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Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km 2) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results. [ABSTRACT FROM AUTHOR]

Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. Digital terrain analysis has been suggested as a way forward to model soil moisture variability across landscapes, and multiple digital terrain indices have been developed. However, the performance of these terrain indices depends on the resolution of the digital elevation models used and, in many cases, user-defined index-specific thresholds. In this study, we compared soil moisture predictions using nine different terrain indices and available soil wetness maps, at varying resolutions and user-defined thresholds, with a field dataset of soil moisture registered in five classes from a forest survey covering a boreal landscape. We found that topography could explain the spatial variation in soil moisture conditions but the effects of changing DEM resolution and user-defined thresholds severely affected the performance of the soil moisture modelling. These results demonstrate that modelled soil moisture conditions need to be validated, as selecting unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results. Challenges caused by heterogeneous soil types within the study area highlight the need for local knowledge when interpreting the modelled results. [ABSTRACT FROM AUTHOR]