使用空间时空长短期内存卷积自编码器重建气溶光学深度
Lu Liang1, Jacob Daniels2, Michael Biancardi3
1Department of Geography and the Environment, University of North Texas, Denton, TX, 76203, USA. lu.liang@unt.edu.
Scientific data
|November 30, 2023
概括
这项研究使用卫星数据和人工智能为德克萨斯州创建了一个无间隙,长期的气溶光学深度 (AOD) 数据集. 这种改进的AOD数据有助于气候和空气质量研究.
科学领域:
- 大气科学 大气科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 气溶光学深度 (AOD) 对气候,空气质量和健康评估至关重要.
- 卫星AOD数据提供了广泛的覆盖范围,但由于各种因素而存在数据缺口.
- 现有的AOD数据集缺乏全面分析所需的长期连续性.
研究的目的:
- 为德克萨斯州 (2010-2022) 开发一个无间隙的,长期的卫星衍生的AOD数据集.
- 使用先进的深度学习技术重建丢失的AOD数据.
- 为环境和公共卫生研究提供可靠的AOD资源.
主要方法:
- 使用中等分辨率成像光谱辐射仪 (MODIS) 多角度实现大气校正 (MAIAC) 产品.
- 采用时空长短期记忆 (LSTM) 卷积自编码器进行数据重建.
- 将重建的数据与独立测试和地面AERONET数据集进行验证.
主要成果:
- 与测试数据相比,在AOD重建中实现了高精度,RMSE为0.017和R2为0.941.
- 证明了与地面AERONET数据 (RMSE 0.052-0.067) 的满意一致.
- 创建了一个全面的数据集,可用于每日,月度,季度和年度决议.
结论:
- 开发的LSTM自编码器有效地重建了缺失的卫星AOD数据.
- 无间隙的德克萨斯州AOD数据集为大气和健康研究提供了宝贵的资源.
- 这项工作增强了卫星AOD数据对科学研究和决策的有用性.
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