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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Statistical Models of Sea Surface Salinity in the South China Sea Based on SMOS Satellite Data
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Statistical Models of Sea Surface Salinity in the South China Sea Based on SMOS Satellite Data

机译：基于SMOS卫星数据的南海海域盐度统计模型

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摘要

Study of sea surface salinity (SSS) plays an important role in the marine ecosystem, estimation of global ocean circulation and observation of fisheries, aquaculture, coral reef, and sea grass habitats. Three statistical methods applied without considering the physical effects of the input parameters are proposed to calculate SSS from soil moisture and ocean salinity (SMOS)-measured brightness temperature (TB) values and associated auxiliary data. Using these three statistical methods, named multiple linear regression (MLR) model, principal component regression (PCR) model, and quadratic polynomial regression (QPR) model, the first predictions of daily and monthly averaged SSS are made with $1 ^circtimes1 ^circ$ spatial resolution in the South China Sea (SCS, in the study area of 4°N-25 °N, 105°E-125°E) during the period between April and June 2013. Results are compared with the corresponding SMOS SSS products and Aquarius SSS products and validated using Argo measurements. Validation results show that the root-mean-squared error (RMSE) of the QPR model is around 0.46 practical salinity units (psu) compared to 0.58 psu for Aquarius daily SSS products. World Ocean Atlas (WOA13) SSS data are also used for validation in the SCS and the QPR model gives a 0.54-psu value of RMSE, which may be compared with 0.69 psu, 0.73 psu for SMOS and Aquarius Level-3 (L3) SSS products, respectively.
机译：海面盐度（SSS）的研究在海洋生态系统，全球海洋环流估计以及渔业，水产养殖，珊瑚礁和海草栖息地的观测中起着重要作用。提出了三种不考虑输入参数物理影响的统计方法，以根据土壤水分和海洋盐度（SMOS）测得的亮度温度（TB）值以及相关的辅助数据来计算SSS。使用名为多元线性回归（MLR）模型，主成分回归（PCR）模型和二次多项式回归（QPR）模型的这三种统计方法，对每日和每月平均SSS的第一个预测是使用$ 1 ^ circtimes1 ^ circ $进行的2013年4月至6月期间，南海的空间分辨率（SCS，研究区域4°N-25°N，105°E-125°E）。将结果与相应的SMOS SSS产品和Aquarius SSS产品并使用Argo测量进行了验证。验证结果表明，QPR模型的均方根误差（RMSE）约为0.46实用盐度单位（psu），而Aquarius日用SSS产品的均方根误差为0.58 psu。世界海洋地图集（WOA13）的SSS数据也用于SCS中的验证，QPR模型给出的RMSE值为0.54 psu，可以与0.69 psu，SMOS的0.73 psu和水瓶座3级（L3）SSS进行比较产品。

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