Exposed soil and mineral map of the Australian continent revealing the land at its barest.

摘要: Multi-spectral remote sensing has already played an important role in mapping surface mineralogy. However, vegetation – even when relatively sparse either covers the underlying substrate or modifies its spectral response, making it difficult to resolve diagnostic mineral features. Here we take advantage of petabyte-scale Landsat datasets covering same areas for periods exceeding 30 years combined with a novel high-dimensional statistical technique extract noise-reduced, cloud-free, and robust estimate response barest state (i.e. least vegetated) across whole continent Australia at 25 m2 resolution. Importantly, our method preserves relationships between different wavelengths spectra. This means that freely available continental-scale product can be machine learning enhanced geological mapping, exploration, digital soil establishing environmental baselines understanding responding food security, climate change, degradation, water scarcity, threatened biodiversity. In this study, authors combine images spanning new estimator produce spectra map Australian largely unobscured by clouds.