Since 2008, straw burning has been vigorously banned in China. To investigate the impact of straw burning on AOD, this study used VIIRS nominal and high confidence fire point data and MODIS cropland land cover data to identify straw burning fire points. Additionally, by utilizing MOD04_3K aerosol products, the study analyzed the spatiotemporal correlation between straw burning during the summer and autumn harvest seasons and the aerosol optical deep (AOD) from 2012 to 2021. The results show that: (1) The distribution of straw burning fire points and ADO in Shandong Province exhibits significant seasonal characteristics, with AOD being heavily influenced by seasonal variations. (2) There is a linear relationship between the number of straw burning fire points and the seasonal mean AOD, with a correlation coefficient of 0.8145 in summer and 0.8907 in autumn. The correlation in autumn is slightly higher than that in summer. (3) The distribution of fire points coincides with the high AOD areas, mainly concentrated in the northern, western, southern, and central-eastern parts of Shandong Province, which are primarily wheat and corn planting regions. (4) The AOD in Shandong province showed a decreasing trend during the decade, and it was more obvious in the northern, western and central-eastern regions. These areas have the same spatial distribution as the regions where straw burning has decreased.
This paper presents a deep learning based method for inversion of Aerosol Optical Depth (AOD) from Landsat 8 OLI multi-band satellite data. Traditional aerosol inversion algorithms include the dark target method, the deep blue algorithm and other algorithms. The dark target method is good for areas with low surface reflectivity such as dense vegetation and water bodies, while the deep blue algorithm is usually used for inversion in areas with high reflectivity. Therefore, this study employs Deep belief networks (DBN) to learn the potential relationship between Landsat 8 OLI multi-band data and AOD. In this paper, Landsat 8 OLI remote sensing images with atmospheric AOD observations over the past 6 years (2013-2018) were collected as the training dataset. A deep confidence network structure is next designed for learning the mapping relationships from Landsat 8 OLI multi-band data to AOD. To evaluate the performance of the proposed method, we used a test set for validation. The experimental results show that the proposed deep learning-based method has higher accuracy and stability in AOD inversion compared to traditional methods.
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