Electrical Conductivity Mapping Research Articles

On-farm experimentation of precision agriculture for differential seed and fertilizer management in semi-arid rainfed zones

Abstract Introduction This study explores the integration of precision agriculture technologies (PATs) in rainfed cereal production within semi-arid regions. Methods utilizing the Veris 3100 sensor for apparent soil electrical conductivity (ECa) mapping, differentiated management zones (MZs) were established in experimental plots in Valsalada, NE Spain. Site-specific variable dose technology was applied for seed and fertilizer applications, tailoring inputs to distinct fertility levels within each MZ. Emphasizing nitrogen (N) management, the study evaluated the impact of variable-rate applications on crop growth, yield, nitrogen use efficiency (NUE), and economic returns. For the 2021/2022 and 2022/2023 seasons, seeding rates ranged from 350 to 450 grains/m2, and basal fertilizer dosages varied between high and low levels. Additionally, the total nitrogen units were distributed differently between the two seasons, while maintaining a uniform topdressing fertilizer dose across all treatments. Results Results revealed a significant increase in yield in MZ 2 (higher fertility) compared to MZ 1 (lower fertility). NUE demonstrated notable improvement in MZ 2, emphasizing the effectiveness of variable-rate N applications. Economic returns, calculated as partial net income, showed a considerable advantage in MZ 2 over MZ 1, resulting in negative outcomes for low-fertility areas in several of the analyzed scenarios, and highlighting the financial benefits of tailored input management. Conclusion This research provides quantitative evidence supporting the viability and advantages of adopting PATs in rainfed cereal production. The study contributes valuable insights into optimizing input strategies, enhancing N management, and improving economic returns in semi-arid regions.

Perpendicular Electrical Conductivity in the Topside Ionosphere Derived from Swarm Measurements

The study of the physical properties of the topside ionosphere is fundamental to investigating the energy balance of the ionosphere and developing accurate models to predict relevant phenomena, which are often at the root of Space Weather effects in the near-Earth environment. One of the most important physical parameters characterising the ionospheric medium is electrical conductivity, which is crucial for the onset and amplification of ionospheric currents and for calculating the power density dissipated by such currents. We characterise, for the first time, electrical conductivity in the direction perpendicular to the geomagnetic field, namely Pedersen and Hall conductivities, in the topside ionosphere at an altitude of about 450 km. For this purpose, we use eight years of in situ simultaneous measurements of electron density, electron temperature and geomagnetic field strength acquired by the Swarm A satellite. We present global statistical maps of perpendicular electrical conductivity and study their variations depending on magnetic latitude and local time, seasons, and solar activity. Our findings indicate that the most prominent features of perpendicular electrical conductivity are located at low latitudes and are probably driven by the complex dynamics of the Equatorial Ionisation Anomaly. At higher latitudes, perpendicular conductivity is a few orders of magnitude lower than that at low latitudes. Nevertheless, conductivity features are modulated by solar activity and seasonal variations at all latitudes.

Gaussian process regression for three-dimensional soil mapping over multiple spatial supports

This study investigates the complexity of spatial soil modelling, particularly focusing on the challenge of variable vertical support in traditional soil data collection. Traditional soil sampling, described in terms of horizons, often fails to accurately pinpoint the specific depths for specific soil properties. This gap is significant, as depth-specific data is crucial for a thorough understanding of soil formation processes and for assessing potential environmental impacts. In digital soil mapping (DSM), the prevalent reliance on standardised depth intervals and mass-preserving spline functions for data resampling results in a modelling approach that tends to disregard depth-related details, thereby introducing potential uncertainties.To address these limitations, this work explores how effectively Gaussian process regression (GPR) can model soil in 3D. This technique comprises two key components: a mean function and a semivariogram-like kernel. Unlike conventional methods that make a single prediction, GPR provides detailed probability distributions. This capability allows for the quantification of prediction uncertainty at various points, offering insights for decision-making and risk assessment purposes. Moreover, GPR has the capability to make volume or block estimates and assess associated uncertainties. Enabling volume-based predictions enriches the range of strategies available for land management.In this research, we employ GPR as a novel 3D soil modelling technique and compare its performance with traditional spline-based methods. The comparison is conducted through a case study on a farm in northern New South Wales, Australia, focusing on the 3D mapping of soil pH and electrical conductivity (EC). Our results demonstrate the ability of GPR to estimate soil properties across various volumes, utilising data from multiple vertical supports, thereby offering a more versatile approach for soil modelling in diverse spatial contexts.

Investigation of the contamination behavior in water and soil of an inactive dump from chemical analysis and geophysical method

In rapidly growing urban areas of developing countries, inappropriate urbanization practices near water bodies lead to significant environmental degradation, including deforestation, soil impermeabilization, and direct waste discharge into rivers and streams. This not only compromises water quality and ecosystem health but also exacerbates flood risks for nearby communities. Dumpsites, common in these regions due to inadequate waste management infrastructure, pose significant environmental threats, including soil and groundwater contamination. Our study employed a frequency-domain electromagnetic survey on an inactive dumpsite adjacent to one of Brazil’s major rivers. This geophysical method provided critical insights for geoenvironmental investigations, facilitating the collection of soil and groundwater samples to understand leachate behavior. We found that the shallower electrical conductivity map aligned with the waste disposal area's boundaries, verified by on-site surveys and excavations. Historical aerial images supported this interpretation and helped explain conductive electrical anomalies in the deeper conductivity map, linked to old irregular housing and waste disposal around the dumpsite. Moreover, the electrical conductivity maps revealed NE-oriented percolation at depth in the study area. Groundwater monitoring wells and soil analyses identified chemical elements exceeding standards set by Brazilian and international environmental agencies in this flow direction. These findings highlight the importance of using investigation techniques with superior spatial resolution compared to traditional environmental monitoring approaches. Electromagnetic methods prove effective in enhancing understanding and management of contamination in dumpsite environments.

Digital Mapping of Soil pH and Electrical Conductivity Using Geostatistics and Machine Learning

This study investigates the spatial variability of soil pH and electrical conductivity (EC) in Suryapet district of Southern Telangana Zone through various digital soil mapping approaches. The 202 surface (0-15cm) soil samples were collected and analysed for pH and EC. The analysed data was further divided into calibration set and validation set in the ratio of 75:25. The geostatistical techniques like Ordinary Kriging, Inverse Distance Weighting (IDW) and Regression Kriging and data mining technique like random forest technique were used to predict the spatial distribution of pH and EC (dSm-1) over the study area. The accuracy of these methods was assessed using validation data set by calculating RMSE, ME and R2 values. The results showed that among all the approaches, random forest (RF) technique performed better with lower RMSE, ME and higher R2 values for spatial prediction of soil pH (RMSE=0.014, ME=0.28 and R2=0.81) and EC (RMSE=0.134, ME=0.022 and R2=0.73). The RF predicted maps show that the pH of soils varied from neutral (6.5-7.5) to slightly alkaline (7.5-8.5) and the soils of Suryapet district were considered as non-saline (EC: 0-2 dSm-1). The findings of the current study shows that among digital soil mapping techniques, random forest model can be an effective tool for assessing spatial variability of soil pH and EC for further studies.

Phantom evaluation of electrical conductivity mapping by MRI: Comparison to vector network analyzer measurements and spatial resolution assessment.

To evaluate the performance of various MR electrical properties tomography (MR-EPT) methods at 3 T in terms of absolute quantification and spatial resolution limit for electrical conductivity. Absolute quantification as well as spatial resolution performance were evaluated on homogeneous phantoms and a phantom with holes of different sizes, respectively. Ground-truth conductivities were measured with an open-ended coaxial probe connected to a vector network analyzer (VNA). Four widely used MR-EPT reconstruction methods were investigated: phase-based Helmholtz (PB), phase-based convection-reaction (PB-cr), image-based (IB), and generalized-image-based (GIB). These methods were compared using the same complex images from a 1 mm-isotropic UTE sequence. Alternative transceive phase acquisition sequences were also compared in PB and PB-cr. In large homogeneous phantoms, all methods showed a strong correlation with ground truth conductivities (r > 0.99); however, GIB was the best in terms of accuracy, spatial uniformity, and robustness to boundary artifacts. In the resolution phantom, the normalized root-mean-squared error of all methods grew rapidly (>0.40) when the hole size was below 10 mm, with simplified methods (PB and IB), or below 5 mm, with generalized methods (PB-cr and GIB). VNA measurements are essential to assess the accuracy of MR-EPT. In this study, all tested MR-EPT methods correlated strongly with the VNA measurements. The UTE sequence is recommended for MR-EPT, with the GIB method providing good accuracy for structures down to 5 mm. Structures below 5 mm may still be detected in the conductivity maps, but with significantly lower accuracy.

Fast high-resolution electric properties tomography using three-dimensional quantitative transient-state imaging-based water fraction estimation.

In this study, we aimed to develop a fast and robust high-resolution technique for clinically feasible electrical properties tomography based on water content maps (wEPT) using Quantitative Transient-state Imaging (QTI), a multiparametric transient state-based method that is similar to MR fingerprinting. Compared with the original wEPT implementation based on standard spin-echo acquisition, QTI provides robust electrical properties quantification towards B1 + inhomogeneities and full quantitative relaxometry data. To validate the proposed approach, 3D QTI data of 12 healthy volunteers were acquired on a 1.5 T scanner. QTI-provided T1 maps were used to compute water content maps of the tissues using an empirical relationship based on literature ex-vivo measurements. Assuming that electrical properties are modulated mainly by tissue water content, the water content maps were used to derive electrical conductivity and relative permittivity maps. The proposed technique was compared with a conventional phase-only Helmholtz EPT (HH-EPT) acquisition both within whole white matter, gray matter, and cerebrospinal fluid masks, and within different white and gray matter subregions. In addition, QTI-based wEPT was retrospectively applied to four multiple sclerosis adolescent and adult patients, compared with conventional contrast-weighted imaging in terms of lesion delineation, and quantitatively assessed by measuring the variation of electrical properties in lesions. Resultsobtained with the proposed approach agreed well with theoretical predictions and previous in vivo findings in both white and gray matter. The reconstructed maps showed greater anatomical detail and lower variability compared with standard phase-only HH-EPT. The technique can potentially improve delineation of pathology when compared with conventional contrast-weighted imaging and was able to detect significant variations in lesions with respect to normal-appearing tissues. In conclusion, QTI can reliably measure conductivity and relative permittivity of brain tissues within a short scan time, opening the way to the study of electric properties in clinical settings.

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.

Approaches to Consider for Site-Specific Field Mapping

This publication discusses the general concept of site-specific farming, i.e., dividing a farm into several smaller management parcels instead of considering the whole farm as a single unit. This publication provides information on methods available that could be incorporated into the farming system for smart decision-making, for example, varying nutrient applications. The publication also discusses Normalized Difference Vegetative Index (NDVI) maps, Global Positioning System (GPS) units, yield monitors, and electrical conductivity maps.

Zonal Application of Plant Growth Regulator in Cotton to Reduce Variability and Increase Yield in a Highly Variable Field

Variable-rate application has great potential to reduce variability and increase yield by spatially optimizing agricultural inputs. In cotton, plant growth regulators (PGRs) control excessive growth and provide suitable plant height for harvest operations. This study evaluates the effect of variable-rate PGR application compared to constant-rate application to reduce yield spatial variability and increase yield. The variable-rate approach was carried out in 2020 based on zonal applications defined by clustering analysis using soil electrical conductivity, vegetation indexes, and yield maps. Application doses and timings were determined by integrating plant height measurements for the whole field in 2019 and by zone in 2020. To compare the two procedures, cultivar and plant populations were kept constant; fertilization and accumulated rain were similar in both seasons. A reduction in yield spatial variability due to the zonal application was observed, with yield coefficient of variation (CV) decreasing from 18% in 2019 to 12% in 2020. Spatial and temporal analysis of Normalized Difference Vegetation Index satellite images showed higher CV values in 2019 (constant-rate) reaching 30% at the end of the season, whereas in 2020 (variable-rate) CV was constant (approximately 10%). Cotton yield increased from 3.5 to 4.3 t ha-1 between 2019 and 2020, which can be partially attributed to the variable-rate approach. The variable-rate approach based on application zones and plant height measurements was a viable strategy for reducing yield spatial variability and likely increasing yield in a highly variable cotton field.

Hydrogeochemical characterization of the alluvial aquifer of Catania Plain, Sicily (South Italy)

A hydrogeochemical study was carried out on the shallow Catania Plain alluvial aquifer, in eastern Sicily to reconstruct its hydrogeological structure, the meteoric recharge and to assess the influence of human activities on groundwater. To characterize the geochemistry of the shallow aquifer, two sampling campaigns were carried out, August–October 2004 and April–May 2005 in 47 sites distributed throughout the plain. The samples were collected and analyzed for physical–chemical parameters and major ions, as well as stable isotopes (δ18O and δ2H). Alluvial deposits with heterogeneous grain sizes constitute the aquifer. Varying conditions of vertical and horizontal permeability lead to the presence of a multilayered aquifer with different conditions of confinement and partial interconnection among layers. The sampled waters were separated into four groups of different compositions due to the water–rock interaction with the different lithologies present in and around the study area. Maps of electrical conductivity and sulfate show a systematic control of land use, in correspondence with the biggest farms. High sulfate concentration is due to both the natural interaction between local meteoric waters and Etna’s plume and the mixing with groundwater coming from the area where evaporitic rocks of the Gessoso Solfifera formation are present. In addition, anthropogenic contamination cannot be ruled out. A rain gauge network, consisting of 3 sites located at different altitudes, was installed to collect rain waters to determine isotopic data (δ2H and δ18O) and to measure the monthly rainfall amount. Based on the isotopic composition of sampled waters, it has been established that beyond the direct meteoric recharge, the recharging areas are in the North (Mt. Etna) and the South (Hyblean Plateau).

Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, and vegetation indices were tested over the experimental area to estimate both soil and leaf EC using Sentinel-2 imagery on the Google Earth Engine platform. Subsequently, estimation models of soil and leaf EC were calibrated by employing machine learning (ML) techniques using 12 spectral bands of Sentinel-2 images. The prediction accuracy of the EC estimation was assessed by using k-fold cross-validation and computing statistical metrics. The results of the study revealed that machine learning algorithms, together with multispectral data, could advance the mapping and monitoring of soil and leaf electrical conductivity.

Integration of Geophysical Survey Data for the Identification of New Archaeological Remains in the Subsoil of the Akrai Greek Site (Sicily, Italy)

For more than a decade now, geophysical prospecting has been considered an integral part of archaeological research; the ability to quickly investigate large areas and locate objects buried in the ground without directly interacting with it is a key feature that makes such surveys essential for identifying and locating, with good accuracy, buried archaeological remains. In this study, two extensive magnetic and electromagnetic surveys were carried out in two different areas of the Akrai archaeological site, where given the distribution of archaeological remains already found at the site, it was likely that additional buried remains were present. The surveys were performed using a proton precession magnetometer and a multifrequency electromagnetic device with a frequency range of 2.5–250 kHz. By processing the data, the vertical magnetic gradient and electrical conductivity maps were obtained. Furthermore, 3D models of electrical conductivity distribution were reconstructed. On comparing the results, it was notable that many anomalies characterized by low vertical gradient values were identified within areas characterized by low electrical conductivity values. These anomalies detected by both surveys can be associated with good probability with buried archaeological remains made up of limestones. In fact, they exhibit shapes and sizes comparable to those of the wall remains already found at the site.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

SALINITY OF SOIL AND IRRIGATION WATER ON RICE PRODUCTIVITY IN THE CANTON OF SAN JACINTO DE YAGUACHI, ECUADOR

The surface of agricultural soils affected with salinity causes degradation and reduction of crop production, negatively impacting the economy of farmers. The objective of this research was to determine the effect of water quality and soil salinity on rice production in the sub-basin of the Yaguachi River, Ecuador. For this purpose, 99 soil samples were georeferenced and analysed over an area of 19 027 ha, as well as water samples representative of the area under study. Soil pH and electrical conductivity (EC) maps were produced and a regression analysis between EC and rice yield was performed on 34 producer plots. The predominant texture of the soils was found to be clayey, with an average pH of 6.73; The 38.4 % of the samples had an EC between 3.8 and 7.1 dS m-1. Rice yield gradually decreased as it was grown in soils with higher salinity and 26.81 % of the yield loss was found to be attributed to the increase in EC. The salinity of the irrigation water and other quality indicators such as the percentage of soluble sodium (Na+) and the sodium adsorption ratio (RAS) classified the water as severely restricted for use. However, given that crop yields did not show as drastic a reduction as expected, it can be said that the rice plant responds to the conditions of the canton of San Jacinto de Yaguachi as moderately tolerant to salinity.

Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain

The increase in soil salinity due to human-induced processes poses a severe threat to agriculture on a regional and global scale. Soil salinization caused by natural and anthropogenic factors is a vital environmental hazard, specifically in semi-arid and arid regions of the world. The detection and monitoring of salinity are critical to the sustainability of soil management. The current study compared the performance of machine learning models to produce spatial maps of electrical conductivity (EC) (as a proxy for salinity) in an alluvial irrigation plain. The current study area is located in the Isparta province (100 km2), land cover is mainly irrigated, and the dominant soils are Inceptisols, Mollisols, and Vertisols. Digital soil mapping (DSM) methodology was used, referring to the increase in the digital representation of soil formation factors with today’s technological advances. Plant and soil-based indices produced from the Sentinel 2A satellite image, topographic indices derived from the digital elevation model (DEM), and CORINE land cover classes were used as predictors. The support vector regression (SVR) algorithm revealed the best relationships in the study area. Considering the estimates of different algorithms, according to the FAO salinity classification, a minimum of 12.36% and a maximum of 20.19% of the study area can be classified as slightly saline. The low spatial dependence between model residuals limited the success of hybrid methods. The land irrigated cover played a significant role in predicting the current level of EC.

Field-Scale Apparent Electrical Conductivity Mapping of Soil Properties in Precision Agriculture

The physical examination of a functioning cacao farm revealed varying pod production rates in its area. Agricultural soil nutrients assessment is usually through soil geochemical/chemical analyses which are laborious and expensive, necessitating faster/cheaper alternatives. This investigation assessed the physical properties that can substitute for geochemical analysis of soil nutrient. The study was executed at 0.3 m depth. The Volumetric Water Content-VWC and Apparent Electrical Conductivity-ECa of the soils were determined using VG-meter-200 moisture-meter and resistivity earth-meter. 912 ECa/VWC points were measured. Soil textural classes (51-sample) were established using Bouyoucos method. Falling head permeability test was conducted on nine cored soil samples for water infiltration assessment. The soils ECa (10-344 µS/cm) and VWC distributions (2-69%) showed similar variation, increase in VWC corresponds to rise in ECa value; soil moisture aids the mobility of ions in solution and a rise in ECa connotes presence of more dissolved ions. The soils were classified as sandy loam, loamy sand and sandy clayey loam. Soils’ permeability ranged from 4.11x10-5-3.97x10-3 cm/sec; infiltration rate varied inversely with the ECa accounting for the moisture variation. Low permeable soil has high nutrient retention and water-holding capacities. Soil physical properties were effective in evaluating the nutrient inconsistency.

Site-specific nitrogen recommendations’ empirical algorithm for maize crop based on the fusion of soil and vegetation maps

The Oklahoma State University algorithm (OSU) is the most widespread recommendation system to calculate the optimal nitrogen (N) rate in cereal crops. This system is based on the map of a crop vegetation index and N-rich strips in the field, serving as a reference for crop vigour. Based on these inputs, yield potential and in-season crop response to N fertilisation are predicted. The system does not consider other factors influencing the spatial variability of crop growth (e.g., soil), even if the integration of additional data sources could provide more correct N recommendations by considering simultaneously the main drivers of crop growth and production variability. Therefore, the main aims of this study were to modify the OSU algorithm by merging maps of soil electrical conductivity and crop vegetation index (NDRE) to identify management zones instead of using vegetation index alone and to implement and apply the algorithm to define crop response to nitrogen, specific by management zones using a statistical approach instead of N-rich strips in the field. The proposed algorithm was calibrated for maize in northern Italy on four experimental fields in 2021. A comparison among the proposed, the original algorithm and one its previous modification (at Clemson University) was carried out in terms of spatial accuracy and levels of recommended N rates. The proposed algorithm, thanks to the integrated approach, distinguished more in detail different crop responses to N within each management zone and provided N recommendations that match the within-field variability better than the original algorithm and subsequent similar modifications. The new approach resulted in potential average N savings of about 12% compared to uniform management. We conclude that the potential of merging soil and vegetation indices and the definition of N rates with a statistical approach could improve performances and, at the same time, facilitate the adoption of the OSU algorithm. Field validation is needed to confirm the promising results shown in this work.

Fault mapping and characterization of a coastal aquifer related to a mangrove ecosystem, using electrical resistivity tomography (ERT), ground penetrating radar (GPR) and hydrochemical data: The case of the Mangue de Pedra Aquifer, Armação dos Búzios, Brazil

The Mangue de Pedra is a rare and fragile ecosystem. It stands above a sandstone-conglomerate substratum of the Neogene Barreiras Formation, which also hosts the local coastal aquifer, known as the Mangue de Pedra Aquifer. The Pai Vitório Fault, a regional structure, which represents the south border of the São João da Barra Graben, separates the Paleoproterozoic crystalline basement from these sediments and limits the occurrence of both the mangrove and aquifer. Aside from hydrochemical data, geophysical surveys, including electrical resistivity tomography (ERT) and ground penetrating radar (GPR), allowed to investigate the local substrate and the role of fault structures affecting the aquifer-mangrove ecosystem, as well as to provide a better understanding on groundwater circulation and effects of seawater interface dynamics. The hydrochemical data for 11 water samples were used for interpolation to generate pH, Eh and electrical conductivity (EC) maps, and the results revealed a zone of less saline water flow and a discharge zone at the cliff's base. The survey of five ERT lines, using the dipole-dipole configuration, provided a picture of the saturated zone of the aquifer. The interpreted sedimentary units have been divided in three different layers, based on water content and variation of the water quality: 1) unsaturated zone; 2) good/intermediate-quality freshwater (34.4–137 Ω-m); 3) brackish/poor-quality freshwater (4.46–34.4 Ω-m). This method indicated the presence of at least five normal faults, including major and shallow structures along the mangrove, and the basement 5 m below sea level. The radargrams also confirmed the presence of most of these faults, recognized as complex fault zones at shallow levels. The interpretation on orientation of these structures was then enhanced by geoprocessing techniques, indicating a major NE-SW trend. This set of faults provides some of the requirements for the mangrove existence, including: a) the shape and materials of the beach and surrounding landscape, composed by chalcedony breccias blocks which are resistant to erosion, protecting the local environment from impact of sea waves; b) pathways for groundwater flow; and c) traps or barriers within the aquifer, which channel less saline waters to that specific site. This set of factors creates a unique ecosystem, which normally does not occur in beaches from open sea shorelines.

Evaluating spatial and temporal variations in sub-field level crop water demands

Variable rate irrigation (VRI) requires accurate knowledge of crop water demands at the sub-field level. Existing VRI practices commonly use one or more variables like soil electrical conductivity, historical yields, and topographic maps to delineate variable rate zones. However, these data sets do not quantify within season variability in crop water demands. Crop coefficients are widely used to help estimate evapotranspiration (ET) at different stages of a crop’s growth cycle, and past research has shown how remotely sensed data can identify differences in crop coefficients at regional and field levels. However, the amount of spatial and temporal variation in crop coefficients at the sub-field level (i.e. within a single center pivot system) has not been widely researched. This study aims to compare sub-field ET estimates from two remote sensing platforms and quantify spatial and temporal variations in aggregated sub-field level ET. Vegetation indices and reference ET data were collected at Kansas State University’s Southwest Research Extension Center (SWREC) and two Water Technology Farms during the 2020 corn growing season. Weekly maps of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) from aerial imagery are combined with empirical equations from existing literature to estimate both basal and combined crop coefficients at a 1-meter resolution. These ET estimates are aggregated to a 30 m resolution and compared to the Landsat Provisional Actual ET dataset. Finally, actual ET estimates from aerial images were aggregated using k-means clustering and stationary variable speed zones to determine if there is enough variation in actual ET at the sub-field level to build variable rate irrigation schedules. An equivalence test demonstrated that the aerial imagery and Landsat data sources produce significantly different crop coefficient estimates. However, the two datasets were moderately correlated with Pearson’s product-moment correlation coefficients ranging from -0.95 to 0.86. Both the aerial imaging and Landsat datasets showed high variability in crop coefficients during the first 5-6 weeks after emergence, with these coefficients becoming more spatially uniform later in the growing season. These crop coefficients may help irrigators make more informed irrigation management decisions during the growing season. However, more research is needed to validate these remotely sensed ET estimates and integrate them into an irrigation decision support system.