Abstract
Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (PODcloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO2 parameter inversion.
Highlights
To solve the above problems, this paper proposes a more effective cloud detection method for Cloud and aerosol polarization imaging detector (CAPI), which is a thresholds detection method based on different underlying surface thresholds from near ultraviolet to near infrared (NNDT)
The cloud areas marked by the two results are basically the same, and only the determination of a very few pixels at the edge of the cloud has a deviation, which is within a reasonable range in the thresholds cloud recognition
The larger deviation between NNDT-CLFG and Second-generation GLobal Imager (SGLI)-CLFG appears in the polar scene, where a large number of cloud pixels at the edge of the cloud cluster marked by NNDT-CLFG are marked as clear sky by SGLI-CLFG
Summary
CO2 is one of the greenhouse gases on earth, and the increase in its concentration over the last century seriously affects the environment of human survival [1]. The IPCC report in 2014 shows that the radiative forcing effect of aerosols is the largest source of uncertainty in climate change assessment. Monitoring and evaluating the spatial distribution and parameters of both CO2 and aerosols have become a matter of great concern for scientists. Research and improvement of aerosol and CO2 parameter retrieval schemes have been carried out all over the world. Large-scale observation of the earth can be achieved by satellite remote sensing, which has become one of the main ways to detect CO2 and aerosol [2]
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