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There are three main methods for atmospheric CH4 detection: ground-based, airborne, and satellite-based. Ground-based detection was the earliest to develop and has now formed an observation system represented by the Total Carbon Column Observation Network (TCCON) and the National Disturbance and Change in Atmospheric Composition Detection Network (NDACC). Airborne detection has also seen the development of a series of detectors, such as the Airborne Laser Infrared Absorption Spectrometer (ALIAS) and the second-generation ALIAS (ALIAS-II), the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic), and the next-generation AVIRIS (AVIRIS-NG). While ground-based and airborne observations offer high precision, their spatial coverage is limited, restricting their application to localized observations. Satellite observation, unaffected by numerous natural conditions, enables continuous, stable, and high-precision observations globally, and provides verification and support for bottom-up emission inventories through a top-down approach.

With the development of remote sensing technology, agricultural monitoring and management are gradually moving towards intelligence and precision. Using time-series remote sensing imagery to determine agricultural activities and classify crops in specific plots has become an important tool in modern agriculture.

Human activities since the First Industrial Revolution have more than doubled the concentration of methane in the atmosphere. While methane emissions account for 3% of total greenhouse gas emissions by mass, their radiative forcing (or climate forcing) accounts for 23% of total radiation. The 2023 Methane Emissions Report released by GHGSat shows that by the end of 2023, global methane emissions reached approximately 378 MtCO2e, double the amount emitted in 2022. Currently, methane is the second largest greenhouse gas after carbon dioxide and is receiving significant attention in all major economies worldwide.

The China-Belarus Great Stone Industrial Park (hereinafter referred to as the China-Belarus Industrial Park) is located near Minsk, the capital of Belarus, and is a joint venture between China and Belarus. The Chinese shareholders are China National Machinery Industry Corporation (SINOMACH) and Harbin Investment Group Co., Ltd., holding 60% of the shares; the Belarusian shareholders are the Minsk Regional Government, the Minsk Municipal Government, and Belarusian Horizon Holding Group, holding 40% of the shares. The industrial park is conveniently located near Minsk International Airport, with a total land area of ​​91.5 square kilometers, and the first phase of the project covers an area of ​​8.5 square kilometers.

With urbanization and economic growth, water pollution has become a key factor restricting sustainable urban development. Online water quality monitoring, characterized by automation, flexibility, speed, real-time detection, and in-situ analysis, has become a demand and development trend in urban water quality monitoring. Hyperspectral imagers, capable of acquiring continuous spectral information of target objects, are used in fields such as environmental monitoring. They are advanced remote sensing devices with increasingly widespread applications in environmental monitoring.

Monitoring the water environment of rivers and lakes has always been a highly valued issue for humankind. Compared with traditional monitoring methods, satellite remote sensing technology has the characteristics of being macroscopic, efficient, and economical, and has significant advantages in obtaining long-term, large-scale spatiotemporal changes in the water environment of rivers and lakes, and has been widely used in many fields.

Hyperspectral technology, while imaging the spatial features of a target object, disperses each spatial pixel to form dozens or even hundreds of narrow bands for continuous spectral coverage. The resulting spectral data can be visualized using an image cube, where two dimensions represent space and the other represents the spectrum. In this three-dimensional space integrating spectral and spatial information, "continuous" spectra and diagnostic feature spectra of ground features can be arbitrarily obtained. This allows for direct identification of target features based on spectral knowledge and further quantitative information about the features. In geological applications, mineral identification and information processing technologies can be categorized into three main types: characteristic parameters of single diagnostic absorption, complete waveform features, and spectral knowledge models.

Western Egypt's deserts are arid, but the eastern Sahara is even more extreme, receiving only a few centimeters of rainfall annually, earning it the name "ultra-arid." However, during the last Ice Age, the Sahara was a tropical savanna with a climate similar to modern-day Kenya and Tanzania. Annual rainfall was much greater, with numerous rivers and lakes. Hundreds of thousands of years of rainfall have also created vast underground aquifers beneath the shifting sands, known as the Nubian Sandstone Aquifer System (NSAS).

In recent years, with the increasing severity of environmental problems, people have paid more and more attention to environmental protection. Vegetation plays an important role in the Earth system and is an important renewable resource on the Earth's surface. Vegetation index remote sensing monitoring technology is a method that can obtain vegetation status through satellite remote sensing images. By acquiring optical remote sensing data of vegetation parameters, quantitative analysis of the structure and function of ecosystems can be carried out.

Hyperspectral image classification methods differ fundamentally from traditional multispectral classification. A continuous spectral curve can be obtained from each pixel of a hyperspectral image. This allows for comparison between known spectral curves and the spectral curves obtained from each pixel in the image. Ideally, if the two spectral curves are similar, it can indicate which substance the pixel is closer to.