一种改进的基于气象变量的气溶光学深度估计方法,通过将物理机制模型与两阶段模型模型相结合
Fuxing Li1, Xiaoli Shi2, Shiyao Wang2
1State Environmental Protection Key Laboratory of Satellite Remote Sensing, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China; School of Geographical Sciences, Hebei Normal University, Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Shijiazhuang, 050024, China.
Chemosphere
|July 10, 2024
概括
一个新的STG-ERM模型使用气象数据改进了气溶光学深度 (AOD) 检索,大大提高了北京-天津-河北地区的数据覆盖率和准确性. 这种方法为填补卫星-AOD产品缺口提供了一种有价值的方法.
科学领域:
- 大气科学 大气科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 使用气象变量的气溶光学深度 (AOD) 检索可能受到数据缺口和准确性的限制.
- 像埃尔特曼检索模型 (ERM) 这样的现有方法需要改进,以提高时空覆盖率和精度.
研究的目的:
- 通过整合时空线性混合效应 (STLME) 和地理权重回归 (GWR) 模型来开发和评估改进的AOD检索方法.
- 提高北京-天津-河北 (BTH) 地区的AOD估计的准确性和数据覆盖率.
主要方法:
- 通过结合STLME和GWR模型,开发了一种两阶段模型STG-ERM.
- 该STG-ERM模型应用于2019年和2020年BTH地区的气象数据.
- 检索结果与多角度实施大气校正 (MAIAC) AOD数据进行了交叉验证.
主要成果:
- 该STG-ERM模型显著增加了2019年的数据覆盖率为39.0%,2020年的数据覆盖率为40.5%.
- 交叉验证显示了与以前的模型相比的实质性改进,具有高的确定系数 (R2=0.86) 和可接受的预测误差.
- 合并年均AOD显示出明显的空间变化,平原的价值较高,山区的价值较低,以及强烈的季节性模式.
结论:
- STG-ERM模型为AOD检索提供了强大而准确的方法,性能优于早期的气象模型.
- 气象数据覆盖范围的影响融合了AOD准确性,在AOD高的地区灵敏度更高.
- 连续的高分辨率气象数据可以进一步提高模型性能,使STG-ERM成为填补卫星AOD产品缺口的宝贵工具.
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