• 遥感科学与应用技术 •

### 基于GSM01融合的多传感器数据叶绿素a浓度反演

1. 福州大学空间数据挖掘与信息共享教育部重点实验室, 福建省空间信息工程研究中心, 福州 350002
• 收稿日期:2013-08-15 修回日期:2013-11-06 出版日期:2013-12-25 发布日期:2013-12-25
• 作者简介:陈芸芝（1982-），女，福建连江人，博士，助理研究员，研究方向为资源与环境遥感。E-mail：chenyunzhi@fzu.edu.cn
• 基金资助:

福建省自然科学基金项目（2012J01166）；福建省科技计划重点项目（2012Y0047）；福建省教育厅科技项目（JA12022）；福州大学科研启动项目（022453）。

### Retrieval of Chlorophyll a Concentration with Multi-sensor Data by GSM01 Merging Algorithm

CHEN Yunzhi, ZHENG Gaoqiang, WANG Xiaoqin, CHEN Xi

1. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350002, China
• Received:2013-08-15 Revised:2013-11-06 Online:2013-12-25 Published:2013-12-25

Abstract:

Chlorophyll a concentration, which can be routinely measured by ocean color remote sensing at large scale, is one of the most important indicators to evaluate water quality. The standard inversion algorithms based on empirical model, however, often overestimate chlorophyll a concentration in case Ⅱ waters. After tuning key parameters of a typical semi-analytical model called GSM01 (Garver-Siegel-Maritorena-01), multi-sensor reflectance data of East China Sea acquired on May 11, 2008, which were from Aqua MODIS, Terra MODIS and SeaWiFS, were merged together to retrieve chlorophyll a concentration. The retrieved result was compared with that of the adaptive weighted averaging method and validated by field survey data. Result showed that retrieval of chlorophyll a concentration with multi-sensor data by GSM01 merging algorithm has four advantages: (1) the range of the retrieved values with GSM01 was basically consistent with the in-situ measurements. Because the influence of water's absorption and scattering on remote sensing reflectance was taken into account by GSM01 model, overestimation of chlorophyll a concentration due to high concentrated suspended particulates near the coast was thus avoided. (2) More input bands (from original 6 for single senor to final 18 for multi-sensor in this case) were involved in the merging procedure, the freedom degree of model solution as well as the accuracy of the retrieval was thus improved. In addition, the spatial consistency of the result is ensured by minimizing root mean square error between the measured values and the retrieved values from different remote sensing data source. (3) Instead of merging the chlorophyll a concentration data, which were input of the adaptive weighted averaging method, the GSM01-based method directly merges the original ocean color remote sensing reflectance data, which can better prevent the propagation of error. And (4) the GSM01-based method shows more flexibility because the input reflectance bands can be specified as required.