Abstract：Accurate maps of the spatial and temporal dynamics of cyanobacteria blooms are urgently needed in the Taihu Lake, which is a drinking water resource for cities around the lake. Satellite imagery can be used as a cost-effective method for remotely monitor trends in cyanobacteria blooms. However, atmospheric effects and sun-target-satellite geometry can make multi-temporal images of blooms inconsistent with each other and cause uncertainties in bloom data extraction. In this paper, four remote sensing approaches were applied to retrieve cyanobacteria bloom information in the Taihu Lake during the whole year of 2006. These approaches included the near infrared (NIR) single band data, the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), and the normalized difference water index(NDWI). Two kinds of MODIS (moderate-resolution imaging spectroradiometer) products, i.e., the top-of-atmosphere (TOA) radiance images without atmospheric correction (MOD02) and the surface reflectance images with atmospheric correction (MOD09), were selected as the data source. Furthermore, three factors comprising the aerosol optical thickness (AOT), the solar zenith angle, and the sensor zenith angle were chosen as indicators of radiation transfer processes to evaluate their influence on the remote sensing indices during the extraction of cyanobacteria bloom information. Specifically, the relationships between retrieval threshold values and the three indicators were analyzed to evaluate the temporal influences quantitatively. The results showed that: ① these three factors had more impact on NIR single band data and the NDWI, and less impact on the RVI and NDVI (RVI was less sensitive than NDVI in regard to the atmospheric factors); ② both AOT and the solar zenith angle were positively correlated with the threshold values. Whether or not these relationships hold water for other cases needs to be further examined. It is thus held that these four remote sensing approaches should be used carefully for monitoring cyanobacteria blooms when atmospheric correction is not applied.
张月, 肖彧, 常晶晶, 刘健, 王亚琼, 贺春燕, 何冰. 大气校正对基于遥感指数提取藻华信息的影响[J]. 国土资源遥感, 2015, 27(3): 7-12.
ZHANG Yue, XIAO Yu, CHANG Jingjing, LIU Jian, WANG Yaqiong, HE Chunyan, HE Bing. Effects of atmospheric correction on extracting cyanobacteria bloom information based on remote sensing indices. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 7-12.
[1] 徐京萍,张柏,李方,等.基于MODIS数据的太湖藻华水体识别模式[J].湖泊科学,2008,20(2):191-195. Xu J P,Zhang B,Li F,et al.Detecting modes of cyanobacteria bloom using MODIS data in Lake Taihu[J].Journal of Lake Sciences,2008,20(2):191-195. [2] 段洪涛,张寿选,张渊智.太湖蓝藻水华遥感监测方法[J].湖泊科学,2008,20(2):145-152. Duan H T,Zhang S X,Zhang Y Z.Cyanobacteria bloom monitoring with remote sensing in Lake Taihu[J].Journal of Lake Sciences,2008,20(2):145-152. [3] 梅安新,彭望琭,秦其明,等.遥感导论[M].北京:高等教育出版社,2001:28-33. Mei A X,Peng W L,Qin Q M,et al.An Introduction to Remote Sensing[M].Beijing:Higher Education Press,2001:28-33. [4] Staylor W F.Degradation rates of the AVHRR visible channel for the NOAA 6,7,and 9 spacecraft[J].Journal of Atmospheric and Oceanic Technology,1990,7(3):411-423. [5] Chander G,Markham B.Revised Landsat-5 TM radiometric calibration procedures and post calibration dynamic ranges[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(11):2674-2677. [6] 秦伯强,胡维平,陈伟民,等.太湖水环境演化过程与机理[M].北京:科学出版社,2004:1-8. Qin B Q,Hu W P,Chen W M,et al.The Process and Mechanism of Water Environment Evolution in Lake Taihu[M].Beijing:Science Press,2004:1-8. [7] 徐希孺.遥感物理[M].北京:北京大学出版社,2005:292-297. Xu X R.Remote Sensing Physics[M].Beijing:Peking University Press,2005:292-297. [8] Richer R.Geo-atmospheric processing of airborne imaging spectrometry data[J].International Journal of Remote Sensing,2002,23(13):2631-2649. [9] Dekker A G.The use of the thematic mapper for the analysis of eutrophic lakes:A case study in the Netherlands[J].International Journal of Remote Sensing,1993,14(5):799-822. [10] Thiemann S,Kaufmann H.Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the Mecklenburg Lake district,Germany[J].Remote Sensing of Environment,2000,73(2):227-235. [11] 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[J].Remote Sensing and Environment,2005,96(2):176-187. [12] Reinart A,Kutser T.Comparison of different satellite sensors in detecting cyanobacterial bloom events in the Baltic Sea[J].Remote Sensing and Environment,2006,102(1/2):74-85. [13] Koponen S,Pulliainen J,Kallio K,et al.Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data[J]. Remote Sensing of Environment,2002,79(1):51-59. [14] 顾亮,张玉超,钱新,等.遥感指数在湖泊叶绿素a反演研究中的应用[J].环境保护科学,2008,34(2):53-56. Gu L,Zhang Y C,Qian X,et al.Application of remote sensing index in retrieving chlorophyll-a concentration of lakes[J].Environmental Protection Science,2008,34(2):53-56. [15] Rouse J W,Haas D W.Monitoring the Vernal Advancement and Retrogradation(Greenwave Effect)of Natural Vegetation[R].Greenbelt,Md:NASA Goddard Space Flight Cent,1974:11-13. [16] Tucker C J.Red and photographic infrared linear combinations for monitoring vegetation[J].Remote Sensing of Environment,1979,8(2):127-150. [17] 赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003:121-124. Zhao Y S.The Principle and Method of Analysis of Remote Sensing Application[M].Beijing:Science Press,2003:121-124. [18] 金相灿,刘明亮.中国湖泊富营养化[M].北京:中国环境科学出版社,1990:51-54. Jin X C,Liu M L.Lake Eutrophication in China[M].Beijing:China Environment Science Press,1990:51-54.
2015, Vol. 27 
