Proximal Soil Research Articles

Modeling within field variation of the compaction layer in a paddy rice field using a proximal soil sensing system

AbstractA key characteristic of flooded paddy fields is the plough pan. This is a sub‐soil layer of greater compaction and bulk density, which restricts water losses through percolation. However, the thickness of this compacted layer can be inconsistent, with consequences such as variable percolation and leaching losses of nutrients, which therefore requires precision management of soil water. Our objective was to evaluate a methodology to model the thickness of the compacted soil layer using a non‐invasive electromagnetic induction sensor (EM38‐MK2). A 2.7 ha alluvial non‐saline paddy rice field was measured with a proximal soil sensing system using the EM38‐MK2 and the apparent electrical conductivity (ECa) of the wet paddy soil was recorded at a high‐resolution (1.0 × 0.5 m). Soil bulk density (n = 10) was measured using undisturbed soil cores, which covered locations with large and small ECa values. At the same locations (within 1 m2) the depth of the different soil layers was determined by penetrometer. Then a fitting procedure was used to model the ECa – depth response functions of the EM38‐MK2, which involved solving a system of non‐linear equations and a R2 value of 0.89 was found. These predictions were evaluated using independent observations (n = 18) where a Pearson correlation coefficient of 0.87 with an RMSEE value of 0.03 m was found. The ECa measurements allowed the detail estimation of the compacted layer thickness. The link between water percolation losses and thickness of the compacted layer was confirmed by independent observations with an inverse relationship having a Pearson correlation coefficient of 0.89. This rapid, non‐invasive and cost‐effective technique offers new opportunities to measure differences in the thickness of compacted layers in water‐saturated soils. This has potential for site‐specific soil management in paddy rice fields.

Use of proximal sensors to evaluate at the sub-paddock scale a pasture growth-rate model based on light-use efficiency

Monitoring pasture growth rate is an important component of managing grazing livestock production systems. In this study, we demonstrate that a pasture growth rate (PGR) model, initially designed for NOAA AVHRR normalised difference vegetation index (NDVI) and since adapted to MODIS NDVI, can provide PGR at spatial resolution of ~2 m with an accuracy of ~2 kg DM/ha.day when incorporating in-situ sensor data. A PGR model based on light-use efficiency (LUE) was combined with in-situ measurements from proximal weather (temperature), plant (fraction of absorbed photosynthetically active radiation, fAPAR) and soil (relative moisture) sensors to calculate the growth rate of a tall fescue pasture. Based on an initial estimate of LUEmax for the candidate pasture, followed by a process of iterating LUEmax to reduce prediction errors, the model was capable of estimating PGR with a root mean square error of 1.68 kg/ha.day (R2 = 0.96, P-value ≈ 0). The iterative process proved to be a convenient means of estimating LUE of this pasture (1.59 g DM/MJ APAR) under local conditions. The application of the LUE-PGR approach to developing an in-situ pasture growth rate monitoring system is discussed.

Using proximal soil sensors and fuzzy classification for mapping Amazonian Dark Earths

We tested if hand-carried field proximal soil sensing (PSS) can be used to map the distribution of anthropogenic Amazonian Dark Earths (ADE). ADE soils are rich in archaeological artefacts, nutrients, organic matter and carbon in the very stable form of pyrogenic carbon, also referred to as black carbon or biochar. To test the capacity of PSS to detect signature ADE properties we measured electrical conductivity (ECa), magnetic susceptibility (MSa) and gamma ray data by transect sampling and compared these readings, using fuzzy classification, with datasets on chemical soil properties from a 28 ha large study area located on the Belterra Plateau of the Lower Amazon in northern Brazil. Results indicate that ECa and MSa measurements were good indicators of ADE signatures, but that the gamma radiation sensor was less useful in the deeply weathered soils. PSS and fuzzy classification can be used for rapid field mapping of ADE for both agricultural and archaeological purposes.

Prediction of Soil Texture Using FT-NIR Spectroscopy and PXRF Spectrometry With Data Fusion

Multiple proximal soil sensors provide more useful and complementary information than a single sensor does for improving the prediction of soil properties. In this study, two data fusion methods (i.e., sample data fusion and sensor data fusion) were performed using a Fourier transform near-infrared (NIR) spectrometer and a portable X-ray fluorescence (PXRF) analyzer for predicting the contents of soil clay, silt, and sand in three sets of soil sample data, which were collected in a medium-scale area and two small-scale areas, respectively. To verify the performance of these methods and confirm related theoretical analysis, calibration models on individual sample and sensor data sets, fused sample data sets, and fused sample and sensor data sets were constructed for three soil textural fractions. First, we applied stepwise multiple linear regression to PXRF measurements and partial least-squares regression to NIR spectral data in the three different sampling areas. Then, we used the sample data set from the medium-scale area as reference and combined it with other two sample data sets to calibrate the models. Finally, we further fused the principal components of NIR spectral data from principal component analysis with the PXRF measurements to improve the accuracy of soil texture prediction. It was found that the models based on fused data could significantly improve the accuracy of soil texture prediction compared with those based on individual sample or sensor data sets. The best model for the prediction of clay and sand contents was calibrated on fused PXRF measured data and NIR principal components from different sampling areas. Whereas geographical locations and soil types might impact the accuracy and fitness of the models, data fusion through multiple sensors and different sampling areas showed great promise as a technique for rapidly assessing soil textural fractions. Further evaluation of the data fusion methods suggested here may provide a practical technique for future applications in soil research.

Linking Geophysics and Soil Function Modeling—An Application Study for Biomass Production

Modeling of soil functions often faces rough estimations of soil properties. These rough estimations originate from coarse soil survey maps and miss to adequately represent small‐scale soil heterogeneity. This study presents an approach to account for this heterogeneity to improve biomass production modeling. We employed two common proximal soil sensing (PSS) methods: ground penetrating radar (GPR) and electromagnetic induction (EMI) to describe soil depths over small spatial scales at the field site Wagna (Austria). The experiment encompasses highly heterogenic soil depth variations of fine topsoil above sandy subsoil. These variations are only roughly contained in the “standard” soil parameterization (STSP) although they strongly influence the local soil water balance. To adequately map small scale soil depth variation for the entire experiment we exploited the advantages of GPR (exact detection of boundary depth between top‐ and subsoil) and EMI (rapid scan on field scales to obtain distinct soil patterns). Both, the resulting small scale “geophysical adapted” soil map and the coarse scale STSP served as soil input for Carbon and Nitrogen Dynamics (CANDY) Plant Universal Simulator (PLUS) to model plant biomass production. The validation with “geophysical adapted” parameterization indicated an improved performance with a RMSE of the modeled grain biomass of 1762.7 kg ha−1 in comparison to 3584.6 kg ha−1 with STSP. The result demonstrated PSS to provide important small‐scale soil data that can improve agro‐ecosystem modeling.

Isolation and characterization of indigenous 2,4-D herbicide degrading bacteria from an agricultural soil in proximity of Sauce Grande River, Argentina

Our objective was to isolate and characterize indigenous bacteria able to use 2,4-D as a sole carbon (C) and energy source from an agricultural soil in the Sauce Grande River basin (Argentina). Culturable-dependant and molecular methods combined were used to identify and characterize putatively dominant indigenous degrading bacteria. Physiological traits, chloride release and biomass production showed the degradative capacity of the isolates obtained and high-performance liquid chromatography (HPLC) was used to corroborate the evidence. Degrading genes (tfdA and tfdB) were detected in all isolates, and their restriction fragment length polymorphisms (RFLP) were analyzed. Altogether, our results suggest that agricultural use of 2,4-D at recommended level leads to selection for a copiotrophic degrading population. The dominant genus able to metabolize 2,4-D in this soil was identified as Cupriavidus by 16S rRNA gene sequencing and the RFLP profiles of all isolates resembled that of Cupriavidus necator JMP134, the model organism for 2,4-D degradation. The strain EMA-G showed a remarkable performance in herbicide degradation (100 % removal in <1 day) in pure culture and is a favorite candidate for future biodegradation experiments.

Performance of Automated Near‐Infrared Reflectance Spectrometry for Continuous in Situ Mapping of Soil Fertility at Field Scale

Diffuse reflectance of soils in the near‐infrared reflectance (NIR) has been related to many chemical soil properties. Diffuse reflectance spectroscopy may become a part of proximal soil sensing and contribute to bridge the gap of knowledge imposed by the inability of conventional methods to resolve the spatial patterns of soil fertility at field scale. We used a mobile automated NIR spectrophotometer (1100–2300 nm) to map the topsoil of three fields located in an organic farm “on‐the‐go” at a speed of 3 to 6 km h−1 to a depth of 15 cm. Spectral data were related to results from conventional laboratory analysis of soil P, K, Mg, soil organic matter (SOM), N, and pH. Maps and semivariograms of the principal component scores computed from the spectral information showed consistent spatial patterns present at two different scales. The strength of correlation between field spectra and soil chemical parameters was location dependent, which will make it difficult to develop cross‐field calibration models. Stable, semiquantitative predictions were obtained for soil pH, N‐tot, SOM, K‐tot, and Mg‐tot (R2: 0.71, 0.69, 0.61, 0.55, 0.53) when training data from all fields where included. To improve relationships between soil spectra and soil attributes, dual‐wavelength indices (DWIs) were used. Even though DWIs showed better partial least squares regression model results compared with smoothed or differentiated spectra, they did not improve cross‐field model calibration.

Integrating multi‐receiver electromagnetic induction measurements into the interpretation of the soil landscape around the school of gladiators atCarnuntum

SummaryRecently, the unique foundations of a school of gladiators were discovered in theRoman town ofCarnuntum (40 km southeast ofVienna,Austria) by applying a combination of non‐invasive archaeological prospection techniques such as magnetometry, ground penetrating radar, aerial photography, airborne laser scanning and airborne imaging spectroscopy. Although the well‐preserved remains of the building complex were revealed down to a depth of 1.8 m by high‐resolution near‐surface geophysics, some questions about the surrounding soil landscape remained unanswered. Therefore, a proximal soil sensing procedure based on a survey with a multi‐receiver electromagnetic induction (EMI) instrument was conducted to interpret the surroundings of the school, covering an area of 5.6 ha. We investigated the usefulness of integrating the complementary apparent electrical conductivity (ECa) and apparent magnetic susceptibility (MSa) measurements for the mapping and investigation of this soil landscape. The multipleECameasurements allowed the identification of zones with low‐conductive gravel outcrops, and zones where silty‐clayey soils were deposited on top of the underlying gravel. AnEC‐depth slicing procedure enhanced the contrast between small soil features, such as frost‐wedge pseudomorphs and drainage gullies, and their background, and provided indications about the depth extent of these features. TheMS‐depth slices showed the foundations of the school of gladiators, an aqueduct and grave monuments. After combining these results with the topography, an integrated visualization of the school in its soil landscape was obtained. This study demonstrated the potential of multi‐receiverEMIsoil surveys to map and interpret the soil landscape and to discern small natural as well as archaeological features.

Digital assessment of soil-salinity dynamics after a major flood in the Niger River valley

Salinisation threatens the sustainability of irrigated paddy fields in western Africa. To reclaim saline soils and improve the management of irrigated paddy fields, it is necessary to evaluate and monitor soil salinity in space and time. Proximal soil sensing by apparent electrical resistivity (ER) measurements can help to survey soil salinity. This study aimed to assess changes in the spatial pattern of salinity in vertisols using high resolution 3D mapping at 2 dates on an 8-ha experimental site located at Kollo, Niger, along the Niger River. For each measurement campaign, the survey combined ER and electrical conductivity (EC) measurements of a 1:5 soil:water extract. This procedure was performed in May 2008 and again in March 2011 after a 100-year flood of the study area by the Niger River. EC measurements were performed on soil samples collected at 3 depths at 140 points. ER measurements were performed at 420 points at 2 and 4 depths for the first and second campaigns, respectively. We built an inference model of EC using a regression-tree method for 3 prediction depths. The predictive data were the two ER measurements, their ratio and a qualitative description of land use. The maps generated by ordinary kriging of predicted EC revealed strong lateral salinity gradients ranging from 0.3 to 5.0dS/m (RMSE=0.79–0.83dS/m). Vertical variability in salinity was also significant and its pattern could be explained by land use, mainly irrigation intensity. In 2011, salt stocks in the upper 60cm of the soil ranged from low stocks (<2kg/m2) to extremely high stocks along the levee border (5.5–8.0kg/m2). Comparisons of the 2008 and 2011 salinity maps at 3 depths indicated stable spatial patterns and highlighted the high resilience of the system. Consequently, desalinisation efforts can only minimise soil salinity in the upper cultivated horizon.

Ecology and conservation of strangler figs in urban wall habitats

Hong Kong’s rugged topography demands creation of developable land by reclaiming the harbor and terracing hillslopes. The vertical faces between adjoining hillslope-platforms are often stabilized by stone retaining walls. Such artificial cliffs embedded in the urban matrix, as a habitat analogue of rocky cliffs, have been colonized by spontaneous-ruderal vegetation. Detailed field assessment of 289 walls with 793 trees permitted in-depth analysis of this unique tropical urban ecology to inform conservation. The 28 stonewall tree species, mainly native, are dominated by six keystone and companion Ficus (Moraceae) species. They can develop into large cliff hangers > 20 m height on the apparently precarious and inhospitable walls. The many joints between masonry blocks provide opportunities for lodging and germination of seeds brought by frugivorous birds and bats. The aft-soil behind walls provides catchment for normal root functions. Beginning as pioneers and persisting as climax members, they short-circuit the seral processes. Wall-gripping capability as large epilithic woody hemi-epiphytes is due to the strangler-fig habit, an evolutionary trait that originated in the tropical forest. The polymorphic roots with morphological plasticity and self-grafting ability, and their multiple modes of interactions with wall niches, account for firm attachment on the vertical habitat and exploration of proximal soils. A model of strangler-fig growth on stone walls serving as surrogate host tree is developed. The diverse and versatile rooting modes are the basis to sustain strangler-fig wall conditions to permit continuation of strangler-fig habit. Such unique nature-in-city and natural-cum-cultural gems deserve conservation as urban ecological heritage.

Mapping complex soil patterns with multiple‐point geostatistics

SummaryThe commonly used variogram function is incapable of modelling complex spatial patterns associated with repetitive, connected or curvilinear features, because it is a two‐point statistic. Because this was strongly limiting to petroleum‐ and hydrogeologists, they developed multiple‐point geostatistics (MPG), an approach that replaces the variogram by a training image (TI). However, soil scientists also face complex spatial patterns andMPGmight be of use to them as well. Therefore, this paper aims to introduceMPGto soil science and demonstrate its potential with a case study of polygonal subsoil patterns caused byWeichselian periglacial frost cracks inBelgium. A high‐resolution proximal soil sensing survey provided a reference image from which a continuous (655 sensor data) and a categorical (100 point observations) dataset were extracted. As a continuousTI, we used the geophysical data of another part of the field, and as categoricalTIwe used a classified photograph of an ice‐wedge network inAlaska. The resultingMPGmaps reconstructed the polygonal patterns very well and corresponded closely to the reference image. Consequently, we identifyMPGas a promising technique to map complex soil patterns and suggest that it should be added to the pedometrician's toolbox.

Using bivariate multiple-point statistics and proximal soil sensor data to map fossil ice-wedge polygons

Multiple-point statistics (MPS) is a collection of geostatistical simulation algorithms that uses a multiple-point training image (TI) as structural model instead of a two-point variogram. MPS allows to simulate more complex random fields, like phenomena characterized by spatial connectivity. A very recent development is multivariate MPS in which an ensemble of variables can be simulated simultaneously using a multivariate TI. We investigated if multivariate MPS can be used for the processing of proximal soil sensor data, i.e. interpolating the sensor data and predicting the target variable. We measured a field with fossil ice-wedge polygons in the subsoil with an electromagnetic induction sensor and used these measurements to predict the location of wedge material in the subsoil. We built a bivariate TI with a categorical image of a random polygonal network as primary variable and a continuous image of the corresponding sensor values as secondary variable. Then, we performed a bivariate reconstruction with the recently developed Direct Sampling software. The resulting E-types provided an interpolated sensor data map and a probability map predicting the location of wedge material in the subsoil. This procedure was compared to the more traditional approach of interpolating the sensor data with ordinary kriging and performing a fuzzy k-means classification. Comparing the resulting maps with an aerial photograph revealing the location of the ice-wedges through polygonal crop marks, showed that MPS reconstructed the polygonal patterns much better. The local accuracy of the MPS maps was proven by an independent quantitative validation based on nine extra measurement lines and 94 bore hole samples. As a first application in soil science, our case study showed that multivariate MPS can be used for the processing of proximal soil sensor data. The flexibility of the technique opens perspectives for other new applications and therefore multivariate MPS can become a valuable part of the pedometrical toolbox.

Monitoring the Perturbation of Soil and Groundwater Microbial Communities Due to Pig Production Activities

This study aimed to determine if biotic contaminants originating from pig production farms are disseminated into soil and groundwater microbial communities. A spatial and temporal sampling of soil and groundwater in proximity to pig production farms was conducted, and quantitative PCR (Q-PCR) was utilized to determine the abundances of tetracycline resistance genes (i.e., tetQ and tetZ) and integrase genes (i.e., intI1 and intI2). We observed that the abundances of tetZ, tetQ, intI1, and intI2 in the soils increased at least 6-fold after manure application, and their abundances remained elevated above the background for up to 16 months. Q-PCR further determined total abundances of up to 5.88 × 10(9) copies/ng DNA for tetZ, tetQ, intI1, and intI2 in some of the groundwater wells that were situated next to the manure lagoon and in the facility well used to supply water for one of the farms. We further utilized 16S rRNA-based pyrosequencing to assess the microbial communities, and our comparative analyses suggest that most of the soil samples collected before and after manure application did not change significantly, sharing a high Bray-Curtis similarity of 78.5%. In contrast, an increase in Bacteroidetes and sulfur-oxidizing bacterial populations was observed in the groundwaters collected from lagoon-associated groundwater wells. Genera associated with opportunistic human and animal pathogens, such as Acinetobacter, Arcobacter, Yersinia, and Coxiella, were detected in some of the manure-treated soils and affected groundwater wells. Feces-associated bacteria such as Streptococcus, Erysipelothrix, and Bacteroides were detected in the manure, soil, and groundwater ecosystems, suggesting a perturbation of the soil and groundwater environments by invader species from pig production activities.

In situ gradient distribution of polycyclic aromatic hydrocarbons (PAHs) in contaminated rhizosphere soil: a field study

Little information is available heretofore on the gradient distribution of persistent organic pollutants in rhizosphere on a field scale. In this field study, we seek to explore the in situ distribution gradient of polycyclic aromatic hydrocarbons (PAHs) in rhizosphere soil proximal to the roots. Clover (Trifolium pratense L.) and hyssop (Hyssopus officinalis L.) grew in situ in the contaminated field soil near a petrochemical plant and were harvested when about 30 cm tall with mature roots. Rhizosphere soils of the plants were sampled including the rhizoplane, strongly adhering soil, and loosely adhering soil. Eleven EPA-priority PAHs were detected in each layer of rhizosphere soils in proximity to the root surface. The PAH concentrations followed the descending order of bulk soil, loosely adhering soil, strongly adhering soil, and rhizoplane soil in proximity to the root surface of clover and hyssop. The rhizosphere effect (R, in percent) on PAH distribution clearly decreased with increasing distance from the root, and a more significant decrease was observed for hyssop compared to clover. R values were generally lower for three- and four-ringed PAHs in the rhizosphere, which were more significant in loosely and strongly adhering rhizosphere layers. Our field observations combined with previous potted studies demonstrated that PAH concentrations in rhizosphere soils increased with distance from the root. Results of this work provide new information on the fate of PAHs in rhizosphere.

Uncertainty propagation in VNIR reflectance spectroscopy soil organic carbon mapping

Visible and near infrared (VNIR) diffuse reflectance spectroscopy (DRS) offers high potential as a fast and accurate proximal soil sensing technique for soil carbon estimation. The objective of this study is to evaluate the use of VNIR soil spectroscopy for mapping soil organic carbon (SOC) spatial distribution on a 100ha arable field strongly affected by erosion. The analysis was performed in two main steps: firstly, we focused on the uncertainty in the VNIR spectroscopy regression model (PLSR) under varying number and locations of training samples from which an optimal number of input samples were selected; secondly, we analysed uncertainty propagation in the coupled PLSR and spatial prediction for the selected optimal number of training samples.The PLSR quality parameters are changing exponentially with increasing number of input training samples. The PLSR model constructed using only 37 samples provided a good predictive capability with R2 over 0.7 and RPD over 1.5. The uncertainty of the final map as expressed by mean standard deviation, lowered 3 times when the number of input training samples changed from 37 to 128. The accuracy of the soil map was assessed through the uncertainty propagation analysis for the purpose of evaluating how the uncertainties were partially associated with the PLSR model and partially coming from the spatial prediction propagate to the final output map. We conclude that the PLSR predictions caused lower uncertainty in comparison with uncertainty coming from spatial predictions by kriging algorithm. The study confirms that the SOC prediction as made by VNIR spectral characteristics is a powerful tool, which together with digital soil mapping techniques (DSM) provides the basis for high resolution field scale mapping.

Distance and similarity-search metrics for use with soil vis–NIR spectra

Many techniques used in soil vis–NIR sensing are based on the measurement of the similarity or distance between samples. The question that frequently arises when two samples are very close in the vis–NIR space is whether they are also close (or similar) in terms of soil compositional characteristics. A good soil vis–NIR similarity metric must be also able to reflect the soil compositional similarity. In this respect, the main aims of this work were as follows: i. investigate the relationship between soil vis–NIR similarity and soil compositional similarity and ii. evaluate different distance metric algorithms for soil vis–NIR similarity search.We evaluated the following distance metrics: Euclidean (ED), Mahalanobis (MD), spectral angle mapper (SAM), surface difference spectrum (SDS), spectral information divergence (SID), principal component distance (PC‐M), optimized PC distance (oPC‐M), locally linear embedding distance (LLE‐M) and σ‐locally linear embedding (σLLE‐M). The first five methods mentioned previously correspond to methods that operate in the spectral space while the remaining ones work by projecting the vis–NIR data onto a low dimensional space.We used a global soil vis–NIR spectral library (GSSL) to test the different distance metric algorithms. The GSSL was divided into a reference set (Xr) and an unknown set (Xu). The distance algorithms were used to find in Xr the most spectrally similar samples of Xu. In order to evaluate the compositional similarity, the clay content and pH values of the Xu were compared to the clay content and pH values of the samples found in Xr by each algorithm.The experimental results showed that the vis–NIR similarity measures that better reflect the soil compositional similarity are those corresponding to the oPC‐M, LLE‐M and σLLE‐M methods. We also show that the SDS approach is a suitable method for computing distances in the spectral space. Finally, in this paper we discuss how these methods can also be used in proximal soil vis–NIR sensing applications.

Sensor data fusion for topsoil clay mapping

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

Modeling soil electrical conductivity–depth relationships with data from proximal and penetrating ECa sensors

Apparent soil electrical conductivity (ECa), a widely used proximal soil sensing technology, is related to several important soil properties, including salinity, clay content, and bulk density. Particularly in layered soils, interpretation of ECa variations would be enhanced with better calibrations to depth-wise variations in these soil properties. Thus, the objective of this research was to combine point measurements of layer conductivity obtained using ECa-equipped penetrometers with mapped ECa data from proximal sensors for improved quantification of conductivity–depth relationships in layered soils. Data were collected from a set of large plots on a claypan-soil landscape in central Missouri which had been managed in grain and perennial grass cropping systems for 20years. Soil ECa variation with depth was represented by both two- and three-layer models that were visualized and parameterized with the use of penetrometer ECa data. The three-layer model provided a more realistic representation of the soil profiles in the study area and provided similar topsoil depth estimation accuracy as the commonly used two layer model. Penetrometer ECa data also provided efficient and accurate (r2=0.92) estimation of calibration-point topsoil depth as an alternative to soil coring and manual determination. A key to accurate model calibration was selection of points from areas of spatially-homogeneous proximal ECa. Combining penetrometer ECa with proximal ECa data improved modeling of conductivity–depth relationships in terms of model selection, model parameterization, and model calibration.

Toward the Ultimate Soil Survey: Sensing Multiple Soil and Landscape Properties in One Pass

Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling, and government soil surveys have limited utility in many precision applications. A new multi‐sensor platform was developed that records apparent soil electrical conductivity (ECa), optical reflectance readings with red and near‐infrared light‐emitting diodes and pH along with topography data. The objective of this study was to evaluate its performance for estimation of soil cation exchange capacity (CEC), organic matter (OM), and pH on eight fields in four states, comparing results with lab‐analyzed samples, USDA soil surveys, and 1‐ha grid maps. Proximal soil sensor measurements correlated well with lab‐analyzed soil samples. The OM calibrations using ECa, optical, and/or topographic data showed good performance with R2 of 0.8 or higher and ratio of prediction to deviation (RPD) of 2.5 or greater in all fields. In CEC calibrations, five of six fields had higher than 0.86 for R2 and greater than 2.8 for RPD. The pH calibration results showed RPD of 2.1 or greater and R2 of 0.76 or higher in seven fields. The sensor maps showed small‐scale variability not detected at conventional grid sample scales or with USDA soil surveys. Using the proximal soil sensors, the average root mean square error of prediction for OM was 2.78 g kg−1, CEC 1.20 cmolc kg−1, and pH 0.26 for the project fields. These values are significantly lower than the soil property ranges found in the soil surveys. This is a promising development for improving farm practices and management.

Proximal Soil Sensing ‐ edited by Viscarra Rossel, R.A., McBratney, A.B. &amp; Minasny, B.

European Journal of Soil ScienceVolume 63, Issue 6 p. 874-874 Proximal Soil Sensing - edited by Viscarra Rossel, R.A., McBratney, A.B. & Minasny, B. R. Webster, R. WebsterSearch for more papers by this author R. Webster, R. WebsterSearch for more papers by this author First published: 07 September 2012 https://doi.org/10.1111/j.1365-2389.2012.01490.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume63, Issue6December 2012Pages 874-874 RelatedInformation