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New strategy to improve estimation of diffuse attenuation coefficient for highly turbid inland waters

机译：改善内陆浑浊水域扩散衰减系数估算的新策略

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摘要

The diffuse attenuation coefficient, K_d(λ), is an important water optical property. Detection of K_d(λ) by means of remote sensing can provide significant assistance in understanding water environment conditions and many biogcochemical processes. Even when existing algorithms exhibit good performance in clear open ocean and turbid coastal waters, accurate quantification of highly turbid inland water bodies can still be a challenge due to their bio-optical complexity. In this study, we examined the performance of two typical pre-existing K_d(490) models in inland water bodies from Lake Taihu, Lake Chaohu, and the Three Gorges Reservoir in China. On the basis of water optical classification, new K_d(490) models were developed for these waters by means of the support vector machine approach. The obtained results showed that the two pre-existing K_d(490) models presented relatively large errors by comparison with the new models, with mean absolute percentage error (MAPE) values above ～30%. More importantly, among the new models, type-specific models generally outperformed the aggregated model. For water classified as Type 1 + Type 2, the type-specific model produced validation errors with MAPE = 16.8% and RMSE = 0.98 m~(-1). For water classified as Type 3, the MAPE and RMSE of the type-specific model were found to be 18.8% and 1.85 m~(-1), respectively. The findings in this study demonstrate that water classification (prior to algorithm development) is needed for the development of excellent K_d(490) retrieval algorithms, and the type-specific models thus developed are an important supplement to existing K_d(490) retrieval models for highly turbid inland waters.
机译：漫射衰减系数K_d（λ）是重要的水光学特性。通过遥感检测K_d（λ）可以为理解水环境条件和许多生物化学过程提供重要的帮助。即使当现有算法在清澈的海洋和浑浊的沿海水域中表现出良好的性能时，由于其生物光学的复杂性，对高度浑浊的内陆水体进行准确的量化仍然是一个挑战。在这项研究中，我们研究了两个典型的K_d（490）模型在太湖，巢湖和中国三峡水库的内陆水体中的性能。在水光学分类的基础上，通过支持向量机方法为这些水开发了新的K_d（490）模型。所得结果表明，两个现有的K_d（490）模型与新模型相比存在较大的误差，平均绝对百分比误差（MAPE）值在〜30％以上。更重要的是，在新模型中，特定于类型的模型通常优于聚合模型。对于分类为类型1 +类型2的水，特定类型的模型产生验证误差，MAPE = 16.8％，RMSE = 0.98 m〜（-1）。对于分类为3类的水，该类型特定模型的MAPE和RMSE分别为18.8％和1.85 m〜（-1）。这项研究的结果表明，开发出色的K_d（490）检索算法需要水分类（在算法开发之前），因此开发的类型特定模型是现有K_d（490）检索模型的重要补充。浑浊的内陆水域。

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  • 来源
    《International journal of remote sensing》 |2014年第10期|3350-3371|共22页
  • 作者

    Deyong Sun; Zhongfeng Qiu; Yunmei Li; Kun Shi; Changchun Huang; Shaoqi Gong;

  • 作者单位

    Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, JiangSu Nanjing, China,School of Marine Sciences, Nanjing University of Information Science & Technology, JiangSu Nanjing, China;

    Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, JiangSu Nanjing, China,School of Marine Sciences, Nanjing University of Information Science & Technology, JiangSu Nanjing, China;

    Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, JiangSu Nanjing, China;

    State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, JiangSu Nanjing, China;

    Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, JiangSu Nanjing, China;

    Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science & Technology, JiangSu Nanjing, China,College of Remote Sensing, Nanjing University of Information Science & Technology, JiangSu Nanjing, China;

  • 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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