Assessment of SoilGrids data for soil erosion estimation at watershed scale: A case study in northern Thailand

Soil erosion has been identified as one of the most destructive forms of land degradation, posing a threat to the sustainability of global economic, social and environmental systems. This underscores the need for sustainable land management that takes erosion control and prevention into consideration. This requires the use of state-of-the-art erosion prediction models. The models often require extensive input of detailed spatial and temporal data, some of which are not readily available in many developing countries, particularly detailed soil data. The soil dataset Global Gridded Soil Information (SoilGrids) could potentially fill the data gap. Nevertheless, its value and accuracy for soil erosion modelling in the humid tropics is still unknown, necessitating the need to assess its value vis-à-vis field-based data. The major objective of this study was to conduct a comparative assessment of the value of SoilGrids and field-based soil data for estimating soil loss. Soil samples were collected from five physiographic positions (summit, shoulder, back slope, foot slope, and toe slope) using the soil catena approach. Samples were collected using a 5-cm steel sample ring (undisturbed) and a spade (disturbed). Data of the landform, predominant vegetation types, canopy cover, average plant height, land use, soil depth, shear strength, and soil color were recorded for each site. The soil samples were subjected to laboratory analysis for saturated hydraulic conductivity, bulk density, particle size distribution, and organic matter content. Pedotransfer functions were applied on the SoilGrids and field-based data to generate soil hydrological properties. The resultant field-based data were compared with the SoilGrids data for corresponding points/areas to determine the potential similarities of the two datasets. Both datasets were then used as inputs for soil erosion assessment using the revised Morgan-Morgan-Finney model. The results from both datasets were again compared to determine the degree of similarity. The results showed that with respect to point-based comparison, both datasets were significantly different. At the hillslope delineation level, the field-based data still consistently had a greater degree of variability, but the hillslope averages were not significantly different for both datasets. Similar results were recorded with the soil loss parameters generated from both datasets; point-based comparison showed that both datasets were significantly different, whereas the reverse was true for parcel/area-based comparison. SoilGrids data are certainly useful, especially where soil data are lacking; the utility of this dataset is, however, dependent on the scale of operation or the extent of detail required. When detailed, site-specific data are required, SoilGrids may not be a good alternative to soil survey data in the humid tropics. On the other hand, if the average soil properties of a region, area, or land parcel are required for the implementation of a particular project, plan, or program, SoilGrids data can be a very valuable alternative to soil survey data.