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

Patrick Filippi; Brett M. Whelan; Thomas F. A. Bishop

doi:10.1071/SR23112

RESEARCH ARTICLE (Open Access)

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

Patrick Filippi

^A ^* , Brett M. Whelan ^A and Thomas F. A. Bishop

^A

+ Author Affiliations

- Author Affiliations

^A Precision Agriculture Laboratory, Sydney Institute of Agriculture, School of Life and Environmental Science, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia.

^* Correspondence to: patrick.filippi@sydney.edu.au

Handling Editor: Abdul Mouazen

Soil Research 62, SR23112 https://doi.org/10.1071/SR23112

Submitted: 29 June 2023 Accepted: 14 December 2023 Published: 16 February 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

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.

Keywords: broadacre cropping, digital soil mapping, precision agriculture, proximal sensing, remote sensing, soil constraints, soil spatial variability.

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