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首页> 外文会议>SPIE Conference on Remote Sensing Technologies and Applications in Urban Environments >Comparison of satellite remote sensing data in the retrieve of PM10 air pollutant over Quito, Ecuador
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Comparison of satellite remote sensing data in the retrieve of PM10 air pollutant over Quito, Ecuador

机译：厄瓜多尔基多PM10空气污染物检索卫星遥感数据的比较

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Most of the large cities have an air quality network to measure air pollution including PM10. However, air quality monitoring network has a high cost and it is spatially limited. Quito, capital of Ecuador, is a city with an automatic air quality network (REMMAQ) composed by 9 stations. The REMMAQ works since 2002, measuring PM10 only in 4 regular stations located at different points along the city. This scarce quantity of PM10 measures led us to propose a new strategy to obtain PM10 data in all the city. Several studies have already considered the retrieving of PM10 from remote sensing data in cities with an air quality network. In order to find an optimal model to retrieve PM10 in Quito, this study compare the use of 3 different satellite sensors (Landsat-7 ETM+, Landsat-8 OLI and TERRA/MODIS) between 2013 to 2017. Additional to remote sensing data, we also use field data considering the REMMAQ. In each sensor, we used different variables and environmental indexes to model the best fit equation to quantify PM10 in all the city, finding the significant variables for each type of data and year. The variables considered were the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Soil-adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Stability Index (NSI), surface reflectance Blue Band (Bl), surface reflectance Green Band (B2) and surface reflectance Red Band (B3). These variables were considered because most of them are used in different studies combined with meteorological data. All the procedures were implemented in R Studio. The empirical models using remote sensing data/derived products and air quality data can help in retrieving air pollutants in large cities. This work is a valuable contribution for the study of the spatialization of PM10 in order to find new alternatives in the use of remote sensing data to support government decisions.
机译：大多数大城市都有空气质量网络来测量包括PM10的空气污染。然而，空气质量监测网络具有高成本，其空间有限。厄瓜多尔的资本基多是一个由9个站组成的自动空气质量网络（Remmaq）的城市。雷米克自2002年起作用，仅在沿着城市不同点的4个普通站中衡量PM10。这种稀缺数量的PM10措施使我们提出了新的策略来获得所有城市的PM10数据。几项研究已经考虑了从具有空气质量网络的城市遥感数据检索PM10。为了找到最佳模型来检索在基多的PM10，这项研究比较了2013到2017之间的3种不同卫星传感器（Landsat-7 Etm +，Landsat-8 Oli和Terra / Modis）的使用。遥感数据，我们考虑到remmaq也使用现场数据。在每个传感器中，我们使用不同的变量和环境索引来模拟最佳拟合方程来量化所有城市的PM10，为每种类型的数据和年份找到重要变量。所考虑的变量是归一化差异植被指数（NDVI），土地表面温度（LST），土壤调整后植被指数（SAVI），归一化差异水指数（NDWI），标准化稳定性指数（NSI），表面反射率蓝带（BL） ），表面反射率绿色带（B2）和表面反射红带（B3）。考虑这些变量，因为其中大多数用于不同的研究与气象数据相结合。所有程序都在R工作室实施。使用遥感数据/衍生产品和空气质量数据的经验模型可以有助于检索大城市的空气污染物。这项工作是对PM10的空间化研究的宝贵贡献，以便在使用遥感数据时寻找新的替代方案来支持政府决定。

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