A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Abstract

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

Methane Retrieval from Hyperspectral Infrared Atmospheric Sounder on FY3D

“…The medium resolution spectral imager type II (MERSI-II) mounted on FY3D integrates the functions of the original two imaging instruments (MERSI and a visible infrared scanning radiometer (VIRR)) of the Fengyun-3 satellite, and the spectral channels have been expanded from the original 20 channels to 25 channels [31]. Among these, there are 6 visible bands, 10 visible infrared bands, 3 short-wave infrared bands and 6 medium-tolong-wave infrared bands, the spectral coverage range is 470~12,000 nm, and the spatial resolution is 250 m to 1 km [32]. This is the first imaging instrument in the world that can obtain data regarding the global 250 m resolution infrared split window region.…”

Remote Sensing of Aerosols and Water-Leaving Radiance from Chinese FY-3/MERSI Based on a Simultaneous Method

“…It integrates two imaging instruments from the original FY-3 satellite-the visible and infrared radiometer (VIRR) and the first-generation MERSI function. FY-3D MERSI-II has a total of 25 bands, and it has 6 channels in the visible light band, 10 channels in the visible light/near-infrared band, 3 channels in the short-wave infrared band, and 6 channels in the medium-and long-wave infrared band [30,31]. The instrument can obtain abundant Earth-gas radiation images at the same time.…”

Spatial and Seasonal Dynamics of Air Quality in the Beijing-Tianjin-Hebei Region: An Analysis Using K-means Clustering and BP Neural Networks

“…Lastly, Ahmad & Ahmad (2023) studied an AQI prediction model based on a layer-recurrent neural network, demonstrating its utility for the Air Pollution Control Bureau in enhancing the accuracy of AQI predictions and pollution control measures. Si et al (2023) studied a new algorithm for haze recognition based on FY3D/MERSI-II remote sensing data.…”