基于MERSI和MODIS的太湖水体叶绿素a含量反演

doi:10.11821/yj2011020009

地理研究 ›› 2011, Vol. 30 ›› Issue (2): 291-300.doi: 10.11821/yj2011020009

基于MERSI和MODIS的太湖水体叶绿素a含量反演

韩秀珍, 郑伟, 刘诚, 安思颖

中国气象局国家卫星气象中心,北京 100081

收稿日期:2010-07-22 修回日期:2010-11-18 出版日期:2011-02-20 发布日期:2011-02-20
作者简介:韩秀珍 (1974-),女,辽宁阜新人,博士,副研,主要从事卫星遥感反演和GIS等方面的研究。 E-mail:hanxz@cma.gov.cn
基金资助:
基于风云三号数据的太湖蓝藻水华监测模型研究(40801176/D010702);浙江省科技厅项目(2009C33071)

Estimation of chlorophyll-a using MERSI and MODIS images in Taihu Lake, China

HAN Xiu-zhen, ZHENG Wei, LIU Cheng, AN Si-ying

National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China

Received:2010-07-22 Revised:2010-11-18 Online:2011-02-20 Published:2011-02-20

摘要/Abstract

摘要： 水体叶绿素a含量的遥感反演是监测水体光学特性、评价水体污染的一个重要指标。本文以FY-3A/MERSI和AQUA/MODIS遥感影像为数据源,结合水体实测的叶绿素a含量,利用两类反射率模型,研究星载数据遥感反演叶绿素a的可行性。研究表明:基于FY-3A/MERSI和AQUA/MODIS可见光-近红外通道的光谱反演模型(R_λ₁^-1-R_λ₂^-1)×R_λ₃和R_λ₁^-1×R_λ₃在太湖水体叶绿素a含量反演方面取得了较高的精度。基于MERISI通道的模型反演相关系数R²分别在0.60和0.72左右,基于MODIS通道的模型反演相关系数R²分别在0.55和0.65左右。通过比较分析,决定叶绿素a含量反演精度的主要因素包括两个方面,一是通道位置,即蓝波段和近红外波段是叶绿素a反演的敏感波段;其次,卫星空间分辨率,即较高的空间地面分辨率改善了混合像元。因此,MERSI比MODIS对应模型获得了较高的叶绿素a反演精度。这一结果将有助于FY-3A/MERSI遥感数据在环境监测和水体污染领域的进一步研究,为国产卫星的应用提供一定的参考。

关键词: FY-3A/MERSI, AQUA/MODIS, 叶绿素a含量, 卫星遥感

Abstract: The Chlorophyll-a (Chl-a) concentrations in water are of great importance to the monitoring of water quality and ecosystem balance. Remote sensing offers a convenient and systematical tool for the observations of water at a long time scale. In this paper, we present a study of Chl-a estimation using the reflectance models (MOD3: R_λ₁^-1-R_λ₂^-1)×R_λ₃and MOD2:R_λ₁^-1×R_λ₃) derived from the Medium Resolution Spectral Image (MERSI) onboard the newly launched FY-3A satellite and the Moderate Imaging Spectroradiometer (MODIS) onboard the AQUA platforms. Validation studies demonstrated that both models provided reliable estimates of Chl-a concentrations with determination coefficients R² of 0.72~0.79 (MOD2) and 0.52~0.76 (MOD3) for MERSI standard band settings. This accuracy is slightly better than that of the MODIS results with R² of 0.65~0.69 and 0.43~0.70 for MOD2 and MOD3, respectively. Comparison analysis between models and sensors indicated that the blue and near infrared wave ranges are of potential for Chl-a estimation. Besides, the higher spatial resolution of MERSI (250m) may explain the better performance for both models compared to that of MODIS. This research will be helpful for the development of future Chl-a estimation models using the satellite observations.

Key words: MERSI, MODIS, Chl-a, remote sensing

韩秀珍, 郑伟, 刘诚, 安思颖. 基于MERSI和MODIS的太湖水体叶绿素a含量反演[J]. 地理研究, 2011, 30(2): 291-300.

HAN Xiu-zhen, ZHENG Wei, LIU Cheng, AN Si-ying. Estimation of chlorophyll-a using MERSI and MODIS images in Taihu Lake, China[J]. GEOGRAPHICAL RESEARCH, 2011, 30(2): 291-300.

参考文献

[1] Reinart A, Kuster T. Comparison of different satellite sensors in detecting cyanobacterial bloom events in the Baltic Sea. Remote Sensing of Environment, 2006,102:74~85.

[2] Carmichael W W. Health effects of toxin-producing cyanobacteria: "The CyanoHABs". Human and Ecological Risk Assessment, 2001,(7): 1393~1407.

[3] 刘付程,史学正,于东升,等.太湖流域典型地区土壤全氮的空间变异特征. 地理研究, 2004, 23 (1) : 63~70.

[4] Moses W J, Gitelson A A, Berdnikov S, et al. Satellite estimation of chlorophyll-a concentration using the red and NIR Bands of MERIS:The Azov Sea case study. IEEE Geoscience and Remote Sensing Letters, 2009,(6):845~849.

[5] 叶晶, 李万彪, 严卫. 利用MODIS数据反演多层云光学厚度和有效粒子半径.气象学报, 2009,67 (4): 613~622.

[6] Gordan H R, Brown O B, Evans R H, et al. A semianalytic radiance model of ocean color. Journal of Geophysical Research, 1988,93: 10909~10924.

[7] Mittenzwey K H, Ullrich S, Gitelson A A, et al. Determination of chlorophyll-a of inland waters on the basis of spectral reflectance. Limnology and Oceanography, 1992,37: 147~149.

[8] Dekker A. Detection of the optical water quality parameters for eutrophic waters by high resolution remote sensing. Ph.D. Thesis, Free University, Amsterdam, The Netherlands, 1993.

[9] Han L, Rundquist D. Comparison of NIR/RED ratio and first derivative of reflectance in estimating algal-chlorophyll concentration: A case study in a turbid reservoir. Remote Sensing of Environment, 1997,62:253~261.

[10] 吕恒,江南,罗潋葱. 基于TM 数据的太湖叶绿素a 浓度定量反演.地理科学, 2006,26 (4): 472~476.

[11] 荀尚培, 翟武全, 范伟. MODIS巢湖水体叶绿素a浓度反演模型. 应用气象学报, 2009,20 (1): 95~101.

[12] Dall'olmo G, Gitelson A A, Rundquist D C, et al. Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands. Remote Sensing of Environment, 2005,96:176~187.

[13] Gitelson A A, Vina A, Arkebauer T J, et al. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letter, 2003: 30, 1248, doi:10.1029/2002GL016450.

[14] Gitelson A A, Schalles J F, Hladik C M. Remote chlorophyll-a retrieval in turbid, productive estuaries: Chesapeake Bay case study. Remote Sensing of Environment, 2007,109:464~472.

[15] Gitelson A A, Dall'olmo G, Moses W, et al. A simple semi-analytical model for remote estimation of chlorophyll-a in in turbid waters: Validation. Remote Sensing of Environment, 2008,112:3582~3593.

[16] Moses W J, Gitelson A A, Berdnikov S, et al. Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data-successes and challenges. Environment Research Letter, 2009,(4):045005.

[17] Kucharik C J, Norman J M, Gower S T. Measurements of branch area and adjusting leaf area index indirect measurements. Agricultural and Forest Meteorology, 1998,91:69~88.

[18] Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 2002,81:45~57.

基于MERSI和MODIS的太湖水体叶绿素a含量反演

Estimation of chlorophyll-a using MERSI and MODIS images in Taihu Lake, China

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