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首页> 外文期刊>International journal of remote sensing >Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-α using Landsat TM/ETM+ over turbid waters
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Effects of sediments and coloured dissolved organic matter on remote sensing of chlorophyll-α using Landsat TM/ETM+ over turbid waters

机译：Landsat TM / ETM +在浑浊水域中沉积物和有色溶解有机物对叶绿素-α遥感的影响

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摘要

Remote sensing of chlorophyll-a is challenging in water containing inorganic suspended sediments (i.e. non-volatile suspended solids, NVSS) and coloured dissolved organic matter (CDOM). The effects of NVSS and CDOM on empirical remote-sensing estimates of chlorophyll-a in inland waters have not been determined on a broad spatial and temporal scale. This study evaluated these effects using a long-term (1989-2012) data set that included chlorophyll-a, NVSS, and CDOM from 39 reservoirs across Missouri (USA). Model comparisons indicated that the machine-learning algorithm BRT (boosted regression trees, validation Nash-Sutcliffe coefficient = 0.350) was better than linear regression (validation Nash-Sutcliffe coefficient = 0.214) for chlorophyll-a estimate using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery. Only a small proportion of BRT model residuals could be explained by sediments or CDOM, and the observed trends in BRT residuals were different from the theoretical effects expected from NVSS and CDOM. Our results also indicated a small systematic bias by the BRT model, but it was not likely caused by NVSS or CDOM.
机译：在含有无机悬浮沉淀物（即非挥发性悬浮固体，NVSS）和有色溶解有机物（CDOM）的水中，对叶绿素-a的遥感具有挑战性。 NVSS和CDOM对内陆水体中叶绿素a经验遥感估计的影响尚未在广泛的时空尺度上确定。这项研究使用长期（1989-2012）数据集（包括美国密苏里州39个水库的叶绿素a，NVSS和CDOM）评估了这些影响。模型比较表明，使用Landsat Thematic Mapper（TM）和增强的专题Mapper Plus（ETM +）图像。沉积物或CDOM只能解释一小部分的BRT模型残差，并且观察到的BRT残差趋势与NVSS和CDOM的理论影响是不同的。我们的结果还表明，BRT模型存在较小的系统偏差，但这不太可能是由NVSS或CDOM引起的。

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  • 来源
    《International journal of remote sensing》 |2018年第6期|1421-1440|共20页
  • 作者

    Lin Shengpan; Qi Jiaguo; Jones John R.; Stevenson R. Jan;

  • 作者单位

    Michigan State Univ, Dept Integrat Biol, E Lansing, MI 48824 USA;

    Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA;

    Univ Missouri, Sch Nat Resources, Columbia, MO USA;

    Michigan State Univ, Dept Integrat Biol, E Lansing, MI 48824 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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