Sea surface temperature (SST) is one of the physical parameters of the ocean, which plays an important role in meteorology, navigation, and fishing. SST retrieval from thermal infrared remote sensing has become a research focus because of its advantages of high efficiency and wide observation range. However, more accurate retrieval algorithms and more complete data processing procedures are needed for the satellite data with higher spatial–temporal resolution. High-precision SST retrieval models based on split-window algorithm were established using Gaofen-5 Visual and Infrared Multispectral Imager (VIMS) data, some of which introduced the quadratic term of brightness temperature difference into the models. The highest accuracy is better than 0.15 K. Moreover, we proposed a relatively complete cross-calibration approach to solve the problem of unstable calibration coefficients in bands 11 and 12 of VIMS. Therefore, the accuracy of the models was verified by satellite images. At last, the models were applied to the retrieval of SST in the vicinity of Fuqing Nuclear Power Plant. We expand the practical value of VIMS in SST retrieval and thermal discharge monitoring and provide technical support for the application of high-resolution remote sensing data.
Urban river water bodies have multiple functions such as landscape, ecology, and shipping. With the improvement of people's living standards, the requirements for urban river water quality and water environment quality are gradually increasing. The traditional ground station sampling and analysis method will cost a lot of manpower and financial resources. Remote sensing technology has the advantages of macroscopic, periodic revisiting, low cost, diverse platforms, and rich data. UAV remote sensing data is suitable for rivers with a smaller width. The surface spectrometer is used to measure the spectral reflectance of the water surface and analyze and establish the relationship model between the spectral reflectance of the water surface and water quality. In Jiading District, Shanghai, as an application demonstration area, remote sensing measurement was carried out for the main water quality parameters and water quality types of the river. The remote sensing identification accuracy of typical water quality parameters was better than 70%, and two water quality parameters was better than 80%. The accuracy of remote sensing recognition of water quality type is better than 80%. The hyperspectral remote sensing technology can improve the frequency and efficiency of water quality monitoring. It can be used as an effective supplementary means for ground monitoring and provide continuous data support for long-term monitoring of river water quality.
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