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Taking 19 typical coal mining areas in Anhui Province as the research object, this study uses AHSI hyperspectral data from the Gaofen-5 02 satellite to conduct remote sensing monitoring of methane column concentration using a matched filtering algorithm, analyzes the spatial distribution of methane emissions, and provides data support and scientific basis for the dynamic monitoring of regional methane emission sources.

The 2026 Shenzhen Commercial Aerospace Industry Development Conference was held, and CAS Xiguang Aerospace was invited to participate and deliver a keynote speech, showcasing the technological achievements of Xiguang's series of hyperspectral satellites. Relying on its full-chain independent research and development capabilities, the company has implemented remote sensing applications in multiple industries such as precision agriculture, environmental monitoring, and mineral surveys, contributing to the high-quality development of the commercial aerospace industry.

On March 16, 2026, the Xiguang-1 06 satellite (Dafo satellite) was successfully launched. Developed entirely independently by CAS Xiguang, it is the only in-service full-spectrum commercial remote sensing satellite in China. It empowers the ecological governance of agriculture, forestry, and mining with quantitative hyperspectral technology, fulfills its positioning as an emerging pillar industry of commercial aerospace, and connects with the Leshan Giant Buddha to convey the warmth of science and technology and humanism.

Under the background of the 15th Five-Year Plan, CAS Xiguang Aerospace promotes the deep integration of commercial aerospace and artificial intelligence, builds a hyperspectral intelligent computing constellation, and realizes the leap from "space sensing and ground computing" to "space data and space computing". With on-board computing power + AI big model, it empowers the high-quality development of industries such as agriculture, forestry, mining and water, and ecological emergency response.

Precisely pinpointing tectonic intersection nodes accelerates the delineation of mineralized target areas.

Shanghai New International Expo Centre: Focusing on the trillion-dollar new blue ocean of aerospace!

Focusing on the development of the space computing power industry, Taibo Think Tank officially launched the "Panoramic Map of the Space Computing Power Industry Chain (2026)". Based on industry insights and in-depth research, this study clearly outlines the landscape of the space computing power industry by breaking down the core links and key participants in the upstream, midstream and downstream sectors.

Chlorophyll a is the main photosynthetic pigment in phytoplankton and is often used as an indicator of phytoplankton biomass. Chlorophyll a concentration (Chla) is a key indicator for assessing eutrophication levels and studying carbon cycling in aquatic environments. Compared to traditional surface water sampling methods that can only measure a limited number of points, remote sensing technology can provide a wider range of spatial distribution information for chlorophyll a.

On March 5, the Fourth Session of the 14th National People's Congress opened at the Great Hall of the People, where Premier Li Qiang delivered the government work report. This report, which concerns the economic trajectory up to 2026 and even the entire 15th Five-Year Plan period, sent a powerful signal to China's aerospace industry.

In recent years, with the breakthroughs in visual foundational models in the field of natural images, a question has gradually become clear: can hyperspectral remote sensing also build its own "foundational model"? Unlike RGB images, hyperspectral imagery typically contains hundreds of consecutive bands, possessing stronger physical meaning, higher dimensional redundancy, and scarcer labeled data. Traditional methods often rely on small-scale datasets for supervised training, resulting in limited model generalization ability and difficulty in cross-regional and cross-sensor transfer. Therefore, the hyperspectral field urgently needs a unified modeling paradigm capable of pre-training using massive amounts of unlabeled data.