Depth to sodicity constraint mapping of the Murray-Darling Basin, Australia

Pozza, Liana; Filippi, Patrick; Whelan, Brett; Wimalathunge, N.S.; Jones, E.V.; Bishop, Thomas

doi:10.1016/j.geoderma.2022.116181

Cited by 4 publications

(4 citation statements)

References 51 publications

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“…Airborne gamma is commonly used in many Australian DSM studies at the national (Viscarra Rossel et al 2015), regional (Pozza et al 2022), and farm level (Filippi et al 2019b). This gamma data has proven useful in modelling the variation in several soil properties.…”

Section: Discussionmentioning

confidence: 99%

“…While many DSM studies have used satellite imagery as a covariate, there can be considerable differences in the type of imagery and the processing implemented. For example, some studies use a single-day remotely-sensed satellite image (Mirzaee et al 2016), whereas others use multitemporal satellite images (Pozza et al 2022). Many things need to be considered when processing satellite data for use as a covariate.…”

Section: Discussionmentioning

confidence: 99%

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

Filippi,

Whelan,

Bishop

2024

Soil Res.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Filippi,

Whelan,

Bishop

2024

Soil Res.

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“…A previous study by Pozza [43] carried out an analysis on soil constraint depth, specifically the depth of soil sodicity. The study predicted the depth to soil sodicity in the Murray-Darling Basin, Australia, by modeling ESP using several covariates in a machine learning model (random forest).…”

Section: Alternative Approachesmentioning

confidence: 99%

“…Different percentiles of NDVI values were utilized as one of the covariates to predict depth and soil sodicity constraints. Pozza [43] also eliminated seven other important covariates for the prediction, including rainfall, temperature, elevation, slope, radiometric potassium, soil type, and mid-depth. The result reported a moderate quality prediction with an r 2 of 0.49 and Lin's CCC (Concordance Correlation Coefficient) of 0.66.…”

Section: Alternative Approachesmentioning

confidence: 99%

A Study of the Relationships between Depths of Soil Constraints and Remote Sensing Data from Different Stages of the Growing Season

et al. 2023

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The presence of salinity and sodicity in the root zone can limit root development and impact crop yield. Topsoil constraints are likely to have the greatest impact on crop growth early in the growing season, when plant roots are still shallow. Later in the growing season, subsoil constraints may have a greater impact as roots reach deeper into the soil. This study investigated whether different patterns of spatial variation in crop growth would be evident in remote sensing data captured from different stages of the growing season, with the aim of providing a means of indicating whether soil constraints in the topsoil and in the subsoil might be impacting crop growth. If a topsoil constraint is impacting growth, we might expect its effects to show through a negative correlation between the soil constraint and the early-season vegetation index. However, we would not expect to observe the impact of a subsoil constraint until later in the season (when roots have reached the constraint). To test the results from the analysis of remote sensing data, we used soil data from five fields from across Australia’s northern grains-growing region. We used these data to assess soil constraint severity and correlations between the soil constraints and enhanced vegetation index (EVI). The results of the study were inconclusive, and it was difficult to identify a dominant soil constraint with a clear relationship to crop growth. The soil data were also insufficient to draw conclusions about the depths of any dominant soil constraints. Furthermore, there was a lot of subjectivity in the interpretations of the correlations between remote sensing and soil data. The study also investigated the consistency of the spatial variation in EVI over multiple years, but the results were still inconclusive. In conclusion, this study highlights the challenges of using remote sensing data to diagnose soil constraints in agricultural settings. While remote sensing can provide useful insights into crop growth, interpreting these data and drawing meaningful conclusions about soil constraints requires further research and development.

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