Large-scale soil mapping using multi-configuration EMI and supervised image classification

Abstract

Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1-2 m depth) is responsible for the storage of moisture and nutrients that are accessible to crops. This can potentially be achieved with a combination of direct sampling and Electromagnetic Induction (EMI) measurements, which have shown great potential for soil characterization due to their noninvasive nature and high mobility. However, only a few studies have used EMI beyond the field scale because of the challenges associated with a consistent interpretation of EMI data from multiple fields and acquisition days. In this study, we performed a detailed EMI survey of an area of 1 km 2 divided in 51 agricultural fields where previous studies showed a clear connection between crop performance and soil properties. In total, nine apparent electrical conductivity (ECa) values were measured at each location with a depth of investigation ranging between 0-0.2 to 0-2.7 m. Based on the combination of ECa maps and available soil maps, an a priori interpretation was performed and four sub-areas with characteristic sediments and ECa were identified. Then, a supervised classification methodology was used to divide the ECa maps into areas with similar soil properties. In a next step, soil profile descriptions to a depth of 2 m were obtained at 100 sampling locations and 552 samples were analyzed for textural characteristics. The combination of the classified map and ground truth data resulted in a 1 m resolution soil map with eighteen units with a typical soil profile and texture information. It was found that the soil profile descriptions and texture of the EMI-based soil classes were significantly different when compared using a two-tailed t-test. Moreover, the high-resolution soil map corresponded well with patterns in crop health obtained from satellite imagery. It was concluded that this novel EMI 3 data processing approach provides a reliable and cost-effective tool to obtain high-resolution soil maps to support precision agriculture and agro-ecological modelling.

Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information

“…The dominant reference soil groups are Cambisols, Luvisols, Planosols, and Stagnosols (WRB, 2015). Previous research in this area showed that crop performance during long periods of water scarcity is strongly influenced by soil heterogeneity at the field scale and beyond (Brogi et al, 2019;Rudolph et al, 2015;Stadler et al, 2015;von Hebel et al, 2018). The study area is part of the Terrestrial Environmental Observatories (TERENO) network (Bogena et al, 2018;Schmidt, Reichenau, Fiener, & Schneider, 2012;Simmer et al, 2015).…”

Calibration, Conversion, and Quantitative Multi-Layer Inversion of Multi-Coil Rigid-Boom Electromagnetic Induction Data

“…In precision agriculture, σ a patterns help to optimize fertilizer and irrigation application [6,7], to investigate long-term treatment effects [8], to investigate plant and soil interactions [9,10,11,12], and to test the ability of plants to grow in saline soil conditions [13]. The spatial distribution of σ a can also be used to identify buried cables [14], flood embankments [15], and features within morphological, geological, and geoarchaelogical units [16,17,18]. Recently, Heil et al [19] provided a comprehensive overview of σ a mapping applications focusing on the two-coil EM38 (Geonics Ltd., Mississauga, Canada) system.…”

Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture

“…In both cases (2D and 3D models), the obtained data can be managed for calculating several parameters, such as water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant [18]. Moreover, the images of the soil can be processed to obtain several parameters related to soil water management (such as soil moisture and detection of fertilizers) [19].…”