DOI: 10.3724/SP.J.1047.2013.00911

Journal of Geo-information Science (地球信息科学学报) 2013/15:6 PP.911-917

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

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:chlorophyll-a concentration,GSM01,data merging,inversion

ReleaseDate:2015-04-17 13:34:35

[1] 杨煜, 李云梅, 王桥, 等.富营养化的太湖水体叶绿素a浓度模型反演[J].地球信息科学学报, 2009, 11(5):5597-5603.

[2] 丘仲锋, 崔廷伟, 何宜军.基于水体光谱特性的赤潮分布信息MODIS遥感提取[J].光谱学与光谱分析, 2011, 31(8):2233-2237.

[3] 文斐, 孙晓霞, 郑珊, 等.2011年春、夏季黄、东海叶绿素a和初级生产力的时空变化特征[J].海洋与湖沼, 2012, 43(3):438-444.

[4] 曲利芹, 管磊, 贺明霞.Sea WiFS和MODIS叶绿素浓度数据及其融合数据的全球可利用率[J].中国海洋大学学报(自然科学版), 2006, 36(2):321-326.

[5] Gregg W. Ocean-colour data merging[R]. Dartmouth, Canada: Reports of the International Ocean-Colour Coordinating Group (IOCCG), 2007.

[6] Kwiatkowska E J, Fargion G S. Application of machine-learning techniques toward the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(12):2844-2860.

[7] Pottier C, Garcon V, Larnicol G, et al. Merging SeaWiFS and MODIS/aqua ocean color data in North and Equatorial Atlantic using weighted averaging and objective analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11):3436-3450.

[8] Melin F, Zibordi G and Djavidnia S. Merged series of normalized water leaving radiances obtained from multiple satellite missions for the Mediterranean Sea[J]. Advances in Space Research, 2009, 43(3):423-437.

[9] Zubko V, Leptoukh G G, Gopalan. A study of data-merging and interpolation methods for use in an interactive online analysis system: MODIS terra and aqua daily aerosol case[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12):4219-4235.

[10] Saulquin B, Gohin F, Garrello R. Regional objective analysis for merging high-resolution MERIS, MODIS/Aqua, and SeaWiFS Chlorophyll-a data from 1998 to 2008 on the European Atlantic Shelf[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1):143-154.

[11] Maritorena S, Siegel D A, Peterson A R. Optimization of a semianalytical ocean color model for global-scale applications[J]. Applied Optics, 2002, 41(15):2705-2714.

[12] Maritorena S, Siegel D A. Consistent merging of satellite ocean color data sets using a bio-optical model[J]. Remote Sensing of Environment, 2005, 94(4):429-440.

[13] Maritorena S, d'Andon O H F, Mangin A, et al. Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues[J]. Remote Sensing of Environment, 2010, 114(8):1791-1804.

[14] Kahru M, Lee Z, Kudela R M, et al. Multi-satellite time series of inherent optical properties in the California Current[J]. Deep Sea Research Part Ⅱ: Topical Studies in Oceanography (to be published),

[15] 汪文琦, 董强, 商少凌, 等.基于两种半分析算法的水体吸收系数反演[J].热带海洋学报, 2009, 28(5): 35-42.

[16] 丘仲锋, 席红艳, 何宜军, 等.东海赤潮高发区半分析算法色素浓度反演[J].环境科学, 2006, 27(8):1516-1521

[17] Smith R C, Baker K S. Optical properties of the clearest natural waters (200-800 nm)[J]. Applied Optics, 1981, 20(Compendex):177-184.

[18] Pope R M, Fry E S. Absorption spectrum (380-700 nm) of pure water. Ⅱ. Integrating cavity measurements[J]. Applied Optics, 1997, 36(Compendex):8710-8710.

[19] Kou L, LabrieD, Chylek P. Refractive indices of water and ice in the 0.65 to 2.5 micrometer spectral range[J]. Applied Optics, 1993, 32(Compendex):3531-3540.

[20] 宋庆君, 唐军武.黄海、东海海区水体散射特性研究[J].海洋学报, 2006, 28(4):56-63.

[21] 陈芸芝, 汪小钦, 吴波, 等.基于自适应加权平均的水色遥感数据融合[J].遥感技术与应用, 2012, (3):333-338.

[22] 雷惠, 潘德炉, 陶邦一, 等.东海典型水体的黄色物质光谱吸收及分布特征[J].海洋学报, 2009, 31(2):57-62.

[23] 张民伟, 董庆, 唐军武, 等.基于表观光谱反演黄东海水体固有光学量研究[J].光谱学与光谱分析, 2011, 31(5):1403-1408.

[24] 周伟华, 霍文毅, 袁翔城, 等.东海赤潮高发区春季叶绿素a和初级生产力的分布特征[J].应用生态学报, 2003, 14(7):1055-1059.

[25] 文斐, 孙晓霞, 郑珊, 等.2011年春、夏季黄、东海叶绿素a和初级生产力的时空变化特征[J].海洋与湖沼, 2012, 43(5):438-444.

[26] 朱建荣. 长江口外海区叶绿素a浓度分布及其动力成因分析[J].中国科学D辑, 2004, 34(8):757-762.