Orbital imaging spectroscopy of Earth’s mineral surface: Results and stability from EMIT’s first two years

NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer, operating on the International Space Station, has produced the first hectare-scale mineral maps of Earth’s arid regions. This study assesses the temporal consistency of these maps and tests for changes in surface mineralogy over the first two years of data collection. We fit a discrete-state Hidden Markov Model (HMM) to time series data from 39 globally distributed study sites to quantify measurement noise and distinguish sensor artifacts from real mineralogical changes. Our findings indicate that mineral classifications are highly repeatable, showing 80%–98% consistency depending on the specific mineral. We then use the HMM framework to test for transitions in surface mineralogy. This research provides a principled statistical basis for interpreting EMIT mineral products and combining noisy remote time series observations into unified mineral maps.

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Publication Details

Journal
Remote Sensing of Environment
Published
2026-08-17
DOI
https://doi.org/10.1016/j.rse.2026.115605
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

Orbital imaging spectroscopy of Earth’s mineral surface: Results and stability from EMIT’s first two years

David Ray Thompson, R. N. Clark, Gregory S. Okin, Philip G. Brodrick et al.
Remote Sensing of Environment
Geochemistry and Geologic Mapping
article

Orbital imaging spectroscopy of Earth’s mineral surface: Results and stability from EMIT’s first two years

David Ray Thompson, R. N. Clark, Gregory S. Okin, Philip G. Brodrick, Robert O. Green, Marie-Stephane Guillaumont
article en

Abstract

NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) imaging spectrometer, operating on the International Space Station, has produced the first hectare-scale mineral maps of Earth’s arid regions. This study assesses the temporal consistency of these maps and tests for changes in surface mineralogy over the first two years of data collection. We fit a discrete-state Hidden Markov Model (HMM) to time series data from 39 globally distributed study sites to quantify measurement noise and distinguish sensor artifacts from real mineralogical changes. Our findings indicate that mineral classifications are highly repeatable, showing 80%–98% consistency depending on the specific mineral. We then use the HMM framework to test for transitions in surface mineralogy. This research provides a principled statistical basis for interpreting EMIT mineral products and combining noisy remote time series observations into unified mineral maps.

Remote Sensing of EnvironmentVol. 346
Centre National de la Recherche Scientifique (FR), Planetary Science Institute (US), Jet Propulsion Laboratory (US), University of California, Los Angeles (US), University of Clermont Auvergne (FR), Sigma Clermont (FR), Clermont Université (FR)
Openalex Percentile: Top 7%
Geochemistry and Geologic Mapping
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