Abstract
Camera-based soil surface change measurement is a cost-efficient and non-invasive approach to assess soil erosion. A challenging aspect in this context is the obscuring of the sediment yield by subsidence phenomenon such as soil consolidation and compaction in the beginning of a rainfall event (masking effect). Based on the camera elevation changes and measured field observations, we develop an approach to estimate these masking effects and to approximate a correction function. We therefore conduct ten rainfall simulations (3 m x 1 m) on different agricultural slopes, measuring runoff and sediment concentration. With a time-lapse camera system, we generate high resolution digital elevation models every 20 s. An s-shaped curve is fitted via non-linear regression for every rainfall simulation. We use the variables of these functions as well as a combination of the different field observations – bulk density, soil moisture, grain size distribution, total organic carbon, slope steepness, surface cover and surface roughness – as input values for an adjustment. We are able to estimate the masking effects at the beginning of rainfall events as functions of soil and plot characteristics and therefore offer a potential to increase the informative value of camera-based soil erosion measurements on agricultural fields.
Published Version
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