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Satellite remote sensing technology plays an important role in monitoring water quality in inland water bodies.Wide-ranging, relatively fast, low-costThe inversion of key water quality parameters is possible. 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 requires high temporal resolution; and complex and variable optical properties 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.

Construction sites, as crucial locations for urban development, have always been a major source of dust pollution. With the increasing demands for urban environmental quality from the government in recent years, effectively controlling dust pollution from construction sites has become a focus of public attention. Against this backdrop, dust control nets, as an efficient and practical dust control tool, are increasingly widely used on construction sites, becoming an indispensable part of dust control efforts. Therefore, by extracting and statistically analyzing dust control net data, we can indirectly assess the distribution and progress of construction sites, identifying hotspots for civil engineering activities.

Among the many types of spectrometers, interferometric spectrometers are distinguished by their...High light throughput, high spectral resolution, high signal-to-noise ratio, and wide band coverageIt has significant advantages. Unlike traditional dispersive spectrometers that rely on prisms or gratings, it uses the principle of interference to superimpose light of different wavelengths to form fringes, and then uses Fourier transform to decode the interference information into a spectrum. It can distinguish extremely close wavelengths, maintain a high signal-to-noise ratio under weak light or infrared conditions, and achieve wide-band measurements from visible light to mid- and far-infrared. It is widely used in atmospheric monitoring, environmental remote sensing, and infrared material analysis.

In the field of marine and inland waterway monitoring, vessel identification has always been a crucial application of remote sensing technology. Vessel distribution is directly related not only to shipping traffic management, maritime law enforcement, and national defense, but also to fisheries resource monitoring, illegal fishing control, environmental protection, and emergency rescue. Traditional vessel monitoring methods primarily rely on radar and optical sensors, but these are susceptible to interference and have blind spots under complex sea conditions, cloud cover, or nighttime conditions. The emergence of hyperspectral remote sensing has brought new possibilities to vessel identification.

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, 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.

Hyperspectral remote sensing technology can capture subtle differences in crops across different spectral ranges by continuously acquiring ground reflectance information across hundreds of narrow bands. This high-dimensional spectral information not only reflects the physical and chemical characteristics of crops but also reveals changes in their state at different growth stages, thus offering unique advantages in crop identification and classification in agriculture.

Vegetation attributes are typically measured by converting reflectance spectra into a single numerical value or vegetation index. Hyperspectral or narrow-band vegetation indices contain narrow-band vegetation characteristics and wavelength information that can only be captured by hyperspectral instruments.

In recent years, climate change and other human-related environmental issues have garnered significant attention in scientific literature. Historically, wildfires were largely detected by monitoring vast areas from fire observation towers and using simple devices such as fire detectors. However, this method was not very accurate, and its effectiveness could be affected by human fatigue accumulated during long observation periods. On the other hand, alternative sensors used to detect gases, flames, smoke, and heat emissions typically require extended measurement times. Furthermore, due to the limited measurement range of these sensors, only a large number of sensors could cover large areas. The rapid development of target recognition, deep learning, and remote sensing technologies has provided us with new methods for finding and tracking wildfires.

In modern agriculture, remote sensing technology is gradually changing the way we perceive and manage land. Hyperspectral remote sensing, in particular, is like giving satellites a "chemical sense of smell," enabling them to identify subtle differences in plants. Compared to traditional cameras that can only record red, green, and blue, hyperspectral cameras can record hundreds of continuous spectral bands, accurately "seeing" the differences between different crops, soils, and even pests and diseases.

Cross-calibration uses a sensor with higher calibration accuracy as a reference to calibrate the sensor to be calibrated. The principle is to select synchronous or near-synchronous image pairs imaging the same target, and based on the analysis of the matching of the two sensors' spectral responses, observation geometry, and atmospheric parameters, establish the relationship between the digital count values ​​of the two sensor images. The calibration coefficients of the sensor to be calibrated are then solved using the known radiometric calibration coefficients of the reference sensor. Typically, the DN value of a satellite remote sensor has a linear relationship with its entrance pupil radiance (apparent radiance, Top-of-Atmosphere Radiance, TOA): L = gain·DN + offset, where gain and offset are the gain and intercept of the calibration coefficients, respectively, L represents the sensor's entrance pupil radiance, and DN represents the digital count value of the image.