sensing techniques. However, significant differences in the absolute moisture values retrieved were observed. This discrepancy was attributed to different sensing depths and areas and different sensitivities to soil surface roughness. For GPR, the effect of roughness was excluded by operating at low frequencies (0.2-0.8 GHz) that were not sensitive to the field surface roughness. The root mean square (rms) error between soil moisture measured by GPR and TDR was 0.038 m3·m-3. For the radiometer, the rms error decreased from 0.062 (horizontal polarization) and 0.054 (vertical polarization) to 0.020 m3·m-3 (both polarizations) after accounting for roughness using an empirical model that required calibration with reference TDR measurements. Monte Carlo simulations showed that around 20% of the reference data were required to obtain a good roughness calibration for the entire field. It was concluded that relatively accurate measurements were possible with both methods, although accounting for surface roughness was essential for radiometry.

Combined estimation of effective electrical conductivity and permittivity for soil monitoring

Abstract: The mapping of moisture content, composition and texture of soils is attracting a growing interest, in particular with the goal of evaluating threats to soil quality, such as soil salinization. Fast non-invasive geophysical surveys are often used in this context. The aim of this work was to study constitutive models that can be used to parameterize electrical conductivity and permittivity starting from a unifying conceptual approach, and to evaluate whether the information carried by one measurement type can be used to identify soil parameters that are then used to predict the other geophysical quantity. To this end, a recently-developed constitutive model was here extended and modified to consider also the grain surface conductivity, a critical component in most natural situations. The extended model was successfully tested against laboratory measurements. In addition, the new model was compared against five other equations that use similar soil parameterizations. It was concluded that only three out of the five selected models yield similar predictions, while the remaining two predict a different geophysical response for the same soil texture. Following this analysis, a methodology was developed to estimate soil salinity starting from the simultaneous measurements of bulk electrical conductivity and permittivity and validate this methodology against laboratory experiments. The method is valid in situations where the conductivity of the pore-water remains approximately constant during the measurement period. Key features of the approach proposed to map soil salinization are (i) simplicity, (ii) absence of fitting parameters and (iii) the fact that moisture content does not need to be measured or estimated independently. The methodology was tested on a large number of soil samples and proved robust and accurate.

Decision trees for digital soil mapping on subtropical basaltic steeplands

Abstract: When soil surveys are not available for land use planning activities, digital soil mapping techniques can be of assistance. Soil surveyors can process spatial information faster, to assist in the execution of traditional soil survey or predict the occurrence of soil classes across landscapes. Decision tree techniques were evaluated as tools for predicting the ocurrence of soil classes in basaltic steeplands in South Brazil. Several combinations of types of decicion tree algorithms and number of elements on terminal nodes of trees were compared using soil maps with both original and simplified legends. In general, decision tree analysis was useful for predicting occurrence of soil mapping units. Decision trees with fewer elements on terminal nodes yield higher accuracies, and legend simplification (aggregation) reduced the precision of predictions. Algorithm J48 had better performance than BF Tree, RepTree, Random Tree, and Simple Chart.

Using Canonical Correspondence Analysis (CCA) to identify the most important DEM attributes for digital soil mapping applications

Abstract: Topography has an important influence on the distribution of soils and their properties, especially in hilly lands, and related data are easily available, measurable and recognizable from digital elevation models (DEMs). To our knowledge, little attention has previously been paid to the effect of DEM attributes on the distribution of soils, using ordination methods. The objective of this study was to analyze relationships between topographical properties derived from DEM and soil distribution and to discuss their applicability in Digital Soil Mapping (DSM). The study was carried out in the Borujen area of central Zagros, Iran. A total of 13 plots (each one of 6.75 ha) were set up to calculate the percentages of the dominant soil series. Fifteen DEM attributes, including slope, aspect, curvature, maximum and minimum curvature, planform curvature, profile curvature, tangent curvature, wetness index, power index, sediment index, area solar radiation, direct radiation, diffuse radiation and direct duration were also computed. Canonical Correspondence Analysis (CCA) was used to summarize the data set and to evaluate the expected relationships. The results obtained show that there was a relatively strong correspondence between soils' series distribution and topographical properties. The DEM attributes that best related to the first axis were maximum curvature, slope and sediment index, all of which significantly positive correlated, and wetness index, direct duration and minimum curvature, all of which were negatively related. The second axis showed a negative trend with wetness index, direct duration and aspect, and a positive trend with sediment index and slope. These gradients were closely related to the first three canonical axes and explained 71.8% of the total variance of the soil series. The residual variance (28.2% of the total variance) was related to other soil forming factors, like parent material and vegetation cover, which were not investigated in this study. Considering that DEMs are still the most important source of environmental information, understanding the role of topographical factors in a region should help us to identify soils and their properties better and enable us to apply these derivates as input data in DSM.

Using EM and VERIS technology to assess land suitability for orchard and vineyard development

Abstract: Orchard and vineyard producers conduct preplant site evaluations to help prevent planting permanent tree and vine crops on lands where the crop will not perform to its highest potential or attain its full life expectancy. Physical soil characteristics within specific soil profiles and spatially throughout an orchard influence decisions on land preparation, irrigation system selection, horticultural choices, and nutrient management. Producers depend on soil surveys to help them understand the soil characteristics of the land and may be interested in technology that provides additional information. Electromagnetic induction (EM38) and four-probe soil resistance sensors (VERIS) are being used in combination with global positioning systems to map spatial variability of soils using apparent soil electrical conductivity (ECa). The hypothesis evaluated in this study is whether rapid, in situ, and relatively low-cost methods of measuring ECa (EM38 and VERIS) can effectively identify and map physical soil variability in non-saline soils. The supposition is that in non-saline soils, ECa levels will relate well to soil texture and water-holding capacity and can be used to map physical soil variability. In turn, the information can be used to guide decisions on preplant tillage, irrigation system design, water and nutritional management, and other horticultural considerations. Two sites in the Sacramento Valley were mapped each with EM38 and VERIS methods. Site-specific management zones were identified by each provider on ECa maps for each site, and then soil samples were collected by University of California researchers to verify these zones. Results showed that on non-saline soils, ECa measured with both EM38 and VERIS correlate with physical soil properties such as gravel, sand, silt, and clay content but the relationship between conductivity and these physical soil properties varied from moderately strong to weak. The strength of the correlation may be affected by several factors including how dominant soil texture is on conductivity relative to other soil properties and on methods of equipment operation, data analysis and interpretation. Overall, the commercial providers of ECa surveys in this study delivered reasonable levels of accuracy that were consistent with results reported in previous studies. At one site, an ECa map developed with VERIS provided more detail on physical soil variability to supplement published soil surveys and aided in the planning and development of a walnut orchard. At a second site, almond yield appeared to correlate well with distinctly different soil zones identified with EM38 mapping.

Hydropedological insights when considering catchment classification

Abstract: . Soil classification systems are analysed to explore the potential of developing classification systems for catchments. Soil classifications are useful to create systematic order in the overwhelming quantity of different soils in the world and to extrapolate data available for a given soil type to soils elsewhere with identical classifications. This principle also applies to catchments. However, to be useful, soil classifications have to be based on permanent characteristics as formed by the soil forming factors over often very long periods of time. When defining permanent catchment characteristics, discharge data would therefore appear to be less suitable. But permanent soil characteristics do not necessarily match with characteristics and parameters needed for functional soil characterization focusing, for example, on catchment hydrology. Hydropedology has made contributions towards the required functional characterization of soils as is illustrated for three recent hydrological catchment studies. However, much still needs to be learned about the physical behaviour of anisotropic, heterogeneous soils with varying soil structures during the year and about spatial and temporal variability. The suggestion is made therefore to first focus on improving simulation of catchment hydrology, possibly incorporating hydropedological expertise, before embarking on a catchment classification effort which involves major input of time and involves the risk of distraction. In doing so, we suggest to also define other characteristics for catchment performance than the traditionally measured discharge rates. Such characteristics may well be derived from societal issues being studied, as is illustrated for the Green Water Credits program.

Potential and limitations of using soil mapping information to understand landscape hydrology

Abstract: . This paper addresses the following points: how can whole soil data from normally available soil mapping databases (both conventional and those integrated by digital soil mapping procedures) be usefully employed in hydrology? Answering this question requires a detailed knowledge of the quality and quantity of information embedded in and behind a soil map. To this end a description of the process of drafting soil maps was prepared (which is included in Appendix A of this paper). Then a detailed screening of content and availability of soil maps and database was performed, with the objective of an analytical evaluation of the potential and the limitations of soil data obtained through soil surveys and soil mapping. Then we reclassified the soil features according to their direct, indirect or low hydrologic relevance. During this phase, we also included information regarding whether this data was obtained by qualitative, semi-quantitative or quantitative methods. The analysis was performed according to two main points of concern: (i) the hydrological interpretation of the soil data and (ii) the quality of the estimate or measurement of the soil feature. The interaction between pedology and hydrology processes representation was developed through the following Italian case studies with different hydropedological inputs: (i) comparative land evaluation models, by means of an exhaustive itinerary from simple to complex modelling applications depending on soil data availability, (ii) mapping of soil hydrological behaviour for irrigation management at the district scale, where the main hydropedological input was the application of calibrated pedo-transfer functions and the Hydrological Function Unit concept, and (iii) flood event simulation in an ungauged basin, with the functional aggregation of different soil units for a simplified soil pattern. In conclusion, we show that special care is required in handling data from soil databases if full potential is to be achieved. Further, all the case studies agree on the appropriate degree of complexity of the soil hydrological model to be applied. We also emphasise that effective interaction between pedology and hydrology to address landscape hydrology requires (i) greater awareness of the hydrological community about the type of soil information behind a soil map or a soil database, (ii) the development of a better quantitative framework by the pedological community for evaluating hydrological features, and (iii) quantitative information on soil spatial variability.