光学学报, 2019, 39 (6): 0601002, 网络出版: 2019-06-17   

基于NPP-VIIRS卫星数据的渤黄海浊度反演算法研究 下载: 1061次

Inversion Algorithm for Turbidity of Bohai and Yellow Seas Based on NPP-VIIRS Satellite Data
作者单位
南京信息工程大学海洋科学学院, 江苏 南京 210044
摘要
浊度是水环境和水质状况的重要监测指标。卫星遥感技术具有宏观的空间覆盖性和重复的定期采样性,是一种有效的监测水体浊度的方法。基于NPP-VIIRS卫星的遥感反射率,建立了一个适用于渤黄海的水体浊度遥感反演算法,并将其应用到VIIRS卫星图像上,研究了渤黄海水体浊度的时空分布特征。结果表明:该算法具有较高的反演精度(决定系数R2为0.97,均方根误差为16 NTU,平均绝对误差为23 NTU,平均相对误差为34.63%)。渤黄海水体浊度在空间尺度上基本呈现近岸高、远岸低的分布特征;在时间尺度上,冬季浊度维持在一个较高的水平,春季浊度高值区逐渐收缩, 夏季达到最低,只在沿岸区域仍有较高的浊度,而秋季浊度又逐渐升高。
Abstract
Turbidity is an important indicator for monitoring water environment and water quality, and the satellite remote sensing technology has the advantages of macroscopic spatial coverage and repeated sampling, which is an effective way of monitoring water turbidity. Based on the remote sensing reflectivity of NPP-VIIRS satellite, a water turbidity remote sensing inversion algorithm is developed and applied to the VIIRS satellite data to obtain a long-time series of satellite-derived water turbidity in the Bohai and Yellow seas. The results indicate that the proposed algorithm has a high accuracy with the R2 of 0.97, the root mean square error of 16 NTU, the mean absolute deviation of 23 NTU, and the mean relative error of 34.63%. On a spatial scale, the turbidity distributions are generally high in the near-shore areas and low in the offshore areas. In contrast in the time scale, the water turbidity is at a high level in winter, but the regions with high turbidity shrink in spring. The turbidity is generally at the lowest level in summer and only the coastal waters show high turbidity values. In autumn, the turbidity gradually increases.

丁梦娇, 丘仲锋, 张海龙, 李兆鑫, 毛颖. 基于NPP-VIIRS卫星数据的渤黄海浊度反演算法研究[J]. 光学学报, 2019, 39(6): 0601002. Mengjiao Ding, Zhongfeng Qiu, Hailong Zhang, Zhaoxin Li, Ying Mao. Inversion Algorithm for Turbidity of Bohai and Yellow Seas Based on NPP-VIIRS Satellite Data[J]. Acta Optica Sinica, 2019, 39(6): 0601002.

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