题目：“An empirical algorithm to seamlessly retrieve the concentration of suspended particulate matter from water color across ocean to turbid river mouths”

报告人：余小龙 博士

时间：2019年11月29日（11:00-12:30）

地点：希平楼C2-404

联系人：闫静（13599929610）

简历：余小龙，博士毕业于荷兰特文特大学，在美国麻省大学波士顿分校工作两年。目前为厦门大学海洋科学流动站博士后。主要研究方向为水体辐射传输模型和海洋水色遥感。近五年内，在《Remote Sensing of Environment》, 《Optics Express》等国际知名期刊以第一作者发表论文4篇（3篇顶刊），以合作作者发表论文2篇。

摘要：

We propose a globally applicable algorithm (GAA_SPM) to seamlessly retrieve the concentration of suspended particulate matter (SPM) (C_SPM) from remote sensing reflectance (R_rs(l)) across ocean to turbid river mouths without any hard-switching in its application. GAA_SPM is based on a calibrated relationship between C_SPM and a generalized index for SPM (GI_SPM) from water color. The GI_SPM is mainly composed of three R_rs(l) ratios (671, 745, and 862 nm over 551 nm, respectively), along with weighting factors assigned to each ratio. The weighting factors are introduced to ensure the progressive application of R_rs(l) in the longer wavelengths for increasing C_SPM. Calibration of GAA_SPM employed data collected from multiple estuarine and coastal regions of Europe, China, Argentina, and the USA with the measured C_SPM spanning from 0.2 to 2068.8 mg/L. Inter-comparison with several recalibrated well-known C_SPM retrieval algorithms demonstrates that GAA_SPM has the best retrieval accuracy over the entire C_SPM range with a relative mean absolute difference (rMAD) of 41.3% (N = 437). This averaged uncertainty in GAA_SPM-derived C_SPM is mostly attributed to the retrievals from less turbid waters where C_SPM < 50 mg/L (rMAD = 50%, N = 214). GAA_SPM was further applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements over prominent coastal areas and produced reliable C_SPM maps along with realistic spatial patterns. In contrast, applications of other C_SPM algorithms resulted in less reliable C_SPM maps with either unjustified numerical discontinuities in the C_SPM spatial distribution or unsatisfactory retrieval accuracy. Therefore, we propose GAA_SPM as a preferred algorithm to retrieve C_SPM over regions with a wide range of C_SPM, such as river plume areas.