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首页> 外文期刊>Geo-spatial information science >Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations
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Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations

机译：使用OLI和TM影像在不同情况下比较不同经典水指数的地表水提取性能

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摘要

Recently, water extraction based on the indices method has been documented in many studies using various remote sensing data sources. Among them, Landsat satellites data have certain advantages in spatial resolution and cost. After the successful launch of Landsat 8, the Operational Land Imager (OLI) data from the satellite are getting more and more attention because of its new improvements. In this study, we used the OLI imagery data source to study the water extraction performance based on the Normalized Difference Vegetation Index, Normalized Difference Water Index, Modified Normalized Water Index (MNDWI), and Automated Water Extraction Index (AWEI) and compared the results with the Thematic Mapper (TM) imagery data. Two test sites in Tianjin City of north China were selected as the study area to verify the applicability of OLI data and demonstrate its advantages over TM data. We found that the results of surface water extraction based on OLI data are slightly better than that based on TM in the two test sites, especially in the city site. The AWEI and MNDWI indices performs better than the other two indices, and the thresholds of water indices show more stability when using the OLI data. So, it is suitable to combine OLI imagery with other Landsat sensor data to study water changes for long periods of time.
机译：近来，在使用各种遥感数据源的许多研究中已经记录了基于指标法的水提取。其中，Landsat卫星数据在空间分辨率和成本上具有一定优势。在成功发射Landsat 8之后，由于其新的改进，来自卫星的Operational Land Imager（OLI）数据越来越受到关注。在这项研究中，我们使用OLI图像数据源基于归一化植被指数，归一化差异水指数，修正归一化水指数（MNDWI）和自动提取水指数（AWEI）来研究水提取性能，并比较了结果主题映射器（TM）图像数据。选择了华北地区天津市的两个测试点作为研究区域，以验证OLI数据的适用性并证明其优于TM数据。我们发现，在两个测试地点，特别是在城市地点，基于OLI数据的地表水提取结果略好于基于TM的地表水提取结果。 AWEI和MNDWI指数的表现优于其他两个指数，而使用OLI数据时，水指数的阈值显示出更高的稳定性。因此，将OLI影像与其他Landsat传感器数据相结合以研究长时间的水变化是合适的。

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