Proximal and remote sensing - what makes the best farm digital soil maps?

Context Digital soil maps (DSM) across large areas have an inability to capture soil variation at within-fields despite being at fine spatial resolutions. In addition, creating field-extent soil maps is relatively rare, largely due to cost.Aims To overcome these limitations by creating soil maps across multiple fields/farms and assessing the value of different remote sensing (RS) and on-the-go proximal (PS) datasets to do this.Methods The value of different RS and on-the-go PS data was tested individually, and in combination for mapping three different topsoil and subsoil properties (organic carbon, clay, and pH) for three cropping farms across Australia using DSM techniques.Key results Using both PS and RS data layers created the best predictions. Using RS data only generally led to better predictions than PS data only, likely because soil variation is driven by a number of factors, and there is a larger suite of RS variables that represent these. Despite this, PS gamma radiometrics potassium was the most widely used variable in the PS and RS scenario. The RS variables based on satellite imagery (NDVI and bare earth) were important predictors for many models, demonstrating that imagery of crops and bare soil represent variation in soil well.Conclusions The results demonstrate the value of combining both PS and RS data layers together to map agronomically important topsoil and subsoil properties at fine spatial resolutions across diverse cropping farms.Implications Growers that invest in implementing this could then use these products to inform important decisions regarding management of soil and crops. Creating maps at the farm-scale is a promising approach to for an accurate understanding of true spatial variation for a range of agronomically important soil attributes. This study assessed the value of different proximal and remote sensing datasets to map topsoil and subsoil carbon, clay content, and pH across three different farms in Australia. Results showed that using a combination of remote and proximal sensing data resulted in the best models, followed by remote only, and then proximal only.