2.5) enables accurate a'/> High spatiotemporal resolution PM2.5 concentration estimation with satellite and ground observations: A case study in New York City
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首页> 外文会议>2018 IEEE International Conference on Environmental Engineering >High spatiotemporal resolution PM2.5 concentration estimation with satellite and ground observations: A case study in New York City
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High spatiotemporal resolution PM2.5 concentration estimation with satellite and ground observations: A case study in New York City

机译：卫星和地面观测的高时空分辨率PM2.5浓度估算：以纽约市为例

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High spatiotemporal resolution concentration of fine particulate matter (PM2.5) enables accurate and detailed air quality monitoring, especially for metropolitan cities with high levels of population density. Although ground air quality monitoring stations can provide timely and accurate observations, they are usually very sparsely distributed, and cannot provide PM2.5concentration data with continuous spatial coverage. Instead, satellite observations, e.g., Landsat 8/Thermal Infrared Sensor (TIRS) and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), can both obtain data with continuous coverage. However, there is a trade-off between satellite sensors' spatial and temporal resolution. Hence, this study presents an estimation model for PM2.5concentrations that combines these multi-source data to produce high spatiotemporal resolution concentration maps in urban area. The approach is tested on New York City, NY, USA. Specifically, we first use cloud-free MODIS thermal band images and the corresponding ground-station PM2.5records to build a local PM2.5prediction model. Then, we exploit a spatiotemporal image fusion technique to obtain Landsat-like thermal band image series from Landsat 8/TIRS (100 m spatial resolution) and Terra/MODIS (1 km spatial resolution) sensors. Finally, we convert the fused high spatiotemporal resolution thermal band images to PM2.5concentration maps by the prediction model from step 1. The validation between the estimated and the real PM2.5values shows that the detailed Landsat-like high spatial resolution PM2.5estimations are more accurate than the original blurred MODIS one.
机译：高时空分辨率的细颗粒物浓度（PM \ n 2.5 \ n）可以进行准确而详细的空气质量监控，尤其是对于人口密度高的大城市而言。尽管地面空气质量监测站可以提供及时而准确的观测结果，但它们通常分布非常稀疏，无法提供PM \ n 2.5 \ n具有连续空间覆盖的浓度数据。相反，卫星观测，例如Landsat 8 /热红外传感器（TIRS）和Terra /中分辨率成像光谱仪（MODIS），都可以获取连续覆盖的数据。但是，在卫星传感器的空间和时间分辨率之间需要权衡。因此，本研究提出了PM \ n 2.5 \ nconcentrations，将这些多源数据组合在一起，以生成市区中的高时空分辨率浓度图。该方法已在美国纽约州纽约市进行了测试。具体来说，我们首先使用无云的MODIS热能带图像和相应的地面站PM \ n 2.5 \ n记录以建立本地PM \ n 2.5 \ n预测模型。然后，我们利用时空图像融合技术从Landsat 8 / TIRS（100 m空间分辨率）和Terra / MODIS（1 km空间分辨率）传感器获得类似Landsat的热带图像序列。最后，我们将融合的高时空分辨率热带图像转换为PM \ n 2.5\n浓度按照步骤1中的预测模型进行映射。估算值与实际PM之间的验证\ n 2.5 \ nvalues显示详细的Landsat-例如高空间分辨率PM \ n 2.5 \ nestimations比原始的模糊MODIS更加准确。

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