Spatial soil information in South Africa: Situational analysis, limitations and challenges

Paterson, Garry; Turner, D. D.; Wiese, Liesl; Zijl, George van; Clarke, Catherine E.; Tol, Johan van

doi:10.17159/sajs.2015/20140178

Cited by 40 publications

(21 citation statements)

References 31 publications

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“…The LT database is the only soil dataset that covers the whole of South Africa at a 1:250,000 scale. A land type is an area with relatively homogenous soil forming factors (climate, geology and topography) resulting in relatively homogenous soil distribution patterns [32]. The LT database is currently the best readily available source of hydrological soil information available in South Africa.…”

Section: Soil Informationmentioning

confidence: 99%

Importance of Detailed Soil Information for Hydrological Modelling in an Urbanized Environment

2020

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Soil information is critical in watershed-scale hydrological modelling; however, it is still debated which level of complexity the soil data should contain. In the present study, we have compared the effect of two levels of soil data on the hydrologic simulation of a mesoscale, urbanised watershed (630 km2) in central South Africa. The first level of soil data, land type (LT) data, is currently the best, readily available soil information that covers the whole of South Africa. In the LT database, the entire study area is covered by only two soil types. The second level of soil data (DSM) was created by means of digital soil mapping based on hydropedological principles. It resulted in six different soil types with different hydrological behaviour (e.g., interflow, recharge, responsive). The two levels of soil data were each included in the revised version of the Soil and Water Assessment Tool (SWAT+). To compare the effects of different complexity of soil information on the simulated water balance, the outputs of the uncalibrated models were compared to the three nested gauging stations of the watershed. For the LT scenario, the simulation efficiencies calculated with the Kling–Gupta efficiency (KGE) for the three nested gauging stations (640 km2, 550 km2, 54 km2) of 0, 0.33 and −0.23 were achieved, respectively. Under the DSM scenario, KGE increased to 0.28, 0.44 and 0.43 indicating an immediate improvement of the simulation by integrating soil data with detailed information on hydrological behaviour. In the LT scenario, actual evapotranspiration (aET) was clearly underestimated compared to MODIS-derived aET, while surface runoff was overestimated. The DSM scenario resulted in higher simulated aET compared to LT and lower surface runoff. The higher simulation efficiency of DSM in the smaller headwater catchments can be attributed to the inclusion of the interflow soil type, which covers the governing runoff generation process better than the LT scenario. Our results indicate that simulations benefit from more detailed soil information, especially in smaller areas where fewer runoff generation processes dominate.

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Section: Soil Informationmentioning

confidence: 99%

Importance of Detailed Soil Information for Hydrological Modelling in an Urbanized Environment

2020

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“…Although a large amount of soil investigations has been carried out in South Africa, information access is restricted. Local or provincial soil information is often archived and not freely available to the public due to a lack of a central repository [21]. Long-term erosion monitoring in the Karoo by [22] showed evidence of considerable soil degradation and badlands formation but information on the soils' degradation status and the loss of soil functions and productivity is usually not included in soil maps.…”

Section: Introductionmentioning

confidence: 99%

Soil Degradation Mapping in Drylands Using Unmanned Aerial Vehicle (UAV) Data

2019

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Arid and semi-arid landscapes often show a patchwork of bare and vegetated spaces. Their heterogeneous patterns can be of natural origin, but may also indicate soil degradation. This study investigates the use of unmanned aerial vehicle (UAV) imagery to identify the degradation status of soils, based on the hypothesis that vegetation cover can be used as a proxy for estimating the soils’ health status. To assess the quality of the UAV-derived products, we compare a conventional field-derived map (FM) with two modelled maps based on (i) vegetation cover (RGB map), and (ii) vegetation cover, topographic information, and a flow accumulation analysis (RGB+DEM map). All methods were able to identify areas of soil degradation but differed in the extent of classified soil degradation, with the RGB map classifying the least amount as degraded. The RGB+DEM map classified 12% more as degraded than the FM, due to the wider perspective of the UAV compared to conventional field mapping. Overall, conventional UAVs provide a valuable tool for soil mapping in heterogeneous landscapes where manual field sampling is very time consuming. Additionally, the UAVs’ planform view from a bird’s-eye perspective can overcome the limited view from the surveyors’ (ground-based) vantage point.

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“…Minasny et al., 2013) and DSM in specific geographical regions (e.g. Paterson et al., 2015; Zeraatpisheh et al., 2020). The reviews by Lamichhane et al.…”

Section: Introductionmentioning

confidence: 99%

Perspectives on validation in digital soil mapping of continuous attributes—A review

Piikki

Wetterlind

Söderström

et al. 2021

Soil Use and Management

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We performed a systematic mapping of validation methods used in digital soil mapping (DSM), in order to gain an overview of current practices and make recommendations for future publications on DSM studies. A systematic search and screening procedure, largely following the RepOrting standards for Systematic Evidence Syntheses (ROSES) protocol, was carried out. It yielded a database of 188 peer‐reviewed DSM studies from the past two decades, all written in English and all presenting a raster map of a continuous soil property. Review of the full‐texts showed that most publications (97%) included some type of map validation, while just over one‐third (35%) estimated map uncertainty. Most commonly, a combination of multiple (existing) soil sampe datasets was used and the resulting maps were validated by single data‐splitting or cross‐validation. It was common for essential information to be lacking in method descriptions. This is unfortunate, as lack of information on sampling design (missing in 25% of 188 studies) and sample support (missing in 45% of 188 studies) makes it difficult to interpret what derived validation metrics represent, compromising their usefulness. Therefore, we present a list of method details that should be provided in DSM studies. We also provide a detailed summary of the 28 validation metrics used in published DSM studies, how to interpret the values obtained and whether the metrics can be compared between datasets or soil attributes.

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Spatial soil information in South Africa: Situational analysis, limitations and challenges

Cited by 40 publications

References 31 publications

Importance of Detailed Soil Information for Hydrological Modelling in an Urbanized Environment

Importance of Detailed Soil Information for Hydrological Modelling in an Urbanized Environment

Soil Degradation Mapping in Drylands Using Unmanned Aerial Vehicle (UAV) Data

Perspectives on validation in digital soil mapping of continuous attributes—A review

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