基于GSM01融合的多传感器数据叶绿素a浓度反演
收稿日期: 2013-08-15
修回日期: 2013-11-06
网络出版日期: 2013-12-25
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
Received date: 2013-08-15
Revised date: 2013-11-06
Online published: 2013-12-25
叶绿素a浓度是水质状况评价的一个重要指标，而遥感是大面积反演叶绿素a浓度的重要手段。由于采用基于经验模型的标准算法对二类水体叶绿素a浓度的反演值往往偏高，因此本文基于半分析模型GSM01（Garver-Siegel-Maritorena-01），在对模型参数进行调节的基础上，对东海2008年5月11日Aqua MODIS、Terra MODIS、SeaWiFS 3种传感器各波段遥感反射率进行融合，来反演叶绿素a浓度，并将反演结果与自适应加权平均算法获得的叶绿素a浓度数据进行对比。结果表明，基于GSM01融合的多传感器叶绿素a浓度反演，拥有4个优势：（1）GSM01模型反演叶绿素a浓度值范围更符合实测结果，由于该模型考虑水体各组分的散射吸收特性对光谱反射率的影响，避免因高浓度悬浮物质影响造成的近岸水体叶绿素a浓度过高问题；（2）通过融合多传感器反射率数据，用于叶绿素a浓度反演的波段从6个增至18个，光谱信息变丰富，模型求解的自由度提高，叶绿素a浓度反演的精度提高。模型通过误差最小化准则，将不同传感器反演的差异降至最小，保证反演结果的空间连续性；（3）与自适应加权平均采用的融合策略不同，GSM01模型直接利用各传感器遥感反射率数据进行融合而不是针对叶绿素a浓度数据进行融合，避免了误差的传递；（4）GSM01模型可自由组合输入的反射率数据，具有更强的灵活性。
陈芸芝, 郑高强, 汪小钦, 陈曦 . 基于GSM01融合的多传感器数据叶绿素a浓度反演[J]. 地球信息科学学报, 2013 , 15(6) : 911 -917 . DOI: 10.3724/SP.J.1047.2013.00911
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.
Key words： GSM01; data merging; inversion; chlorophyll-a concentration
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