Assessing landscape dust emission potential using combined ground‐based measurements and remote sensing data
journal contributionposted on 25.04.2019 by J.R.C. von Holdt, F.D. Eckardt, Matthew Baddock, Giles F.S. Wiggs
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Modeled estimates of aeolian dust emission can vary by an order of magnitude due to the spatiotemporal heterogeneity of emissions. To better constrain location and magnitude of emissions, a surface erodibility factor is typically employed in models. Several landscape-scale schemes representing surface dust-emission potential for use in models have recently been proposed, but validation of such schemes has only been attempted indirectly with medium-resolution remote sensing of mineral aerosol loadings and high-resolution land-surface mapping. In this study, we used dust-emission source points identified in Namibia with Landsat imagery together with field-based dust-emission measurements using a Portable In-situ Wind Erosion Laboratory (PI-SWERL) wind tunnel to assess the performance of schemes aiming to represent erodibility in global dust-cycle modeling. From analyses of the surface and samples taken at the time of wind tunnel testing, a Boosted Regression Tree analysis identified the significant factors controlling erodibility based on PI-SWERL dust flux measurements and various surface characteristics, such as soil moisture, particle size, crusting degree and mineralogy. Despite recent attention to improving the characterisation of surface dust-emission potential, our assessment indicates a high level of variability in the measured fluxes within similar geomorphologic classes. This variability poses challenges to dust modelling attempts based on geomorphology and/or spectral-defined classes. Our approach using high-resolution identification of dust sources to guide ground-based testing of emissivity offers a valuable means to help constrain and validate dust-emission schemes. Detailed determination of the relative strength of factors controlling emission can provide further improvement to regional and global dust-cycle modeling.
This research was funded by the National Research Foundation in South Africa as part of research project number: UID 89120.
- Social Sciences
- Geography and Environment