Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Abstract

Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (ECa – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.

Identifying Potential Leakage Zones in an Irrigation Supply Channel by Mapping Soil Properties Using Electromagnetic Induction, Inversion Modelling and a Support Vector Machine

“…Unfortunately, extensive soil characterisation is challenging, owing to time and the cost-prohibitive nature of sampling and analysis. Increasingly, electromagnetic (EM) induction instruments have proven to be a good source of digital data to create digital soil maps (DSM), because the measured apparent electrical conductivity (EC a -mS m −1 ) is related to clay [6] and mineralogy [7], as well as CEC [8] and salinity [9].…”

Digital mapping of soil salinity at various depths using an EM38

“…It has been shown that the use of electromagnetic (EM) induction instruments can be used as ancillary data to rapidly map soil properties relevant to salinity. This is because EM instruments measure the apparent soil electrical conductivity (EC a ), which is a function of properties such as clay (Saey, Simpson, Vermeersch, Cockx, & Van Meirvenne, 2009), mineralogy (Nagra, Burkett, Huang, Ward, & Triantafilis, 2017) and moisture (Brevik, Fenton, & Lazari, 2006). More importantly, when these properties are uniform across a study site (Friedman, 2005), the shallow measuring EM38 instrument has been used to first enable site selection for calibration (Triantafilis, Laslett, & Mcbratney, 2000) and subsequently for mapping salinity at field (Li, Shi, Webster, & Triantafilis, 2013), across farms (Buchanan & Triantafilis, 2009) and landscapes (Odeh, Todd, Triantafilis, & McBratney, 1998).…”