The tropospheric vertical column density of NO₂ (Trop NO₂ VCD) can be obtained using satellite remote sensing, but it has been discovered that the Trop NO₂ VCD is affected by uncertainties such as the cloud fraction, terrain reflectivity, and aerosol optical depth. A certain error occurs in terms of data inversion accuracy, necessitating additional ground observation verification. This study uses surface NO₂ mass concentrations from the China National Environmental Monitoring Center (CNEMC) sites in Jiangsu Province, China in 2019 and the Trop NO₂ VCD measured by MAX-DOAS, respectively, to verify the Trop NO₂ VCD product (daily and monthly average data), that comes from the TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The results show that the spatial distributions of NO₂ in TROPOMI and OMI exhibit a similar tendency and seasonality, showing the characteristics of being high in spring and winter and low in summer and autumn. On the whole, the concentration of NO₂ in the south of Jiangsu Province is higher than that in the north. The Pearson correlation coefficient (r) between the monthly average TROPOMI VCD NO₂ and the CNEMC NO₂ mass concentration is 0.9, which is greater than the r (0.78) between OMI and CNEMC; the r (0.69) between TROPOMI and the MAX-DOAS VCD NO₂ is greater than the r (0.59) between OMI and the MAX-DOAS. As such, the TROPOMI is better than the previous generation of OMI at representing the spatio-temporal distribution of NO₂ in the regional scope. On the other hand, the uncertainties of the satellite products provided in this study can constrain regional air quality forecasting models and top-down emission inventory estimation.

Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data.