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A Method to Derive Tidal Flat Topography in Nantong, China Using MODIS Data and Tidal Levels

机译:利用MODIS数据和潮汐水平,南通拓展平板地形的一种方法

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摘要

Multispectral remote sensing data have proven to be useful in deriving tidal flat topography. However, limited satellite observations over a certain period have large uncertainties. In this study, we used MODIS time-series data with a high observational frequency to generate accurate tidal flat topography in Nantong, China based on the relationship between the T_Tide-derived tidal levels and the MODIS-derived inundation frequency map. First, 8-day MOD09Q1 data from 2007 to 2008 were used to perform the land-water classification. Second, 92 land-water maps were stacked to generate the inundation frequency map of the tidal flat. Then, the T_Tide package was applied to calculate the tidal levels at Lvsi Tide Gauge Station. Finally, the inundation frequency map and the tidal levels were integrated to derive the tidal flat topography, which agreed well with the in-situ elevation data (RMSE = 0.40 m, r=0.89) and the Landsat-based elevation data (RMSE = 0.18 m, r=0.98). In addition, the derived slopes agreed well with the slopes from the in-situ elevation data (RMSE = 1.00‰, r=0.85). We highlighted the necessity of using all MODIS data for deriving an accurate tidal flat topography. Our proposed method has a potential to derive tidal flat topography and temporal changes over the past 20 years from MODIS data.
机译:多光谱遥感数据已被证明在推导潮汐平面的地形方面是有用的。然而,某个时期的有限卫星观测有很大的不确定性。在这项研究中,我们使用具有高观察频率的MODIS时间序列数据,基于T_tiDe衍生的潮汐级和Modis衍生的淹没频率图之间的关系,在南通产生准确的潮汐平面。首先,2007年至2008年的8天Mod09Q1数据用于执行土地水分类。其次,堆叠92层陆地图以产生潮汐平的淹没频率图。然后,应用T_tiDe包以计算LVSI潮汐仪表站的潮汐级。最后,淹没频率图和潮汐水平被整合以导出潮汐平地形,这与原位高程数据相同(RMSE = 0.40米,R = 0.89)和基于Landsat的高程数据(RMSE = 0.18 m,r = 0.98)。此外,衍生的斜率与原位高程数据的斜率很好地同意(RMSE = 1.00‰,r = 0.85)。我们强调了使用所有MODIS数据的必要性,以导出准确的潮汐平面图。我们所提出的方法有可能从Modis数据到过去20年的潮汐平面和时间变化。

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  • 来源
    《Canadian Journal of Remote Sensing》 |2021年第1期|17-32|共16页
  • 作者单位

    College of Marine Science and Engineering Nanjing Normal University Nanjing China;

    Qian Xuesen Laboratory of Space Technology China Academy of Space Technology Beijing China;

    College of Marine Science and Engineering Nanjing Normal University Nanjing China;

    College of Marine Science and Engineering Nanjing Normal University Nanjing China;

    School of Electronic Information Wuhan University Wuhan China;

    School of Science University of New South Wales Canberra Australia Sino Australian Research Consortium for Coastal Management University of New South Wales Canberra Australia;

    College of Marine Science and Engineering Nanjing Normal University Nanjing China;

    College of Marine Science and Engineering Nanjing Normal University Nanjing China;

    College of Marine Science and Engineering Nanjing Normal University Nanjing China;

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  • 正文语种 eng
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