Abstract
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 spectral features. Here we take advantage of the petabyte-scale Landsat datasets covering the same areas for periods exceeding 30 years combined with a novel high-dimensional statistical technique to extract a noise-reduced, cloud-free, and robust estimate of the spectral response of the barest state (i.e. least vegetated) across the whole continent of Australia at 25 m2 resolution. Importantly, our method preserves the spectral relationships between different wavelengths of the spectra. This means that our freely available continental-scale product can be combined with machine learning for enhanced geological mapping, mineral exploration, digital soil mapping, and establishing environmental baselines for understanding and responding to food security, climate change, environmental degradation, water scarcity, and threatened biodiversity.
Highlights
Multi-spectral remote sensing has already played an important role in mapping surface mineralogy
Scaling methods to a continental archive of data presents a major challenge in the analysis of big datasets called heterogeneity whereby outliers and various states are no longer sparse but become proper sub-populations in the data that are difficult to disentangle[16]. We tackle these technical issues and provide the first continental-scale mosaic of Australia at its barest state using the full temporal archive of Landsat observations. We achieve this by proposing a statistical estimator of the barest spectra that is both robust to contamination and, most importantly, correctly maintains the relationship between all the spectral wavelengths enabling the application of machine learning and spatial statistics to further separate vegetation and mineral spectra[17,18]
The approach starts by considering ~16 billion 6-dimensional time series across the continent of Australia where each time series represents the variation of the BLUE, GREEN, RED, NIR, SWIR1 and SWIR2 wavelength bands in a 250-m2 pixel
Summary
Multi-spectral remote sensing has already played an important role in mapping surface mineralogy. Some studies have attempted to exploit the full time series archive of Landsat observations, collected over the last 30 years, to produce per-pixel mosaics of the barest earth (i.e. exposed soil)[14,15] Their approaches were only attempted in small geographic areas and their methodologies are based on user-defined thresholds and data-mining techniques that are unlikely to be sufficiently complex and scalable to correctly remove all the non-bare responses in the observations. We tackle these technical issues and provide the first continental-scale mosaic of Australia at its barest state using the full temporal archive of Landsat observations We achieve this by proposing a statistical estimator of the barest spectra that is both robust to contamination (such as cloud cover, shadows, detector saturation and pixel corruption) and, most importantly, correctly maintains the relationship between all the spectral wavelengths enabling the application of machine learning and spatial statistics to further separate vegetation and mineral spectra[17,18]. The result of our approach is the generation of two continental-scale composite Barest Earth mosaics of Australia, one of shorter temporal depth using only Landsat 8 observations and the other using the full 30 + year archive combining Landsat 5, 7 and 8
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