Assessing Landscape Dust Emission Potential Using Combined Ground-Based Measurements and Remote Sensing Data

Modeled estimates of eolian 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 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 Portable In-situ Wind Erosion Laboratory dust flux measurements and various surface characteristics, such as soil moisture, particle size, crusting degree, and mineralogy. Despite recent attention to improving the characterization 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 modeling 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. Plain Language Summary Atmospheric mineral dust plays an important role in Earth system processes, influencing climate, providing nutrients to ecosystems, and affecting human health. The effect that atmospheric dust has on the climate and environment requires accurate modeling of emission at source, transport through the atmosphere, and deposition. To enable regional to global modeling of the dust cycle, therefore, requires realistic representation of where and when dust emission takes place. However, the highly variable nature of dust emission has resulted in modeling attempts producing disparate results. This research used Landsat remote sensing data in Namibia to identify sources of dust emission at high resolution, followed by ground-based testing using a portable wind tunnel to assess surface classification schemes intended to represent the surface in dust emission models. Despite the proposed schemes offering valuable approaches for characterization of the land surface for modeling, globally applicable representation of dust emission is still hampered by the variability of small-scale emissions. At the sublandform level of our analysis, the heterogeneous nature of dust emission results from the highly variable nature of the surfaces. Our analysis identified several factors controlling the potential for surfaces to emit dust that can be used as inputs to improve dust modeling.