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Stray light elimination is a crucial issue in optical imaging system research. Stray lights typically manifest as high-intensity specks with a spatial coverage much larger than the details of the effective target, significantly impacting image contrast, uniformity, and subsequent radiometric calibration and quantitative analysis. This paper employs a two-dimensional anisotropic Gaussian function to model stray light specks, achieving image stray light correction.

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.

Since 1981, when Goetz et al. first identified limestone and clay minerals from space orbit using the 10-band SMIRR (Space Shuttle Multispectral Infrared Radiometer), remote sensing technology has revolutionized the scope, scale, content, and methods of human geoscientific research. With the successful development of a series of hyperspectral resolution remote sensing instruments (such as China's GF5 AHSI, the US's AVIRIS, and Italy's PRISMA) and the vigorous development of satellite technology, hyperspectral remote sensing technology has made significant progress in data acquisition, radiometric calibration, spectral reconstruction, data processing, and ground feature identification. More and more countries and regions are widely applying hyperspectral remote sensing technology to mineral spectral identification and mapping. This article will analyze hyperspectral remote sensing mineral exploration technology using a case study.

Cotton is one of my country's important economic crops, and its growth process includes key stages such as seedling stage, budding stage, flowering stage, boll stage, and tufting stage. Traditional cotton management relies on manual field inspections and experience-based judgment, which is not only time-consuming and labor-intensive but also makes it difficult to achieve large-scale, rapid, and objective identification of the growth stage. With the development of remote sensing technology, monitoring cotton growth status using spectral indices has become an efficient, low-cost, and automated method. The Boll Cotton Index (BCI), as a remote sensing index targeting the mid-to-late stages of cotton growth, can sensitively reflect boll hull and boll exposure, leaf yellowing, and changes in canopy structure during the tufting stage, making it one of the important indicators for monitoring cotton growth stages.

In the field of quantitative remote sensing, the key to accurately retrieving the true radiative characteristics of ground objects lies in eliminating the influence of atmospheric path radiation. Atmospheric correction methods based on the MODTRAN model, by constructing an accurate radiative transfer mechanism model, perform inverse calculations on the comprehensive radiance received at the sensor's entrance pupil, thereby eliminating interference from atmospheric absorption and scattering. This is an indispensable core step in obtaining accurate physical parameters of ground objects. This case study uses calibration data from the Xiguang-1 05 satellite (Tianxianpei) received on September 23rd at the Dunhuang calibration field in Gansu Province to retrieve the entrance pupil radiance.

By utilizing the photosynthetic pigment index in a narrow band of hyperspectral imaging, vegetation species can be accurately distinguished. This case study uses hyperspectral data from the Xiguang-1 05 satellite (also known as Tianxianpei) and Sentinel-2 data to calculate the ARI1 anthocyanin index and extract forest land within farmland areas. Thanks to the 9 nm spectral resolution and narrow band design of the Xiguang-1 05 satellite (Tianxianpei), the reflectance difference at precise locations of 550 nm and 700 nm is significantly amplified, making forest land characteristics readily apparent. Compared to Sentinel-2 results, there are fewer false positives and the extracted results have less noise. Furthermore, a single image is sufficient, eliminating the need for time-series data, thus providing rapid and reliable spectral evidence for monitoring the "non-agriculturalization" of farmland.

Water resources are one of the most precious natural resources for humankind. Although my country ranks fourth in the world in total freshwater resources, its per capita share is only one-quarter of the world average. Utilizing remote sensing technology to accurately and rapidly acquire dynamic information on land-based water bodies is an efficient technical means for water resource surveys and area monitoring.

There is a considerable body of research on water body information extraction at present, with most data sources being multispectral. Various methods exist for extracting water bodies of different types and regions, each with its own advantages; however, research on their application in hyperspectral satellite data is relatively limited. Therefore, this paper compares and analyzes different water body extraction methods using the Xi'an Optics-1 05 satellite (also known as the Tianxianpei satellite).

With the continuous deployment of Earth observation platforms such as meteorological satellites, land satellites, and ocean satellites, remote sensing data is experiencing explosive growth. However, the raw data exists in the form of digital voltage values. If it cannot be accurately converted into physically meaningful spectral radiation information, it will be difficult to support the reliability of Earth observation and analysis, let alone meet the quantitative needs of fields such as climate change monitoring and disaster early warning.Spectral radiometric calibration techniquesThis is precisely the key to resolving this core contradiction.

Red mud storage facilities are specialized structures for storing red mud, a highly alkaline waste residue generated during alumina production. Their core function is to prevent red mud leakage and environmental pollution. Because red mud contains fluorides and its accompanying liquid has a pH value far exceeding safety standards, it is classified as Class II general industrial solid waste and requires anti-seepage design according to relevant environmental standards. Early red mud storage facilities, lacking anti-seepage measures, caused groundwater pollution problems. As of 2016, China's accumulated red mud stockpiles exceeded 50 million tons, with a utilization rate of only 15%, and stockpiling remains the primary treatment method.

Identifying and differentiating the distribution of different crop types using remote sensing imagery enables refined management and macro-level monitoring of agricultural production. This monitoring provides data support for pest and disease early warning. Furthermore, the remote sensing classification results can be combined with meteorological, hydrological, and soil information to provide a scientific basis for agricultural yield estimation, disaster loss assessment, and food security decision-making.