Advances in Data Processing and Geoscience Applications of the Geology-1 Hyperspectral Satellite

Dear Colleagues,

The successful launch of Geology-1 on May 17, 2025, marks a new milestone in China’s hyperspectral Earth-observation program. As the nation’s first hyperspectral microsatellite for geological resources and environmental monitoring, Geology-1 demonstrates the potential of compact satellite design for high-precision spectroscopy. The 83 kg satellite integrates visible-to-near-infrared (VNIR, 410–1000 nm, 14 m, 16 bands) and shortwave-infrared (SWIR, 1000–2480 nm, 30 m, 10 bands) sensors, enabling detailed characterization of water, vegetation, soil, minerals, and lithology.

The initial public release of Geology-1 imagery (http://geosat.com.cn/) provides unprecedented access to national hyperspectral data, supporting innovative research in geological mapping, mineral exploration, environmental assessment, and geohazard monitoring. With its broad spectral range, moderate spatial resolution, and agile microsatellite architecture, Geology-1 offers new opportunities for data processing, spectral analysis, and multi-source fusion. This Special Issue aims to highlight recent advances and applications derived from Geology-1 hyperspectral observations, fostering scientific exchange and promoting the integration of hyperspectral technologies into geoscience and sustainable resource management.

This Special Issue aims to present recent progress in hyperspectral data processing, analysis, and geoscience applications enabled by the Geology-1 satellite: China’s first hyperspectral microsatellite for geological and environmental observation. It focuses on advances in spectral calibration, feature extraction, mineral mapping, and multi-source data fusion, promoting new insights into Earth surface processes. This topic fits squarely within the Remote Sensing journal’s scope, emphasizing the development and application of remote sensing technologies for Earth observation, environmental monitoring, and geological exploration.

Articles may address, but are not limited to, the following topics:

• Hyperspectral data preprocessing, calibration, and atmospheric correction for Geology-1;
• Spectral feature extraction, unmixing, and dimensionality reduction methods;
• Multi-source data fusion of hyperspectral, SAR, optical, and DEM datasets;
• Mineral and lithological mapping using Geology-1 imagery;
• Hyperspectral applications in geological hazard and environmental monitoring;
• Deep learning and AI methods for hyperspectral information extraction;
• Cloud-based or automated processing chains for large-scale hyperspectral data;
• Integration of Geology-1 data with other Earth-observation missions for geoscientific analysis;
• Evaluation of mine waste, tailings, and acid mine drainage (AMD) using SWIR spectra;
• Super-resolution, fusion, and dimensionality reduction methods tailored for VNIR-SWIR integration;
• Quantitative inversion of soil properties (e.g., organic carbon and heavy metals) in mining landscapes.

Dr. Yuewei Wang
Dr. Wei Han
Prof. Dr. Yi Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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