Mapping Of Soil Electrical Conductivity Research Articles

Analysis of Low-Frequency Drone-Borne GPR for Root-Zone Soil Electrical Conductivity Characterization

In this study, we analysed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15-45 MHz to analyse parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-metre half-wave dipole antenna, and EMI measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping.

Evaluation of soil EC mapping driven by manual and autopilot-automated steering systems of tractor on oil palm plantation terrain

The autopilot-automated steering system is one of today’s modern technologies in a tractor’s driving system for conducting the operations in farmland. However, further study on the suitability of this steering technology on a particular geographic region is still a necessity. This study evaluated the precision of tractor operations with soil sensor implement using manual and autopilot-automated steering systems on oil palm plantation terrain in Malaysia. A New Holland TD5.75 tractor with 75 hp engine size equipped with a Trimble autopilot-automated steering system pulling a Veris 3100 soil electrical conductivity (EC) sensor was tested in this study. The findings showed that each steering system generated a little different pattern of spatial variability in interpolated soil EC maps. Apart from that, autopilot-automated steering system offered better performances by saving energy expenditure of operator and improving the field capacity of operation. Conclusively, tractor with autopilot-automated steering presented a great suitability for the use in agricultural operations in Malaysia.

Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south-east of Algeria

In semi-arid and arid areas soil salinity has adverse effects both on the environment and agricultural production. The region of Biskra (South-East of Algeria) underwent a strong agricultural transformation from traditional oasis agriculture to an almost exclusive production of dates involving market gardening throughout the year. The main goal was to predict the spatial variation of EC using geostatistics and a Geographic Information System (GIS), comparing also the performance of two classical geostatistical interpolators - Ordinary Kriging (OK), using only point data, and Cokriging (CK), introducing also auxiliary variables to improve prediction accuracy (SI gypsum and SO 4 2− , obtained from the analysis of the chemical and geochemical processes of soil salinization). For this study, a total of 42 soil samples were randomly collected from topsoil (0–15 ​cm) in the irrigated perimeter of El Ghrous, a representative rural community located in the west of Biskra. Aiming to better understand the processes that most influence the evolution of soil salinity in this area, some chemical parameters were determined, among which the electrical conductivity (EC). Moreover, some terrain parameters were derived from a digital elevation model as auxiliary information, and Normalized Difference Vegetation Index (NDVI) was calculated from satellite imagery. The prediction efficiency of the methods was evaluated by calculating the mean error (ME) and the root mean square error (RMSE). The resulting maps showed that soils in the study area are affected by salinization. Cross-validation results showed a better performance in estimating EC of CK, after the introduction of the covariates, than OK, with an RMSE value of 0.92 vs. 1.53. This suggests a greater efficiency of CK in EC prediction in this area, confirming that the introduction of some auxiliary data correlated to the target variable significantly improves the interpolation. A third kriging technique, Indicator Kriging (IK) was applied to generate a map of the probability of exceeding a given threshold. • Comparison of two different EC mapping techniques. • Introduction of auxiliary variables related to EC to improve the interpolation results. • Use of IK to individuate areas with a high probability of exceeding a salinity threshold. • Production of estimated maps for regional planning and for future agricultural projects.

Retrieval of soil salinity from Sentinel-2 multispectral imagery

ABSTRACT Soil salinity is a widespread environmental hazard and the main causes of land degradation and desertification, especially in arid and semi-arid regions. The first step in finding such a solution is providing accurate information about the severity and extent of the salinity spread in affected areas; this can be done by mapping the electrical conductivity (EC) of the soil. Utilizing the potential of high-resolution satellite imagery along with remote sensing techniques is a promising method to map salinity, as it allows for large-scale monitoring and provides high accuracy and efficiency. This paper, therefore, aims at assessing soil salinity by mapping the EC of soils, using satellite imagery from the newly launched Sentinel-2 satellite as well as Landsat-8 data. A field study was carried out using those data, and various salt features were extracted that relate the EC values of field samples to satellite-derived salt features. The study used two different regression approaches MLP and SVR. Additionally, two feature selection algorithms, GA and SFS, were implemented on the data to improve model performance. The study concludes that the proposed method for modeling salinity and the mapping of soil EC can be considered an effective approach for soil salinity monitoring.

Classification of crop and soil homogenous zones using multipolarization C-band SAR

With the acquisition of polarimetric radar data from satellites such as RADARSAT-2 and ALOS PALSAR, this research project explored the potential of using multipolarization C-band synthetic aperture radar (SAR) data to map homogenous soil and crop characteristics within agricultural fields. Homogenous zones within crop fields were defined using coincidental apparent soil electrical conductivity (EC) measured from a soil EC mapping system, green leaf area index (LAI) derived from airborne hyperspectral CASI data, and yield converted to phytomass at harvest (Ph) derived from a yield monitor. Airborne polarimetric C-band SAR images (5.6 cm) at 4 m spatial resolution were obtained over a test site in Ottawa, Canada, on 13 June, 26 June, and 19 July 2001. Linear (HH, VV, HV) and circular (RR, LL, RL) polarization images were synthesized from these data. Once the homogenous zones were determined by EC, LAI, and Ph, the multipolarization SAR data were classified based on these zones using single polarizations and combinations of linear and circular polarizations. Within-field classification accuracies of SAR data were higher when they were based on EC and LAI homogenous zones relative to Ph delineations. The lower SAR classification accuracies based on Ph zones were likely attributed to biomass zones defined relative to harvest rather than coincident with SAR data acquisition. LAI zones were better classified using early season SAR data when greater between-zone variability was present. EC homogenous zones were better classified using SAR data later in the growing season when soil variability was manifested in the vegetation response. Within-field classification performance over the season was dependent on the crop type and the incidence angle. At larger incidence angles (55°), results were more dependent on crop type when compared with smaller incidence angles (35°). In general, a single polarization was capable of discriminating many of the within-field homogenous zones, including EC and LAI zones in corn and LAI and Ph zones in wheat. The linear (HV) and circular (LL or RR) cross-polarizations were the best single polarizations to classify these homogenous zones. Within-field classification of the SAR into EC homogenous zones in the wheat field required polarization combinations, particularly combinations that included HH and VV polarizations. Circular polarizations, either as single polarizations or as part of a combination, were necessary to classify SAR data into EC or LAI homogenous zones as the season progressed. These results suggest that multipolarization SAR data are useful in classifying within-field homogenous zones of corn and wheat, but that this application will require access to data from sensors that synthesize circular polarizations.

How Well Does Zone Sampling Based on Soil Electrical Conductivity Maps Represent Soil Variability?

In zone soil sampling a field is divided into homogenous areas using an easy to measure ancillary attribute (e.g., apparent soil electrical conductivity [ECa]) and a few samples are taken from each zone to estimate the soil characteristics in each zone. This study determined if ECa–directed zone sampling in two fields in northeastern Colorado could correctly predict soil texture and soil organic matter (SOM) patterns of samples taken by a more intensive grid sample method. Each field, which were predominantly Bijou loamy sand (coarse‐loamy, mixed, superactive, mesic Ustic Haplargids), and Valentine sand (mixed, mesic Typic Ustipsamments), was divided into three ECa zones and soil texture and OM content in the top 30 cm of soil were measured. There was a significant difference in the soil texture and SOM in both fields between ECa Zone 1 and Zone 3. Logistic regression showed that in both fields, approximately 80% of the grid sample sites in ECa Zone 1 were correctly predicted. Only 50% of the grid sample sites in ECa Zone 3 were correctly predicted as Zone 3 in one field whereas 77% of the grid sites in ECa Zone 3 were correctly predicted in the other field. However, approximately 80% of the samples in the grid sites ≥10 m from the zone boundaries were classified correctly as compared to the samples that were <10 m from the boundary in which only 50 to 54% were classified correctly. These results support the utilization of ECa‐directed zone sampling as an alternative to grid soil sampling if the transition zones are avoided.

Evaluation of an on-the-go technology for soil pH mapping

Since conventional sampling and laboratory soil analysis do not provide a cost effective capability for obtaining geo-referenced measurements with adequate frequency, different on-the-go sensing techniques have been attempted. One such recently commercialized sensing system combines mapping of soil electrical conductivity and pH. The concept of direct measurement of soil pH has allowed for a substantial increase in measurement density. In this publication, soil pH maps, developed using on-the-go technology and obtained for eight production fields in six US states, were compared with corresponding maps derived from grid sampling. It was shown that with certain field conditions, on-the-go mapping can significantly increase the accuracy of soil pH maps and therefore increase the potential profitability of variable rate liming. However, in many instances, these on-the-go measurements need to be calibrated to account for a field-specific bias. After calibration, the overall error estimate for soil pH maps produced using on-the-go measurements was less than 0.3 pH, while non-calibrated on-the-go and conventional field average and grid-sampling maps produced errors greater than 0.4 pH.

Nitrogen Measurement for Variable‐Rate N Management in Maize

Numerous proximal and remote soil and plant‐testing methods are available to guide variable‐rate N management in maize production. These include preseason and late spring soil nitrate tests, late season stalk nitrate tests, handheld chlorophyll meter readings, remote sensing of soil and crop canopy properties, site‐specific data from yield monitors and soil electrical conductivity maps. Field applicators have even been equipped to sense the N status of a crop and direct the application of any needed N on‐the‐go. However, the adoption rates of variable‐rate N management are low worldwide despite the potential economic and environmental benefits of this practice and the availability of variable‐rate application technology in key areas of maize production. A major obstacle is the finding that recommended N fertilizer rates based on yield goal are often poorly correlated with actual economically optimum N rates. Nitrogen response patterns are often field and season specific and can vary widely within the same field, further complicating adoption. Paired field comparisons of uniform‐ and variable‐rate N management have revealed no consistent advantages for either strategy in yields achieved, whole‐field N usage or N use efficiency by plants. Research with other agronomic crops also indicates that the scale for determining crop N need may be as low as 1 m2. An additional constraint is that growers implementing variable‐rate N techniques often are required to install several adequately fertilized N reference strips in their fields that have proven to be both hybrid and season specific. In addition to these agronomic and logistical challenges, the maximum potential benefit of a variable N strategy vs. a conventional uniform‐rate N strategy is typically between 12 and 37 USD($)/ha. When the cost of developing and executing the variable‐rate strategy is considered, the practice shows a low economic return. In the future, innovations in N soil testing, better crop simulation models, and improved N sensing and application equipment may assist growers in capturing the benefits of site‐specific N management in maize production.

Apparent soil electrical conductivity mapping as an agricultural management tool in arid zone soils

Electromagnetic induction (EM) is a commonly used tool for non-invasive mapping of apparent soil electrical conductivity (EC a). In this paper, we examine three applications of EM surveying used in arid southwestern US agriculture: repetitive salinity mapping, soil texture mapping, and locating buried tile lines. The basic statistical modeling techniques associated with each application are described and then demonstrated using data from three different field survey projects. In the first study, pre- and post-EM surveys are used to quantify the degree of salt removal from a field leaching event. These survey results demonstrate that the degree of salt removal was spatially variable and that the leaching process was not successful in sub-areas of the field that exhibited high pre-survey salinity concentration levels. The second study demonstrates the use of EM survey data for precision soil texture mapping under non-saline conditions, and illustrates how texture prediction maps can be generated from EM survey data. The final study represents an example of how EM survey data can be used to precisely locate the positions of buried tile lines. In this latter study, two different EM survey data sets that were collected 1 year apart produced estimates of tile line positions within 1 m of each other, validating the reliability and repeatability of the proposed tile line identification strategy. These projects demonstrate three applications of EC a surveying techniques used to derive spatial information that aids in the effective management of agricultural fields.

Applying nitrogen site-specifically using soil electrical conductivity maps and precision agriculture technology.

Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.