Abstract: Background, aim, and scope With the discovery of the ozone hole, due to the increasingly serious damage of the stratospheric ozone, the ultraviolet radiation reaching the earth’s surface has increased. The current situation and the long-term trend of ultraviolet radiation have caused widespread concern in the scientific community. The remote sensing observation of ultraviolet radiation has developed earlier in foreign countries, and there have been a lot of research results on the temporal and spatial variations of ultraviolet radiation. The latest sensor is the OMI, which was mounted on NASA’s AURA satellite in 2004. Many studies have verified the accuracy of the ultraviolet radiation data product. The data can be used for the research of spatial distribution and temporal variation of ultraviolet radiation. Materials and methods The UV radiation measurements from the Ozone Monitoring Instrument (OMI) abroad on AURA are used in this paper for investigating the temporal and spatial variation over China from 2005 to 2015, The data format is HDF-EOS (.he5) with a spatial resolution of 0.25°×0.25° and a time resolution of 1 day. First, we use MATLAB software to extract ultraviolet radiation erythemal dose data from UV radiation products (HDF-EOS 5 file format), convert the vector point data into monthly, quarter and year. The interpolation processing is performed using Inverse Distance Weighted (IDW) to regenerate 0.25°×0.25° raster data. Then analyze the temporal and spatial variation characteristics of ultraviolet radiation erythemal dose in China. This paper uses the coefficient of variation to characterize the fluctuations in time and space. The coefficient of variation (CV) is the ratio of the standard deviation (σ) to the mean ( μ). Results On the spatial scale, the UV presents the latitudinal distribution, decreasing from the south to the north, with the value about from 4200 J ∙ m−2 to 1500 J ∙ m−2. At low latitudes and high altitudes, such as the Yunnan-Kweichow Plateau, the annual average UV radiation is maintained at a medium level. Over the higher altitude of Tibetan Plateau, the UV erythemal dose has a higher value with the multi-yearly mean value is about 5500 J ∙ m−2, and over some regions, the value is up to 6000 J ∙ m−2. On the temporal scale, the largest value appears in July of summer with the multi-yearly mean value about 5532.9 J ∙ m−2, the moderate values appear in the spring and autumn, and the smallest value is in December of winter with the multi-yearly mean value about 1089.2 J ∙ m−2. The yearly mean UV erythemal dose presents an increasing trend during 2005 — 2015 for the entire region, while there is a valley value in 2010 with about 3016.5 J ∙ m−2. Discussion The UV radiation exhibits obvious seasonal variations over China, which are mainly determined by changes of the location of direct solar radiation. The temporal variation of UV radiation is mainly affected by the change of total ozone in the atmosphere. Conclusions There is a positive correlation of the UV erythemal dose and the altitude. The UV erythemal dose values are lower over eastern and northeastern regions with lower altitude. The characteristics of the land type and the climate are also the reasons that affect the change of ultraviolet radiation. Such as Sichuan Basin, more precipitation, heavy fog and heavy rain, and low clouds and cloudy days, so that the ultraviolet radiation reaching the earth’s surface decreases with the decrease of total solar radiation, making the annual UV radiation lower than other regions in whole year. On the temporal scale, the UV erythemal dose presents obvious seasonal variation, and the variation as a parabolic curve. The variations of trend of UV erythemal dose are different over different regions, and present mainly two patterns, one is increasing trend in the higher value region of Tibetan Plateau, the other pattern is decreasing trend in the lower value regions except Tibetan Plateau. Recommendations and perspectives The mechanism of temporal and spatial changes affecting ultraviolet radiation is very complicated. Revealing the inherent causes and laws of temporal and spatial distribution characteristics requires more in-depth research and comprehensive analysis using multi-source remote sensing data.
Keywords: OMI; surface albedo; satellite remote sensing; coefficient of variation; spatial interpolation