基于MERIS数据的渤海海表盐度反演

盐度是重要的物理海洋参数，会影响海洋许多过程。基于渤海实测的海表盐度和MERIS卫星获取的遥感反射率数据，建立并检验了一种多元线性模型和人工神经网络模型。经检验，两种模型的反演均方根误差分别为0.858 psu和0.689 psu(psu为实用盐度单位)，对应的相关系数分别为R2=0.81和R2=0.82。将模型应用于MERIS遥感数据，获取了渤海海表盐度图，能够体现渤海海表盐度的空间分布情况。两种模型均可适用于多光谱遥感数据。

Abstract

Salinity is an important physical oceanographic parameter which influences many processes in the ocean. A multi-linear regression model and artificial neural network are developed for sea surface salinity and tested against in situ measurements in the Bohai Sea. The models are validated by in situ data. The root mean square errors are 0.858 psu and 0.689 psu (psu stands for practical salinity unit) with the correlation coefficients of R2=0.81 and R2=0.82, respectively. Then the model is applied to MERIS data to derive sea surface salinity map which can generally characterize the spatial pattern of sea surface salinity of the Bohai Sea. Both models are suitable for multi-spectral remote sensing data.

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青松, 张杰, 包玉海. 基于MERIS数据的渤海海表盐度反演[J]. 激光与光电子学进展, 2013, 50(12): 120102. Qing Song, Zhang Jie, Bao Yuhai. Retrieval of Sea Surface Salinity with MERIS in the Bohai Sea[J]. Laser & Optoelectronics Progress, 2013, 50(12): 120102.

基于MERIS数据的渤海海表盐度反演

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