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首页> 外文期刊>International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences >LINEAR SPECTRAL UNMIXING OF SENTINEL-3 IMAGERY FOR URBAN LAND COVER - LAND SURFACE TEMPERATURE (LST) ANALYSIS: A CASE STUDY OF METRO MANILA, PHILIPPINES
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LINEAR SPECTRAL UNMIXING OF SENTINEL-3 IMAGERY FOR URBAN LAND COVER - LAND SURFACE TEMPERATURE (LST) ANALYSIS: A CASE STUDY OF METRO MANILA, PHILIPPINES

机译：Sentinel-3图像的线性光谱解密，用于城市覆盖 - 陆地表面温度（LST）分析 - 以菲律宾地铁马尼拉的一个案例研究

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The advancement of remote sensing technologies is a huge advantage in various environmental applications including the monitoring of the rapid development in an urban area. This study aims to estimate the composition of the different classes (vegetation, impervious surfaces, soil) in Metro Manila, Philippines using a 300-meter spatial resolution Sentinel-3 Ocean and Land Colour Instrument image. The relationship between these land cover fractions with the spatial distribution of land surface temperature at this scale is evaluated. Sentinel-3 image has a higher spectral resolution (i.e. 21 bands), as compared with other Landsat and Sentinel missions, which is a requirement for an accurate cover mapping. Linear Spectral Unmixing (LSU), a sub-pixel classification method, was employed in identifying the fractional components in the image based on their spectral characteristics. Field survey using spectroradiometer was conducted to acquire spectral signatures of an impervious surface, vegetation, and soil which were used as the endmembers in the unmixing process. To assess the accuracy of the resulting vegetation fractional image, this was compared with a separate land cover pixel-based classification result using a 3-meter high spatial resolution PlanetScope image and with another vegetation index product of Sentinel-3. The results indicate that the recently available Sentinel-3 image can accurately estimate vegetation fraction with R2 = 0.84 and 0.99, respectively. In addition, the land surface temperature (LST) retrieved from Climate Engine is negatively correlated with the vegetation fraction cover (R2 = 0.81) and positively correlated with the impervious surface fraction cover (R2 = 0.66). Soil, on the other hand, has no correlation with the LST.
机译：遥感技术的进步是各种环境应用中的巨大优势，包括监测城市地区的快速发展。本研究旨在利用300米空间分辨率哨路-3海洋和土地彩仪形象估算菲律宾地铁马尼拉地铁马尼拉不同课程（植被，不受植被，不受植被，抗渗，土壤）的组成。评估了这种陆地覆盖级分，在该规模处具有陆地温度的空间分布的关系。与其他Landsat和Sentinel任务相比，Sentinel-3图像具有更高的光谱分辨率（即21条带），这是一种准确的封面映射的要求。用于基于其光谱特性，采用线谱解密（LSU），亚像素分类方法识别图像中的分数分量。进行了使用光谱辐射计的现场测量，以获取不透水表面，植被和土壤的光谱签名，该植被和土壤被用作未混凝剂过程中的终端。为了评估所得到的植被分数图像的准确性，将其与使用3米高的空间分辨率的平面图图像和Sentinel-3的另一个植被指数产品进行比较的基于土地覆盖像素的分类结果。结果表明，最近可获得的哨子-3图像可以分别准确地估计植被级分，分别用R2 = 0.84和0.99进行。另外，从气候发动机检出的陆表面温度（LST）与植被分数盖（R2 = 0.81）负相关，并且与不透水表面分数盖（R2 = 0.66）呈正相关。另一方面，土壤与LST没有相关性。

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