基于4次黄渤海航次实测资料(2014年11月、2015年8月、2016年7月和2017年1月)建立了采用遥感反射率反演海表密度(SSD)的遥感模型。结果表明,多元线性回归模型的反演效果最佳,其决定系数为0.70,平均绝对误差(S_MAPE)为3.49%;采用独立实测数据集(27个样本)对所提模型进行了精度验证,该模型反演的海表密度具有较高的精度(S_MAPE为3.27%)。此外,模型敏感性实验结果显示,S_MAPE波动在3%以内,证实所提模型具有较好的稳定性。同时,将所提模型成功应用于静止水色卫星(GOCI)的卫星数据,并反演得到2016年7月的黄渤海海表密度的空间分布结果。结果表明,沿岸地区密度较大,渤海中心区和山东半岛北部海域、黄海中部海域均呈现较高值,青岛附近海域均呈现低值。

On the basis of our study of 55 samples collected during four cruises in the Yellow and Bohai Seas off the east coast of China (November 2014, August 2015, July 2016, and January 2017), we developed an algorithm for estimating the sea-surface density (SSD) using remote-sensing reflectance. Our results show that the multivariate linear regression model performs the best, with a determination coefficient of 0.70 and a mean absolute percentage error of SMAPE=3.49%. We used an independent dataset (27 in situ observations) to assess the performance of the model, yielding a validation result of SMAPE=3.27%. In addition, the sensitivity experiment of the model show that the observed fluctuation in the SMAPE values is <3%, indicating that our proposed model is relatively stable. Meanwhile, we applied our developed model to the geostationary ocean color imager (GOCI) satellite data recorded in July 2016 and successfully produced the SSD distribution pattern. The spatial characteristics show that the coastal waters, the central parts of the Bohai and Yellow Seas, and the waters off the northern Shandong Peninsula have relatively high SSD values, while relatively low values are distributed along the Qingdao coast.