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

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.

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.

In the fields of computational optical imaging and hyperspectral remote sensing, spatiotemporal joint modulation Fourier transform interferometric spectral imaging technology, with its advantages of high throughput, significant signal-to-noise ratio gain, and all-solid-state hardware without moving parts, has become an indispensable technical path for aerospace payloads and precision laboratory detection. As the most representative architecture in this field, the Sagnac triangular common-path interferometer utilizes its inherent vibration resistance and compact physical structure to achieve efficient coupling and high-fidelity acquisition of target 3D information on the detector image plane through a clever combination of lateral shearing mechanism and carrier push-broom motion. This paper will focus on the optical physics of push-broom systems, deeply analyze the generation mechanism and spatiotemporal mapping law of interferometric field data, and discuss in detail the discretization extraction process of the interferometric matrix, aiming to provide rigorous theoretical basis and technical support for improving the spectral reconstruction accuracy in complex remote sensing environments.

Signal-to-noise ratio (SNR) is a crucial indicator of the detection performance of imaging spectrometers, directly impacting the quality of spectral information acquisition from ground objects. In spaceborne imaging spectrometers, SNR determines the effective identification capability of spectral features of targets such as vegetation, water bodies, and minerals. Because observation geometry and surface reflectance characteristics significantly influence entrance pupil radiance, the SNR of imaging spectrometers varies considerably under different solar altitude angles and surface reflectance conditions. Therefore, it is necessary to quantitatively predict the SNR based on typical observation conditions during the instrument design and performance evaluation phases.

Methane (CH4) is a radioactive and chemically reactive gas in the atmosphere, influencing not only the radiation balance of the Earth-atmosphere system but also playing a crucial role in atmospheric and stratospheric chemical processes. As the second largest greenhouse gas after carbon dioxide, methane's annual average concentration is more than 2.5 times higher than pre-industrial levels. Its global warming potential (GWP, the ability to trap heat in the Earth's atmosphere) is approximately 30 times that of carbon dioxide on a centennial timescale and approximately 84 times on a 20-year timescale. Methane has a relatively short atmospheric lifetime (approximately 12 years), while the effects of carbon dioxide can last for centuries. Compared to the chemically stable carbon dioxide, controlling methane emissions has an immediate effect on achieving carbon neutrality and mitigating global warming.

In lake and wetland monitoring supported by hyperspectral remote sensing technology, total suspended sediment (TSM) is a key indicator for evaluating water transparency and lake siltation. As a transient lake, Hongze Lake has a large fluctuation in water area with water level and an average water depth of only 1.5 m. The complex sources of sediment and the distribution of aquatic vegetation constitute a unique spectral environment.

Carnarvon National Park, located in central Queensland, Australia, boasts spectacular sandstone canyons, ancient rock paintings, and diverse ecosystems. In December 2025, during the Southern Hemisphere summer, the region experienced nine consecutive days without rainfall, with daytime temperatures frequently exceeding 35°C. At least four fire spots were observed in Sentinel-2 imagery during the same period.

The Rumaila oil field is a supergiant oil field located approximately 50 kilometers west of Basra, Iraq (Figure 1), situated in a giant anticline rich in oil and gas on the edge of the Arabian Platform. Discovered in 1953, it is one of Iraq's largest and oldest oil fields, with estimated recoverable crude oil reserves of up to 17 billion barrels, ranking as the sixth largest oil field in the world in terms of current production. As a pillar of Iraq's oil industry, the stable production of the Rumaila oil field is of vital strategic importance to Iraq's economic reconstruction and national finances.

Satellite remote sensing technology plays a crucial role in inland water quality monitoring, enabling the retrieval of key water quality parameters over a large area, relatively quickly, and at low cost. However, inland water quality monitoring also places high demands on satellite remote sensing technology: small water bodies require high spatial resolution; rapidly changing water quality necessitates high temporal resolution; and complex and variable optical characteristics of water bodies require high spectral resolution. Therefore, satellite data that simultaneously meets high spatial, high temporal, and high spectral resolution has significant advantages in inland water monitoring.