首页> 外文期刊>Journal of Hydrology >Development and application of a remote sensing-based Chlorophyll-a concentration prediction model for complex coastal waters of Hong Kong
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Development and application of a remote sensing-based Chlorophyll-a concentration prediction model for complex coastal waters of Hong Kong

机译:基于遥感的香港复杂沿海水域叶绿素a浓度预测模型的开发与应用

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摘要

Despite recent advances in estimation of water quality parameters using satellite remote sensing, the estimation of Chlorophyll-a (Chl-a) has remained problematic due to optical complexity of coastal waters and imprecise atmospheric correction of imagery. Local environmental agencies require frequent measurement and monitoring of Chl-a over coastal regions at detailed level, for water quality assessment and control. To monitor Chl-a around the complex coastal waters of Hong Kong using remote sensing, 27 Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images over a 13-year period from January 2000 to December 2012, were used, along with 120 in situ Chl-a samples. Atmospherically corrected Landsat TM/ETM+ bands 1-4 along with in situ Chl-a data were used to develop and validate regression models for a Chl-a concentration range of 0.3-13.0 mu g/l. Validation results indicated that the ratio of band 3 (red, 0.63-0.69 mu m) and the square of band 1 (blue, 0.45-0.52 mu m), with correlation coefficient (R) of 0.89, Root Mean Square Error (RMSE) of 2.53 mu g/l and Mean Absolute Error (MAE) of 1.02 mu g/l was most capable of representing actual Chl-a concentrations. This is attributed to the differential response of the red and blue wavebands to the Chl-a signal. The study is considered more robust than previous studies of Chl-a retrieval, due to the much larger number of images and in situ samples used for model development and validation, as well as the different times of year, water quality zones, and wide range of Chl-a concentrations which were investigated. The robustness of the developed model was also tested by its application to monitoring an extensive red tide event. The results indicate that the developed model is capable of routine monitoring of such algal blooms which frequently occur from late summer to early autumn in Hong Kong and its adjacent coastal waters. 2015 Elsevier B.V. All rights reserved.
机译:尽管最近在使用卫星遥感技术估算水质参数方面取得了进展,但由于沿海水域的光学复杂性和图像的大气校正不准确,叶绿素a(Chl-a)的估算仍然存在问题。当地的环保机构要求对沿海地区的Chl-a进行频繁的详细测量和监控,以进行水质评估和控制。为了使用遥感监测香港复杂沿海水域的Chl-a,使用了2000年1月至2012年12月的13年期间的27张Landsat Thematic Mapper(TM)和Enhanced Thematic Mapper Plus(ETM +)图像,以及120个原位Chl-a样品。大气校正的Landsat TM / ETM +条带1-4以及原位Chl-a数据用于开发和验证Chl-a浓度范围为0.3-13.0μg / l的回归模型。验证结果表明,频带3(红色,0.63-0.69μm)与频带1的平方(蓝色,0.45-0.52μm)之比,相关系数(R)为0.89,均方根误差(RMSE) 2.53μg / l的浓度和1.02μg / l的平均绝对误差(MAE)最能代表实际Chl-a浓度。这归因于红色和蓝色波段对Chl-a信号的差分响应。与用于Chl-a检索的先前研究相比,该研究被认为更可靠,这是因为用于模型开发和验证的图像和原位样本数量更多,并且一年中的不同时间,水质区域和范围广泛被调查的Chl-a浓度。还通过将其用于监测广泛的赤潮事件,对开发模型的鲁棒性进行了测试。结果表明,所开发的模型能够常规监测此类藻华,该藻华频繁发生于夏末至秋初,在香港及其附近的沿海水域中。 2015 Elsevier B.V.保留所有权利。

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