基于MODIS数据的新疆大气气溶胶光学厚度时空变化分析          下载免费PDF全文

于志翔 《沙漠与绿洲气象(新疆气象)》2023,17(2):128-136

利用中分辨率成像光谱仪（Moderate-resolution Imaging Spectroradiometer,MODIS）大气气溶胶光学厚度产品数据，采用三角剖分算法、最近邻点搜索、插值法和趋势分析法，分析新疆气溶胶光学厚度（Aerosol Optical Depth,AOD）时空变化。结果表明：（1）新疆AOD总体呈下降趋势且地域差异明显，南疆AOD明显高于北疆，高值区主要集中在塔里木盆地边缘和天山北坡经济带。（2）北疆AOD年际变化不明显。2014年最高，2005年最低，年均值在0.15～0.18。南疆则呈明显的年际变化。最高值出现在2006年，为0.42；最低值出现在2017年，为0.22。（3）新疆AOD呈明显季节变化特征。春季最大，秋季最小。（4）2003—2019年南疆环塔里木盆地北部、东南边缘和北疆沿天山北坡经济带AOD增量明显。  相似文献   

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations     

《地学前缘(英文版)》2024,15(1):101686

As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM_2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from –3.4 µg/m³ to –1.6 µg/m³. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM_2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM_2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM_2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.  相似文献