Abstract
Digital soil modelling and mapping is reliant on the availability and utility of easily derived and accessible covariates. In this paper, the value of covariates derived from a time-series of remotely-sensed ASTER satellite imagery and digital elevation models were evaluated for modelling two soil attributes — soil depth and watertable depth. Modelling was performed at two resolutions: a fine resolution (15m pixels) that relates to the resolution of the ASTER Visible-NIR bands, and a larger resolution (90m pixels) that relates to the resolution of the thermal bands of the ASTER imagery. Upscaling to a larger pixel size and downscaling to a smaller pixel size were performed to adjust the covariates where necessary. A regression tree approach was used to model soil depth and watertable depth, recorded as a binary ‘deep’ or ‘shallow’ response, using the ASTER imagery-derived covariates and digital terrain attributes (DTAs). Modelling was performed at a single spatial resolution (15 or 90m pixels) using the imagery-derived covariates only, the DTAs only, or a mixture of both. A multi-resolution model was also generated, by using both imagery-derived covariates and DTAs at both resolutions. When mixed with the DTAs, the imagery-derived covariates helped explain the uncertainty (variance) in the soil depth data but not in the watertable depth. The ASTER-derived downscaled evapotranspiration-based covariates were of particular significance in the soil depth modelling. Watertable depth was best explained by models that used DTAs at a smaller pixel size. Information on vegetative growth was neither superior nor complementary to information on terrain for modelling watertable depth. Using a multi-resolution model significantly improved the modelling of soil depth but not of watertable depth. The effect of covariate and modelling resolution on model performance is discussed within the context of the GlobalSoilMap.net project.
Highlights
The prediction of subsoil attributes is generally more difficult than topsoil attributes because many of the environmental covariates used in digital soil modelling and mapping (DSMM) only generate a topsoil or surface response (e.g. Visible and Near-infrared (Vis-NIR) imagery and gamma radiometry)
The intention of the analysis is to investigate the utility of both ASTER imagery-derived covariates and digital terrain attributes (DTAs)-derived covariates in modelling soil and watertable depth, and to investigate if there is an effect of pixel size in the modelling
The terrain maps show similar patterns between DEM10 and DEM80 covariates with much greater detail in the DEM10 covariates e.g. Curve10 vs. Curve80
Summary
The prediction of subsoil attributes is generally more difficult than topsoil attributes because many of the environmental covariates used in digital soil modelling and mapping (DSMM) only generate a topsoil or surface response (e.g. Visible and Near-infrared (Vis-NIR) imagery and gamma radiometry). There are several possible approaches to modelling and mapping soil depth and watertable depth. Several geophysical sensors (e.g. EMI, GPR) are able to generate subsoil responses that can be related to soil depth under certain conditions (Bramley et al, 2000). Such sensing technologies have been used for subsoil modelling/ mapping, either with or without terrain information (see Grunwald, 2009 for examples). To the authors' knowledge, this has not been done for soil depth or watertable depth Another alternative, and a focus of this work, is to incorporate a vegetation–soil inference system into the modelling/mapping. It should be possible to infer subsoil information from the plant response, in monocultures
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