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Data preparation and fusion were performed using Sentinel Hub and Google Earth Engine. Training samples were labeled using publicly available datasets, publications, and visual interpretation. The supervised learning method OBIA-RF (Object-Oriented Random Forest) was then used to classify and extract land cover features from the agricultural and forestry datasets. Finally, mapping and statistical analysis were conducted. Soil ecological potential was assessed by combining meteorological and soil data.

Debris flows are torrents formed when heavy rain or floods saturate and dilute loose, sandy soil in mountainous areas. They are widely distributed in regions with unique topography and landforms around the world, and their destructive power is more extensive and severe than that of individual collapses, landslides, or floods. Debris flows pose a threat to human life and property through their destructive and burying mechanisms.

With increasing demands for environmental and ecological protection, environmental protection and ecological construction have gradually become a global focus. Forestry development is crucial in the field of ecological construction and environmental protection. Given the significant increase in forest coverage in my country, improving forest quality has become a key focus of current forest management. Current research on forest health in my country is limited to traditional plot surveys, a method that is time-consuming, labor-intensive, and unable to effectively reflect changes in forest health. With advancements in spectral imagers and hyperspectral remote sensing technology, more detailed information about land features can be obtained. Our company uses vegetation indices extracted from hyperspectral images to assess forest health. Based on the spectral characteristics of forest vegetation, we can establish an indicator system for evaluating forest health based on vegetation indices, considering three aspects: vegetation distribution, photosynthetic intensity, and stress caused by pests, diseases, and water.

To obtain information on the spatiotemporal distribution and variation of near-surface CH4 concentration, shortwave infrared CH4 spaceborne detection technology has developed rapidly in recent years. Multiple shortwave infrared detection satellites have been launched both domestically and internationally, conducting a series of CH4 detection and related application studies, achieving space-based CH4 detection. The scientific experimental satellites launched during this period have promoted the development of CH4 satellite remote sensing inversion algorithms and flux inversion algorithms, laying a technological foundation for improving space detection capabilities, producing data products, and directly applying them. Furthermore, they have demonstrated the application potential of shortwave infrared detection in global CH4 source-sink balance estimation from the perspectives of detection data accuracy and flux estimation.

The Republic of Belarus, commonly known as Belarus, has a land area of ​​207,600 square kilometers. The terrain is mostly flat, consisting of plains and basins, with an average elevation of 160 meters. The south is a vast lowland, the central region is mostly plains and low hills, and the north and northwest are slightly higher, with some highlands and rolling hills, the highest point reaching 345 meters. The soil is mainly meadow-podzolic soil, followed by marsh soil and sandy soil; the soil is relatively fertile, with an average growing season of 184-208 days. Water resources are abundant, with numerous rivers and lakes, totaling 20,781 rivers, 12,250 lakes, and 153 reservoirs. The climate is mild and humid, classified as a temperate continental climate, with warm summers, rainy autumns, and wet winters. The average temperature in January is between -4°C and -8°C, and the average temperature in July is between +17°C and +19°C. The lowlands receive an average annual rainfall of 500–650 mm, while the plains and highlands receive 650–750 mm. The abundant rainfall and favorable natural conditions make the area suitable for agricultural production.

Methane emission inventories typically employ a bottom-up approach, linking emissions to related production activities to inform emission control strategies. Top-down atmospheric methane observations, on the other hand, use a reverse approach to improve methane emission concentration estimation. Satellite-based methane observations have attracted significant attention due to their high observation frequency and global coverage. Among these, atmospheric methane column concentration detection using shortwave infrared spectroscopy can achieve near-unit-level detection sensitivity.

The Sixth Integrated Assessment Report released by the Intergovernmental Panel on Climate Change (IPCC) in March 2023 indicated that global surface temperature from 2011 to 2020 was 1.1°C higher than pre-industrial levels (1850–1900). Greenhouse gas emissions from human activities such as fossil fuel combustion and land use have undeniably contributed to global warming. In recent years, extreme weather and climate events such as high temperatures, droughts, and torrential rains have become more frequent and intense, posing serious challenges to the sustainable development of future human society. Accurately monitoring greenhouse gas concentrations, sources, and trends is fundamental to greenhouse gas emission statistics and accounting. Compared with traditional ground-based monitoring methods, satellite remote sensing offers unique advantages such as high resolution, wide coverage, short revisit periods, and continuous dynamic data, providing high-precision, fundamental data support for global, national, and regional greenhouse gas monitoring.

Methane monitoring technologies are generally designed based on optical, chemical, and acoustic principles, primarily including hyperspectral infrared imaging spectrometers, thermal imagers, photoionization detectors, and ultrasonic detectors. These devices can be directly installed on production and transportation facilities for online monitoring, or mounted on mobile vehicles such as vehicles, low-altitude aircraft, or drones for sampling and analysis in a specific area. However, traditional monitoring technologies are limited by factors such as manpower, cost, time, and space, making it impossible to obtain continuous and traceable methane emission data over large areas, and lacking the ability to detect large-scale methane leaks. With advancements in satellite remote sensing technology, especially hyperspectral imaging technology, developed countries are accelerating the research and application of this technology in methane monitoring, aiming to build a global methane monitoring system.

With the continuous advancement of technology, precision agriculture has become an important development direction for modern agriculture. Precision agriculture is an agricultural production model based on modern information technology. By accurately acquiring, analyzing, and applying farmland information, it enables effective monitoring and management of the farmland environment, crop growth, and pests and diseases, thereby improving agricultural production efficiency and the quality of agricultural products. In this process, hyperspectral remote sensing technology plays a crucial role.

Methane (CH4) is a significant and potent greenhouse gas. Since the Industrial Revolution, atmospheric CH4 levels have been steadily increasing, with the global average concentration rising from 1.714 × 10⁻⁶ in 1900 to 1.912 × 10⁻⁶ in 2022, making it the most significant global warming factor besides carbon dioxide (CO2). Furthermore, CH4 has a global warming potential 27–30 times higher than CO2. Controlling methane emissions is crucial for controlling global average temperature rise (less than 2 °C and ideally less than 1.5 °C). Therefore, monitoring atmospheric CH4 has become a key focus and hot topic in carbon emission reduction.