ECa Mapping Research Articles

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter-OM; phosphorous-P2O5; potassium-K2O; the sum of the exchange bases-SEB; and cation exchange capacity-CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil.

Drip Irrigation Soil-Adapted Sector Design and Optimal Location of Moisture Sensors: A Case Study in a Vineyard Plot

To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management.

The power of integrating proximal and high-resolution remote sensing for mapping SOC stocks in agricultural peatlands

Background and aimsSoil electrical conductivity (ECa) data derived from electromagnetic induction (EMI) is valuable for estimating peat thickness and soil organic carbon stocks (SOCstocks). However, generating ECa maps locally using geostatistics limits the coverage area. This study explores the use of digital soil mapping (DSM) with random forest (RF) and universal kriging (UK) to create high-resolution ECa maps from field survey EMI data. The objective is to enhance the predictive accuracy of SOCstocks models in peatlands by incorporating these ECa maps as environmental variables.MethodsThree scenarios were evaluated, combining different environmental variables and modelling techniques for ECa mapping. Scenario 1 used spectral indices from RapidEye satellite data and RF. Scenario 2 included spectral indices and terrain derivatives from LiDAR, with RF. Scenario 3 integrated spectral indices, terrain derivatives from LiDAR, and UK. Afterwards, we evaluated the effectiveness of adding ECa maps as environmental variables for SOCstocks mapping. Finally, we incorporated ECa maps from scenario 2 and RF in three ways: (a) scenario 2 variables only, (b) ECa2 with scenario 2 variables, and (c) ECa3 with scenario 2 variables.ResultsScenarios 2 (ECa2) and 3 (ECa3) outperformed scenario 1 (ECa1). The inclusion of ECa maps significantly improved the accuracy of SOCstocks models.ConclusionOur study demonstrates that DSM, combined with RF and UK techniques, enables the generation of high-resolution ECa maps from field surveys in peatlands. Incorporating these ECa maps as environmental variables enhances the accuracy of SOCstocks mapping, providing valuable insights for peatland management and carbon stock estimation.Graphical abstract

Temporal and operation-induced instability of apparent soil electrical conductivity measurements

Measuring apparent soil electrical conductivity (ECa), using galvanic contact resistivity (GCR) and electromagnetic induction (EMI) techniques, is frequently conducted to reveal spatial soil heterogeneity. Various studies have demonstrated the possibilities for significant changes in the measured quantities over time with relatively stable spatial structure representations. The objective of this study was to quantify the effects of temporal drift and operational noise for three popular ECa mapping instruments. They were placed in stationary positions approximately 8 m apart in an area with relatively low ECa. Temporal drift was assessed using a series of 4.5-h data logs recorded under different weather conditions (from extremely hot to near freezing temperatures). The two EMI instruments were also used to quantify the effect of minor changes in the height, pitch and roll of the sensor with respect to the ground. These operational noise tests were replicated over several days. Our results reveal the GCR measurements of ECa, along with perpendicular coplanar EMI measurements, have shown relatively strong stability over time. Each operational effect introduced measurement uncertainties comparable to the impact of a change in temperature and soil water content.

Soil Density Characterization in Management Zones Based on Apparent Soil Electrical Conductivity in Two Field Systems: Rainfeed and Center-Pivot Irrigation

Understanding the spatial variability of factors that influence crop yield is essential to apply site-specific management. The present study aimed to evaluate apparent soil electrical conductivity (ECa) in two fields (A = rainfeed; B = central-pivot irrigation), based on delimited management zones (MZs). In each MZ, the soil density (Sd) was characterized at two soil depths, and whether the delimitation of MZs, based on the spatial variability of ECa, was able to identify regions of the field with different Sd was assessed. In general, MZs with the highest mean value of ECa also presented the highest mean values of Sd. The highest Sd values were observed in the 0.1–0.2 m layer, regardless of the studied area. Regardless of soil texture, the proposed ECa was able to detect in-field differences in Sd. The delimitation of MZs, based on the spatial variability of ECa mapping, was able to differentiate the mean values of Sd between MZ 1 (1.53 g cm−3) and MZ 2 (1.67 g cm−3) in field A, in the 0.1–0.2 m layer. A statistical difference was observed for the mean values of Sd, in MZ 1, at layer 0.1–0.2 m, when comparing the two fields: A (1.53 g cm−3) and B (1.64 g cm−3). We suggest that further studies should be carried out to confirm the efficiency of ECa in detecting the soil bulk density at different soil depths.

Characterization of the soil compaction based on the mapping of the apparent electrical conductivity

Based on the measurement of soil penetration resistance (PR), it is possible to identify compacted soil layers, where root growth may be harmed, affecting crop development and yield. The objective of this work was to analyze the use of management zones (MZ), delimited on the basis of mapping of the spatial variability of the soil apparent electrical conductivity (ECa), in the differentiation of soil compaction levels. The work was carried out in a 25.8-ha no-tillage area, cultivated under a center pivot. The ECa was measured under two soil moisture conditions (13.7 and 16.45%), using the Terram® equipment. Soil penetration resistance (PR) was measured using the SoloStar PLG5500 penetrograph. Based on the spatial variability ECa mapping, management zones (2, 3, and 4 zones) were delimited. The mean PR values ??of each MZ were compared by the t-test of means. It was possible to differentiate mean values ??of penetration resistance (PR), which vary from 0.9 to 2.10 MPa, from the characterization of management classes generated on the basis of the ECa spatial variability. The highest stratification of PR values ??was obtained as a function of sampling directed at delimited management zones when the soil had lower moisture content (13.7%). The highest mean PR values ??were obtained for the split of the ECa map into at least three classes. It was identified that for the study area there is no need to perform any mechanical decompaction operation.

SPATIAL VARIABILITY OF SOIL APPARENT ELECTRICAL CONDUCTIVITY - EFFECT OF THE NUMBER OF SUBSAMPLES

Soil apparent electrical conductivity (ECa) sensors have been used to detect spatial variability because they correlate with soil attributes. Studies with soil attributes have shown that the number of subsamples and sampling points influences mapping. However, there are no studies that investigated the influence of sampling or subsampling density on ECa maps. Therefore, this study verified the influence of ECa readings per sample point on the semivariance and kriging analysis. The data were collected from an area (2.5 ha) of coffee plants. One hundred sampling points were measured considering 20 readings each. 1, 5, 10, 15, and 20 sample point readings were tested. The influence of the number of readings per sampling point on the ECa mapping was determined using linear regression analysis at a significance level of 5%. The results obtained showed that ECa readings per sampling point significantly influence ECa maps. In addition, they demonstrated that reducing the number of readings per sampling point increases prediction errors by kriging. Thus, ECa maps determined with the highest readings per sampling point were mostly accurate.

Finding Suitable Transect Spacing and Sampling Designs for Accurate Soil ECa Mapping from EM38-MK2

Finding an ideal sampling design is a crucial stage in detailed soil mapping to assure reasonable accuracy of resulting soil property maps. This study aimed to evaluate the influence of sampling designs and sample sizes on the quality of soil apparent electrical conductivity (ECa) maps from an electromagnetic sensor survey. Twenty-six (26) parallel transects were gathered in a 72-ha plot in Southeastern Brazil. Soil ECa measurements using an on-the-go electromagnetic induction sensor were taken every second using sensor vertical orientation. Two approaches were used to reduce the sample size and simulate kriging interpolations of soil ECa. Firstly, the number of transect lines was reduced by increasing the distance between them; thus, 26 transects with 40 m spacing; 13 with 80 m; 7 with 150 m; and 4 with 300 m. Secondly, random point selection and Douglas-Peucker algorithms were used to derive four reduced datasets by removing 25, 50, 75, and 95% of the points from the ECa survey dataset. Soil ECa was interpolated at 5 m output spatial resolution using ordinary kriging and the four datasets from each simulation (a total of twelve datasets). Map uncertainty was assessed by root mean square error and mean error metrics from 400 random samples previously selected for external map validation. Maps were evaluated on their uncertainty and spatial structure of variation. The transect elimination approach showed that maps produced with transect spacing up to 150 m could preserve the spatial structure of ECa variations. Douglas-Peucker results showed lower nugget values than random point simulations for all selected sample densities, except for a 95% point reduction. The soil ECa maps derived from the 75% reduced dataset (by random sampling or Douglas-Peucker) or from 13 transect lines (80 m spacing) showed reasonable accuracy (RMSE of validation circa 0.7) relative to the map interpolated from all survey points (RMSE of 0.5), suggesting that transect spacing of 80 m and reading intervals greater than one second can be used for improving the efficiency of on-the-go soil ECa surveys.

Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China

Tarim River Basin is experiencing heavy soil degeneration in a long term because of the extreme natural conditions, added with improper human activities such as reclamation and rejected field repeatedly, which hindered the soil health. One of the mainly form is soil salinization. Spatial distribution and variation of soil salinity is essential both for agricultural resource management and local economic development. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since 1980s while land use and climate have undergone major changed. Electromagnetic induction (EMI) has been successfully used to directly measurement the spatial distribution of targeting soil property at field- scale, and apparent electrical conductivity (ECa, mS m−1) has become a surrogate of soil salinity (EC, dS m−1) studied by many researchers at local scale. However, the effectiveness of this equipment has not been verified in the typical soil salinization areas in southern Xinjiang, especially on a large scale. This study was aimed to test the performance of ECa jointed with Random Forest (RF) for soil salinity regional–scale mapping at a typical arid area, taking Tarim River Basin as an example. The result showed that ECa together with environmental derivative variables and with RF were suited for regional–scale soil salinity mapping. Predicted accuracy of EC was higher at surface (0–20 cm, R2 = 0.65, RMSE = 5.59) and deeper soil depth (60–80 cm, R2 = 0.63, RMSE = 2.00, and 80–100 cm, R2 = 0.61, RMSE = 1.73), lower at transitional zone (20–40 cm, R2 = 0.55, RMSE = 2.66, and 40–60 cm, R2 = 0.51, RMSE = 2.49). When ECa is involved in modeling, the prediction accuracy of multiple depths of EC is improved by 13.33%–61.54%, of which the most obvious depths are 60–80 cm and 0–20 cm. The results of variable importance show that SoilGrids were also favored the power EC model. Hence, we strongly recommended to joint EMI reads with remote sensing imagery for soil salinity monitoring at large scale in southern Xinjiang. These EC and ECa map can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor water and salt dynamics, and a guide for the design of future soil surveys.

Seeding rate in soybean according to the soil apparent electrical conductivity.

This work aimed to study different seeding rates in soybean, at management zones determined by the mapping of the soil apparent electrical conductivity (ECa) and its relationships with plant phenology and grain yield (GY). The experiment consisted of a completely randomized design, with six replications. The plant population ranged between 311,000, 360,000, and 422,000 plants ha-1, and the fixed population (360,000 plants ha-1). The management zone with the least yield potential, received the highest seed population. The management zone with the highest plant vigor potential, received the lowest seed population. The variables plant height, Normalized Difference Vegetation Index (NDVI) at 50, 66, and 92 days after emergence (DAE), one hundred-grain weight, and GY were analyzed. ECa maps can be used to decide the seed population of the soybean. The decision strategy of increasing 20% of the seed soybean population on the smaller ECa map zones, and decreasing 20% seed population on higher ECa zones was effective and resulted in similar GY, even with the negative pressure of the high resistance of penetration (RP) values in some zones. GY map variability was influenced by ECa 0-0.2 m, by NDVI at 92 DAE and by RP 0.4-0.6 m soil layer.

Combining Evapotranspiration and Soil Apparent Electrical Conductivity Mapping to Identify Potential Precision Irrigation Benefits

Precision irrigation optimizes the spatiotemporal application of water using evapotranspiration (ET) maps to assess water stress or soil apparent electrical conductivity (ECa) maps as a proxy for plant available water content. However, ET and ECa maps are rarely used together. We developed high-resolution ET and ECa maps for six irrigated fields in the Midwest United States between 2014–2016. Our research goals were to (1) validate ET maps developed using the High-Resolution Mapping of EvapoTranspiration (HRMET) model and aerial imagery via comparison with ground observations in potato, sweet corn, and pea agroecosystems; (2) characterize relationships between ET and ECa; and (3) identify potential precision irrigation benefits across rotations. We demonstrated the synergy of combined ET and ECa mapping for evaluating whether intrafield differences in ECa correspond to actual water use for different crop rotations. We found that ET and ECa have stronger relationships in sweet corn and potato rotations than field corn. Thus, sweet corn and potato crops may benefit more from precision irrigation than field corn, even when grown rotationally on the same field. We recommend that future research consider crop rotation, intrafield soil variability, and existing irrigation practices together when determining potential water use, savings, and yield gains from precision irrigation.

Field Electrical Conductivity (EC) Mapping of a Selective Irrigated Units at Northern Al-Jazirah Irrigation Project, Iraq Using GIS and GPS Techniques

The application of apparent electrical conductivity (ECa) to agriculture is due to the soil salinity measurement, which represents a problem in arid areas associated with irrigated agricultural land and shallow aquifers. The purpose of this paper is to apply the modern geo-referenced technology by using LandMapper ERM-02 device and GPS receiver to measure manually the soil ECa of (50) sites in the irrigated units N15 and N16 at Northern Al-Jazirah Irrigation Project. The remote sensing data, the map of Northern Al-Jazirah Irrigation Project and GIS are utilized to support the analysis and the spatial information of the measured ECa data. A kriging interpolation method is adopted to predict the ECa values at non-surveyed location and to create the final soil ECa map. The results of the field measurements show that the ECa in the study area varies between 2.66 to 51.74 dS/m. The results of the geostatistical analysis and the regression function of the ECa data indicate a decrease in the errors associated with the soil ECa interpolated map with increasing the values of soil ECa in the study area. However, the studied method is less accurate than the laboratory based soil sample analysis method, but for a quick estimate of soil salinity, this method is highly satisfied.

Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

Understanding spatial and temporal variability patterns of crop yield and their relationship with soil properties can provide decision support to optimize crop management. The objectives of this study were to (1) determine the spatial and temporal variability of cotton (Gossypium hirsutum L.) lint yield over different growing seasons; (2) evaluate the relationship between spatial and temporal yield patterns and apparent soil electrical conductivity (ECa). This study was conducted in eight production fields, six with 50 ha and two with 25 ha, on the Southern High Plains (SHP) from 2000 to 2003. Cotton yield and ECa data were collected using a yield monitor and an ECa mapping system, respectively. The amount and pattern of spatial and temporal yield variability varied with the field. Fields with high variability in ECa exhibited a stronger association between spatial and temporal yield patterns and ECa, indicating that soil properties related to ECa were major factors influencing yield variability. The application of ECa for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and ECa variation patterns. For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons. Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs. For unstable yield, however, long-term management practices are difficult to implement. For these fields with unstable yield patterns, within season site-specific management can be a better choice. Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times.

Apparent soil electrical conductivity in two different soil types

ABSTRACT Mapping the apparent soil electrical conductivity (ECa) has become important for the characterization of the soil variability in precision agriculture systems. Could the ECa be used to locate the soil sampling points for mapping the chemical and physical soil attributes? The objective of this work was to examine the relations between ECa and soil attributes in two fields presenting different soil textures. In each field, 50 sampling points were chosen using a path that presented a high variability of ECa obtained from a preliminary ECa map. At each sampling point, the ECa was measured in soil depths of 0-20, 0-40 and 0-60 cm. In addition, at each point, soil samples were collected for the determination of physical and chemical attributes in the laboratory. The ECa data obtained for different soil depths was very similar. A large number of significant correlations between ECa and the soil attributes were found. In the sandy clay loam texture field there was no correlation between ECa and organic matter or between ECa and soil clay and sand content. However, a significant positive correlation was shown for the remaining phosphorus. In the sandy loam texture field the ECa had a significant positive correlation with clay content and a significant negative correlation with sand content. The results suggest that the mapping of apparent soil electrical conductivity does not replace traditional soil sampling, however, it can be used as information to delimit regions in a field that have similar soil attributes.

Connecting crop models with highly resolved sensor observations to improve site-specific fertilisation

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

A Preliminary Study of the Application of Electromagnetic Conductivity Meter on Soil Properties of Paddy Cultivation Areas at Wue Village, Jantho, Aceh Besar District, Indonesia

Conventional soil sampling is time consuming and requires meticulous laboratory analysis. Hence, mapping of soil apparent in respect to electrical conductivity (ECa) has been developed to identify areas of contrasting soil properties. Such ECa values are represent measures of soil properties. The sensor system, GF Instrument model CMD-4 were used to analyze soil physical properties. This system consists of three important parts, ECa sensor, data logger and Global Positioning System (DGPS) receiver. This research was aimed to evaluate the relationships between ECa and soil properties as well as the yield of rice (paddy) in paddy’s farming fields. One study site was chosen. The sensor was pulled through a plotted area of 0.25 km2. The distribution map of ECa was developed to identify the contrast of ECa. More than 100 ECa of data points were collected in 3-hour for the large plot. The data was later transferred to a notebook computer for generation of ECa maps using Surfer 11 software. According to the data analyses, field and ECa showed positive correlation. The average values of ECa are significantly different between hilly area and drainage canal area signifying differences in soil structure. Soil ECa could provide a measure of the spatial differences associated with soil physical and chemical properties, which for paddy soil may be a measure of soil suitability for crop growth and its productivity. This sensor can measure the soil ECa through the field quickly for detailed features of the soil and can be operated by just one worker. The ECa map provides some ideas for future soil management

Soil mapping for improved phenotyping of drought resistance in lowland rice fields

Phenotyping of drought resistance traits in rice is challenging at the field level due to the spatial variability of soil characteristics linked to soil water content. Apparent soil electrical conductivity (ECa) mapping may therefore be useful for characterizing spatial variability and improving phenotyping efforts in drought screening trials. ECa mapping was conducted over two seasons in lowland rice drought phenotyping trials. ECa was positively correlated with clay content and soil depth, and negatively correlated with volumetric soil water content during periods of moderate to severe drought. In addition, ECa was negatively correlated with indicators of plant performance including canopy temperature, normalized difference vegetation index, biomass, plant height, and grain yield. The results suggest that clay content and topsoil depth were the primary factors influencing the timing and severity of the drought response during the dry down of the field. Overall, the results illustrate the usefulness of ECa mapping for the spatio-temporal characterization of soil heterogeneity that influences plant performance under drought in lowland rice fields. ECa mapping can be useful for optimizing experimental plot layouts to ensure their uniformity and to improve validity of drought phenotyping and breeding trials.

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

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.

Comparison between electromagnetic induction and fluxgate gradiometer measurements on the buried remains of a 17th century castle

This study aimed to evaluate the different configurations of an electromagnetic induction (EMI) sensor, the EM38DD (Geonics Limited, Canada) with fluxgate gradiometer measurements on an archaeological site. The EM38DD allows measuring both the apparent magnetic susceptibility (MSa or χ a) and the apparent electrical conductivity (ECa or σ a) in two different coil orientations. A gradiometer measures the lateral variations of the vertical magnetic field gradient, caused by the induced and remanent magnetisations. An archaeological site where historical documents indicated the presence of a 17th century brick castle was selected as a test area. The results of the first survey with the EM38DD showed very strong magnetic anomalies in the central field, which were caused by the brick remains of the castle. Therefore, a smaller area was chosen within this field to compare the different configurations of the EM38DD with the gradiometer at the same measurement resolution. The most useful results with the EM38DD were obtained from the MSa measured in a vertical coplanar orientation. Its anomalies corresponded well with the gradiometer anomalies. The gradiometer anomalies were sharper defined than the EM38DD anomalies, but were complicated by the bipolar response pattern. The MSa map in horizontal coplanar orientation was very difficult to interpret, due to the less optimal spatial sensitivity. The wall remains were not visible in the ECa map in horizontal coplanar orientation, although other interesting anomalies were detected.

Geophysical imaging of watershed subsurface patterns and prediction of soil texture and water holding capacity

The spatial distribution of subsurface soil textural properties across the landscape is an important control on the hydrological and ecological function of a watershed. Traditional methods of mapping soils involving subjective assignment of soil boundaries are inadequate for studies requiring a quantitative assessment of the landscape and its subsurface connectivity and storage capacity. Geophysical methods such as electromagnetic induction (EMI) provide the possibility of obtaining high‐resolution images across a landscape to identify subtle changes in subsurface soil patterns. In this work we show how EMI can be used to image the subsurface of a ∼38 ha watershed. We present an imaging approach using kriging to interpolate and sequential Gaussian simulation to estimate the uncertainty in the maps. We also explore the idea of difference ECamapping to try to exploit changes in soil moisture to identify more hydrologically active locations. In addition, we use a digital elevation model to identify flow paths and compare these with the ECameasurement as a function of distance. Finally, we perform a more traditional calibration of ECawith clay percentage across the watershed and determine soil water holding capacity (SWHC). The values of SWHC range from 0.07 to 0.22 m3m−3across the watershed, which contrast with the uniform value of 0.13 derived from the traditional soil survey maps. Additional work is needed to appropriately interpret and incorporate EMI data into hydrological studies; however, we argue that there is considerable merit in identifying subsurface soil patterns from these geophysical images.