Toward high‐resolution agronomic soil information and management zones delineated by ground‐based electromagnetic induction and aerial drone data

Abstract

Detailed knowledge of the intra-field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture.We present an approach that combines ground-based agrogeophysical soil and aerial crop data to delineate field-specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato (Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone-based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI-NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field-scale EMI data included spatial soil information of the upper 0-50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI-NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high-resolution soil information is essential for variable rate applications and agronomic modeling.

Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

“…Frequency-domain electromagnetic induction (FD-EMI) data collected by loop-loop sensors are widely used to investigate near-surface electrical properties, in particular electrical conductivity σ. Typical fields of application include archeological prospection (De Smedt et al 2014;Guillemoteau et al 2019;Kristiansen et al 2022), precision agriculture (Jadoon et al 2015;Rudolph et al 2016;Brogi 5 et al 2019;von Hebel et al 2021), hydrological studies (Vereecken et al 2015;von Hebel et al 2014;Martini et al 2017;Paepen et al 2020), mapping subsurface utilities (Guillemoteau & Tronicke 2015;Thiesson et al 2018;Couchman & Everett 2022), and exploring peat deposits (Altdorff et al 2016;Guillemoteau et al 2017;Beucher et al 2020;Clément et al 2020;McLachlan et al 2020;Klose et al 2022). Inversion of FD-EMI data is a typical example of a ill-posed problem, entailing the need…”

Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness

“…Portable electromagnetic induction (EMI) sensors using harmonic source waveforms (also known as frequency-domain electromagnetics -FDEM) are commonly used to characterize near-surface geoelectrical properties. Such methods are used in various applications including archaeological prospection (De Smedt 5 et al, 2014;Dabas et al, 2016;von Hebel et al, 2021), precision agriculture (Jadoon et al, 2015;Rudolph et al, 2016;Brogi et al, 2019), hydrological studies (Vereecken et al, 2015;von Hebel et al, 2014;Rezaei et al, 2016;Robinet et al, 2018), and environmental studies including the exploration of peat deposits (Altdorff et al, 2016;Beucher et al, 2020;Clément et al, 2020;10 McLachlan et al, 2020). Modern single-frequency, multi-configuration sensors can simultaneously sense the subsurface electrical conductivity for different volumes of investigation.…”

Lasting Effects of Soil Compaction on Soil Water Regime Confirmed by Geoelectrical Monitoring

“…(2020) demonstrated the potential of electromagnetic induction (EMI) and DC‐resistivity methods to monitor the impact of agricultural practices in terms of soil compaction and von Hebel et al. (2020) used EMI and drone‐based multispectral methods to delineate agricultural management zones at larger scales. Despite the accumulated wealth of studies relating soil electrical properties to various soil properties and states, providing a physically based description of how soil structure impacts electrical resistivity remains an open question.…”