Characterization of field‐scale dryland salinity with depth by quasi‐3d inversion of DUALEM‐1 data

To understand the limitations of saline soil and determine best management practices, simple methods need to be developed to determine the salinity distribution in a soil profile and map this variation across the landscape. Using a field study in southwestern Australia, we describe a method to map this distribution in three dimensions using a DUALEM‐1 instrument and the EM4Soil inversion software. We identified suitable parameters to invert the apparent electrical conductivity (EC_a – mS/m) data acquired with a DUALEM‐1, by comparing the estimates of true electrical conductivity (σ – mS/m) derived from electromagnetic conductivity images (EMCI) to values of soil electrical conductivity of a soil‐paste extract (EC_e) which exhibited large ranges at 0–0.25 (32.4 dS/m), 0.25–0.50 (18.6 dS/m) and 0.50–0.75 m (17.6 dS/m). We developed EMCI using EM4Soil and the quasi‐3d (q‐3d), cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor (λ) of 0.07. Using a cross‐validation approach, where we removed one in 15 of the calibration locations and predicted EC_e, the prediction was shown to have high accuracy (RMSE = 2.24 dS/m), small bias (ME = ?0.03 dS/m) and large Lin's concordance (0.94). The results were similar to those from linear regression models between EC_a and EC_e for each depth of interest but were slightly less accurate (2.26 dS/m). We conclude that the q‐3d inversion was more efficient and allowed for estimates of EC_e to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.     

Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data

The soil in arid and semi‐arid areas is often markedly saline, which can severely limit agricultural productivity. Increasingly, geophysical methods are being implemented to map the levels and areal extent of soil salinity. One of the most effective methods is electromagnetic (EM) induction with instruments designed to measure apparent soil electrical conductivity (EC_a). This study describes the generation of electromagnetic conductivity images (EMCIs) by inverting EC_a data obtained with the EM38 and EM31 devices along two closely‐spaced transects by the EM inversion approach in the EM4Soil package. The EM38 EC_a data are shown to be a more effective predictor of soil EC_e. Calibration equations based on calculated true electrical conductivity (σ) and measured electrical conductivity of a saturated soil‐paste extract (EC_e) provide reliable estimates of EC_a. The patterns of σ in a test of the method in soil developed over thick alluvium on a clay plain in central New South Wales, Australia, compare favourably with existing pedological mapping; the mounds and depressions of gilgai were strongly differentiated from the more sandy alluvial sediments that characterize prior stream channels. The overall approach is potentially useful for generating a single calibration equation that can be used to predict EC_e at various depths in the soil. Improvements in EMCI modelling can also be sought by joint inversion of EM with other geophysical datasets.     

Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Spatial prediction of the exchangeable sodium percentage at multiple depths using electromagnetic inversion modelling

Agriculture in the semi‐arid and arid areas of the world requires irrigation. However, in these areas, soils naturally contain large amounts of sodium (sodic) which can cause amongst other things, surface crusting on the topsoil or structural instability in the subsoil. The exchangeable sodium percentage (ESP) needs to be mapped to guide the application of gypsum. Whilst geostatistical techniques, such as ordinary, co‐ and 3‐D kriging have been used, they have often been criticized because they are unable to take into account soil knowledge concerning distribution, processes and factors of formation. The use of digital soil mapping methods which couple remote or proximally sensed data with soil information is increasingly becoming useful because of the production of high‐resolution ancillary data. In this study, we first invert (using EM4Soil software) the electrical conductivity (σ_a –mS/m) of DUALEM‐421 data collected along a single transect. In doing this, we generate a 2‐dimensional electromagnetic conductivity image (EMCI). We couple the estimates of electrical conductivity (σ – mS/m) at 0.30 m depth increments down to 1.5 m with measured soil ESP. We compare the results of inversion using various possible coil array configurations of the DUALEM‐421 to determine a suitable set of data. We conclude that the use of the DUALEM‐41 is optimal (r² = 0.70). We use the calibration to estimate ESP along adjacent transects where we also generate EMCI. We are thus able to estimate ESP at various depths across a clay plain and an associated prior stream channel. We conclude that the collection of additional transects of DUALEM‐421 data as well as the use of a quasi‐3‐D inversion modelling approach would improve prediction.     

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

Large areas of Morocco require irrigation and although good quality water is available in dams, farmers augment river water with poorer quality ground water, resulting in salt build‐up without a sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a linear regression (LR) between calculated true electrical conductivity (σ, mS/m) and electrical conductivity of the saturated soil‐paste extract (ECe, dS/m). Estimates of σ were obtained by inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500‐m grid survey using an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi‐3d model (EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor of 3.0. Using a ‘leave‐one‐out cross‐validation' (LOOCV), of one in 12 of the calibration sites, the use of the q‐3d model yielded a high accuracy (RMSE = 0.42 dS/m), small bias (ME = ?0.02 dS/m) and Lin's concordance (0.91). Slightly worse results were obtained using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00 dS/m; Lin's = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 + 0.041 × σ), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final maps of ECe, along with information on water used for irrigation (ECw) and the characterization of properties of the two main soil types, enabled better understanding of causes of secondary soil salinity. The approach can be applied to problematic saline areas with saline water tables.     

Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

The volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (EC_a – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm³/cm³. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm³/cm³) and approaching permanent wilting point (0.03 cm³/cm³). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.     

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

A 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 (EC_a) 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 EC_a 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 EC_a – 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 EC_a 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.     

Potential to predict depth‐specific soil–water content beneath an olive tree using electromagnetic conductivity imaging

Efficient monitoring of soil moisture is becoming increasingly important. To understand soil–plant–water dynamics, we evaluate the potential of using a multiple‐coil‐array electromagnetic induction instrument and inversion software to map soil moisture beneath an olive tree. On twelve different days, we collected apparent electrical conductivity (EC _a) data using a DUALEM ‐21S and the volumetric soil moisture (θ ) using a bank of soil moisture sensors on opposite sides of the tree. Using EM 4Soil, we inverted the EC _a data on five of the days and established a site‐specific calibration between estimates of true electrical conductivity (σ ) and θ . The strongest calibration relationship between σ and θ (R ² = 0.65) was obtained for a full‐solution, S2 algorithm and damping factor of 1.2. A leave one out cross‐validation (LOOCV ) showed the calibration was robust, with a root mean square error (RMSE ) of 0.046 m³/m³, a mean error (ME ) of 0.001 m³/m³ and a Lin's concordance of 0.72. We subsequently evaluated the calibration relationship on the seven remaining days and over a drying period of 120 days. This approach provides information about the temporal evolution of θ by a LOOCV of validation with a RMSE of 0.037, ME of −0.003 and a Lin's concordance of 0.54. Improvement could be achieved by aligning the DUALEM ‐21S in the same orientation as the sensors, with time‐lapse inversion also being advantageous.     

A new technique for measuring broadband dielectric spectra of undisturbed soil samples

A new coaxial line cell for the determination of dielectric spectra of undisturbed soil samples was developed based on a 1.625‐inch ‐ 50 Ω coaxial system. Undisturbed soil samples were collected from a soil profile of the Taunus region (Germany) and capillary saturated followed by a step‐by‐step de‐watering in a pressure plate apparatus as well as oven‐drying at 40°C. The resultant water contents of the soil samples varied from saturation to air‐dry. Permittivity measurements were performed within a frequency range from 1 MHz to 10 GHz with a vector network analyser technique. Complex effective relative permittivity or electrical conductivity was obtained by combining quasi‐analytical and numerical inversion algorithms as well as the parameterizing of measured full set S‐parameters simultaneously under consideration of a generalized fractional dielectric relaxation model (GDR). The measurement of standard materials shows that the technique provides reliable dielectric spectra up to a restricted upper frequency of 5 GHz. For the soil samples investigated, the real part of complex effective relative permittivity ?′_r,eff and the real part of complex effective electrical conductivity σ′_eff decreased with increasing matric potential or decreasing water contents. Soil texture and porosity affect the dielectric behaviour at frequencies below 1 GHz. For frequencies above 1 GHz minor texture effects were found. The presence of organic matter decreases ?′_r,eff and σ′_eff. At 1 GHz, the empirical model of Topp et al. (1980) is in close agreement with the experimentally determined real part of the effective permittivity with RMSEs ranging from 1.21 for the basal periglacial slope deposit and 1.29 for bedrock to 3.93 for the upper periglacial slope deposit (Ah). The comparison of the experimental results with a semi‐empirical dielectric mixing model shows that data, especially for the organic‐free soils, tend to be under‐estimated below 1 GHz. The main advantage of the new method compared with conventional impedance and coaxial methods is the preservation of the natural in‐situ structure and properties such as bulk density of the investigated soil samples.     

Nutrient accumulation and nitrate leaching under broiler litter amended corn fields

Abstract

Alabama's broiler chicken (Gallus gallus) industry produces large amounts of waste, which are disposed of by application to crop and pasture land. Land application of litter (manure and bedding) from broiler production can lead to contamination from losses of nutrients accumulated in soil. A study was conducted on 2 and 4% slopes from 1991 to 1993 at Belle Mina, Alabama, to determine the effects of broiler litter (BL) on soil elemental concentrations and nitrate leaching under a corn (Zea mays L.) ‐ winter rye (Secale cereale L.) cropping system amended with either: l) 9 mg#lbha^‐1 of BL, 2) 18 mg#lbha^‐1 of BL, or 3) commercial fertilizer (F) at a recommended rate. Soil was sampled to 100 cm prior to corn planting and subsequent to com harvest. Soil leachate samples were collected biweekly with wick lysimeters installed at a depth of 100 cm. Litter applications increased concentrations of soil organic carbon (C), extractable phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), copper (Cu) and zinc (Zn). Post harvest soil sampling indicated leaching of soil nitrate that was generally highest under BL18. Soil electrical conductivity measurements were highest under BL18, but values were not in the range considered detrimental to crops. Nitrate‐N (NO₃‐N) concentrations measured in soil percolate at 1‐m depth on the 2% slope were higher under F than litter treatments. Both the F and BL18 treatments produced some NO₃‐N concentrations above the primary drinking water standard, but averaged only 8.3 and 4.8 mg#lbL^‐1, respectively. The BL9 treatment consistently remained under 10 mg NO₃‐N#lbL^‐1 with a mean concentration of 1.3 mg#lbL^‐1. Overall, litter applied a 9 mg#lbha^‐1 produced agronomic results comparable to F and appeared to be the optimal rate of application under the conditions of this study.     

Determination of soil salinity based on WET measurements using the concept of salinity index

We evaluated the Malicki and Walczak model (MW) and its appropriately modification (MMW) in the prediction of the electrical conductivity of the soil solution (σ_p), utilizing the WET dielectric sensor. In the MMW approach, the prediction of σ_p requires determination of the WET‐based salinity index (X_s) and clay and sand contents of the soil. MMW appears to be more effective than MW in all cases except for the cases of finer soils when σ_p > 3 dS m^?1.     

Using time domain reflectometry for monitoring mineralization of nitrogen from soil organic matter

The mineralization of nitrogen from soil organic matter is important when one tries to optimize nitrogen fertilization and assess risks of N losses to the environment, but its measurement is laborious and expensive. We have explored the possibilities for monitoring N mineralization directly using time domain reflectometry (TDR). Net N and S mineralization were monitored over a 101‐day period in two layers (0–30 and 30–60 cm) of a loamy sand soil during aerobic incubation in a laboratory experiment. At the same time electrical conductivity of the bulk soil, σ_a, was measured by TDR. A series of calibration measurements with different amounts of KNO₃ at different soil moisture contents was made with the topsoil to calculate the electrical conductivity, σ_w, of the soil solution from σ_a and θ. The actual σ_w was determined from the conductivity of 1:2 soil:water extracts (σ_1:2) with a mass balance approach using measured NO₃^– concentrations, after correction for ions present prior to the addition of KNO₃. The average N mineralization rate in the topsoil was small (0.12 mg N kg^?1 day^?1), and, as expected, very small in the subsoil (0.023 mg N kg^?1 day^?1). In the top layer NO₃^– concentrations calculated from σ_a determined by TDR slightly underestimated measured concentrations in the first 4 weeks, and in the second half of the incubation there was a significant overestimation of measured NO₃^–. Using the sum of both measured NO₃^– and SO₄^2– reduced the overestimation. In the subsoil calculated NO₃^– concentrations strongly and consistently overestimated measured concentrations, although both followed the same trend. As S mineralization in the subsoil was very small, and initial SO₄^2– concentrations were largely taken into account in the calibration relations, SO₄^2– concentrations could not explain the overestimation. The very small NO₃^– and SO₄^2– concentrations in the B layer, at the lower limit of the concentrations used in the calibrations, are a possible explanation for the discrepancies. A separate calibration for the subsoil could also be required to improve estimates of NO₃^– concentrations.     

Groundwater Contamination by Landfill Leachates in a Karstic Aquifer

The contamination of a karstic aquifer by the leachate from the urban solid waste landfill of La Mina (Marbella, south of Spain) has been monitored. A characterization of the karstic media and the different storages of water in the carbonate rock have been deduced by the study of the hydrodynamic and hydrochemical variations in water points of the study network. The chemical compositions of four springs, three pumping wells and five piezometers in the surroundings of the landfill, yield two patterns of hydrochemical behaviour at the contaminated points: (1) the contamination at the piezometers, measured by the concentration of Cl^? and by electrical conductivity, increased over time, but was associated with the precipitation of calcite, evidenced by a reduction in the concentration of HCO₃ ^? and Ca²⁺; (2) at points near the landfill, contamination also increased, but the CO₂ from the landfill provoked an additional dissolution of carbonate minerals, a process reflected in the high concentrations of hydrogen carbonate, calcium and magnesium. The contaminated points were irregularly distributed, the most distant piezometer presenting the greatest impact, whereas no traces of contamination were detected at one piezometer close to the landfill. The irregular distribution of these processes is explained by the heterogeneity of the karstic media, with different types of storage (conduits, fractures/fissures and matrix) and a difference in density between the leachate and the groundwater.     

A profiling TDR probe for water content and electrical conductivity measurements of soils

Measurements of water content profiles are of great interest in hydrology and soil science. Time domain reflectometry (TDR) is a well‐established method for water content measurements; however, most TDR probe designs are suitable for measurements in only a small soil volume. In this article, a 1‐m long TDR profiling probe with five measurement sections is described. Unlike most other previous profiling probes, our probe allows for both dielectric permittivity (ε) and electrical conductivity (σ_a) measurements. The accuracy of the ε and σ_a measurements was excellent; the precision of the measurements was, however, significantly poorer than with a 0.20‐m long standard three rod TDR probe. The new probe was installed in a field and successfully measured water content profiles during the growing season of 2009. During an infiltration experiment it was shown that because of its geometry, the profiling probe over‐estimated the wetting‐front velocity. At a 0.10‐m depth, the over‐estimation was almost 30%. The over‐estimate will be less significant at greater depths.     

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (EC_a – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.     

Rhizodeposition of maize: Short‐term carbon budget and composition

The aim of this study was to assess differences in rhizodeposition quantity and composition from maize cropped on soil or on 1:1 (w/w) soil–sand mixture and distribution of recently assimilated C between roots, shoots, soil, soil solution, and CO₂ from root respiration. Maize was labeled in ¹⁴CO₂ atmosphere followed by subsequent simultaneous leaching and air flushing from soil. ¹⁴C was traced after 7.5 h in roots and shoots, soil, soil solution, and soil‐borne CO₂. Rhizodeposits in the leachate of the first 2 h after labeling were identified by high‐pressure liquid chromatography (HPLC) and pyrolysis–field ionization mass spectrometry (Py‐FIMS). Leachate from soil–sand contained more ¹⁴C than from soil (0.6% vs. 0.4%) and more HPLC‐detectable carboxylates (4.36 vs. 2.69 μM), especially acetate and lactate. This is either because of root response to lower nutrient concentrations in the soil–sand mixture or decreasing structural integrity of the root cells during the leaching process, or because carboxylates were more strongly sorbed to the soil compared to carbohydrates and amino acids. In contrast, Py‐FIMS total ion intensity was more than 2 times higher in leachate from soil than from soil–sand, mainly due to signals from lignin monomers. HPLC‐measured concentrations of total amino acids (1.33 μM [soil] vs. 1.03 μM [soil–sand]) and total carbohydrates (0.73 vs. 0.34 μM) and ¹⁴CO₂ from soil agreed with this pattern. Higher leachate concentrations from soil than from soil–sand for HPLC‐measured carbohydrates and amino acids and for the sum of substances detected by Py‐FIMS overcompensated the higher sorption in soil than in sand‐soil. A parallel treatment with blow‐out of the soil air but without leaching indicated that nearly all of the rhizodeposits in the treatment with leaching face decomposition to CO₂. Simultaneous application of three methods—¹⁴C‐labeling and tracing, HPLC, and Py‐FIMS—enabled us to present the budget of rhizodeposition (¹⁴C) and to analyze individual carbohydrates, carboxylates, and amino acids (HPLC) and to scan all dissolved organic substances in soil solution (Py‐FIMS) as dependent on nutrient status.     

基于EM38和WorldView-2影像的土壤盐渍化建模研究

在干旱半干旱地区,土壤盐渍化是常见的土地退化问题之一。本研究选取于田县克里雅河上游边缘典型盐渍化区域作为研究靶区,通过EM38大地电导率仪实测土壤表观电导率,提取不同系数下的土壤调节植被指数(SAVI),分析了SAVI指数与土壤电导率间的相关性,并利用同时期WorldView-2影像的敏感波段建立了基于高分辨率影像数据的土壤盐渍化偏最小二乘回归(PLSR)模型并进行了精度验证。结果表明:①从遥感影像提取SAVI指数时,在系数(L)调节范围内选取固定系数值,系数值(间隔为0.1)从0.1变化到1.0的过程中,相应提取的SAVI指数与土壤电导率的相关性明显提升,相关性系数(r)从0.30提高到0.50,并通过显著性检验(P0.01)。②选取的SAVI1.0、B6、B7、B8四种变量中,以SAVI1.0+B6+B8为变量组合所建立的PLSR模型为最优,该模型较其他变量组合建模的决定系数(R2p)提高了0.11,因此,在研究区该模型具有更好的预测能力,模型精度为RMSEC=0.77dS/m、RC2=0.68、RMSEP=0.79 dS/m、RP2=0.66、RPD=2.2。     

Establishing soil electrical conductivity ‐ Depth relations from electromagnetic induction measurements

Abstract

Principles of electromagnetic induction (EM) and field calibration approaches are discussed as they pertain to the application of EM to soil systems for the purpose of deriving soil electrical conductivity ‐ depth relations. Evidence is provided to support the utility of EM‐derived estimates of EC_a‐depth relations. Limitations of using electromagnetic induction to determine EC_a for discrete depth intervals through the soil are discussed. Current research designed to increase the accuracy of EC_a‐depth determinations by dealing with the spatial variability problem associated with salinity in soil and by mitigating some of the inherent limitations of the calibration approaches is described.     

Investigating soil physical properties and yield response in a grassland field using a dual‐sensor penetrometer and EM38

Precision‐farming applications are mainly based on site‐specific information of soil properties at the field scale. For this purpose, a number of novel sensor techniques have been developed but not intensively tested under different field conditions. This study presents a combined application of a self‐developed dual‐sensor vertical penetrometer (DVP) for measuring volumetric soil water content (VSWC) and cone index (CI), and an EM38 for soil apparent electrical conductivity (EC_a) in a pasture (1.4 ha). To verify the feasibility of the DVP for interpreting the depth‐specific information in the field, not only the soil physical properties and their geographical coordinates were measured, but also geo‐referenced yield data were collected. We found that the yield pattern was quite similar to the soil water‐content pattern of each layer (layer‐1: 5–15 cm; layer‐2: 15–25 cm, layer‐3: 25–35 cm) and EC_a pattern. Using the map‐based comparisons in conjunction with the statistical analyses, the effect of each measured soil physical property (VSWC, CI, and EC_a) on the yield was investigated. The regression between the yield and VSWC at each layer fitted a quadratic equation (R² = 0.515 at 5–15 cm; R² = 0.623, at 15–25 cm; R² = 0.406 at 25–35 cm). The negative correlation between yield and CI at each layer fitted a linear model with R² ≥ 0.510.     

Use of an electromagnetic technique to determine sodicity in saline-sodic soils

Soil sodicity is an increasing problem in arid‐land irrigated soils that decreases soil permeability and crop production and increases soil erosion. The first step towards the control of sodic soils is the accurate diagnosis of the severity and spatial extent of the problem. Rapid identification and large‐scale mapping of sodium‐affected land will help to improve sodicity management. We evaluated the effectiveness of electromagnetic induction (EM) measurements in identifying, characterizing and mapping the spatial variability of sodicity in five saline‐sodic agricultural fields in Navarre (Spain). Each field was sampled at three 30‐cm soil depth increments at 10–30 sites for a total of 267 soil samples. The number of Geonics‐EM38 measurements in each field varied between 161 and 558, for a total of 1258 ECa (apparent electrical conductivity) readings. Multiple linear regression models established for each field predicted the average profile ECe (electrical conductivity of the saturation extract) and SAR (sodium adsorption ratio of the saturation extract) from ECa. Despite the lack of a direct causal relationship between ECa and SAR, EM measurements can be satisfactorily used for characterizing the spatial distribution of soil sodicity if ECe and SAR are significantly auto‐correlated. These results provide ancillary support for using EM measurements to indirectly characterize the spatial distribution of saline‐sodic soils. More research is needed to elucidate the usefulness of EM measurements in identifying soil sodicity in a wider range of salt and/or sodium‐affected soils.