This paper compares three dust detection algorithms over land that were developed for operational, near-real-time processing using the Suomi National Polar Orbiting Partnership Visible Infrared Imaging Radiometer Suite instrument. The three algorithm approaches use different spectral bands, namely deep blue bands, infrared (IR)-visible bands, and IR bands, and are applied for dust observed over dark as well as bright surfaces. The evaluations are performed both using case studies and AERONET matchup data over western CONUS-Mexico region and North Africa-Arabian Peninsula region. The deep blue-based algorithm is found to have the most false detections and its detection performance depends on the Sun-satellite geometries. Simulation analysis shows that there are three causes of this problem: surface reflectance, air mass factors, and phase functions in different geometries. The algorithm based on IR-visible bands has much less false detection than the deep blue bands-based algorithm and has better true positive detection than the IR-based algorithm. The IR bands-based algorithm performs well in the case studies over CONUS–Mexico region, but it fails to detect most of the dust cases over North Africa–Arabian Peninsula region. The results suggest that the IR-visible algorithm is the most suitable for the dust detection of the three algorithms with a small modification. Because the IR-visible algorithm is not able to detect all the dust pixels, detections from the deep blue algorithm only and those from the IR-visible algorithm with relaxed criteria are also provided but are distinguished with a lower quality.
Assessment of human health impact from the exposure to PM10 air pollution is crucial for evaluating environmental damage. We established an empirical model to estimate ground PM10 mass concentration from satellite-derived aerosol optical depth and adopted the dose-response model to evaluate the annual average human health risks and losses related to PM10 exposure over China from 2010 to 2014. Unlike the traditional human health assessment methods, which relied on the in situPM10 concentration measurements and statistical population data issued by administrative district, the approach proposed in this study obtained the spatial distribution of human health risks in China by analyzing the distribution of PM10 concentration estimated from satellite observations and population distribution based on the relationship to the spatial distribution of land-use type. It was found that the long-term satellite observations have advantages over the ground-based observations in estimating human health impact from PM10 exposure.
Lone-term satellite observations, such as Advanced Very High Resolution Radiometer (AVHRR), provide an irreplaceable means in monitoring Earth system through a series of satellites. However, to be able to detect the signal related to climate change, one of the critical requirements is the consistency and stability of calibration among the satellites. Applying Simultaneous Nadir Overpass (SNOs) method (Cao et al., 2002)., we fully accessed instrument-related consistency of AVHRR measurements covering all channels (from visible to IR) and time period from 1978 to 2003. It is seen that the inter-satellite biases in visible channels (channel 1 and 2) show larger inconsistency among satellites especially between NOAA-14 and NOAA-12. The inconsistency is shown as both the large bias and trend in the biases, mostly due to the lack of onboard calibration. Comparatively, the biases in IR channels, i.e., channel 4 and 5 are generally smaller, there are within ± 1 k. However, the difference in the magnitude of the biases among satellites and the dependence of biases on the scene temperature may affect the quality of long term trend derived from such dataset. Analyses of bias root causes indicate that the effect from the difference in Spectral Response Function may not be large enough to account for the observed biases.
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