Int. J. Environ. Res. Public Health 2010, 7 2439 Numerous studies have focused on deriving Chl-a concentration information from hyperspectral sensor data in inland water bodies. All are based on the properties of the reflectance peak near 700 nm of productive turbid waters. Using vector analysis, Stumpf and Tyler showed that the ratio of the near infra-red (NIR) and the red bands of AVHRR and CZCS can identify phytoplankton blooms and has the potential to provide estimates of Chl-a above 10 mg/m3 in turbid estuaries [15]. Gons used the reflectance ratio at 704 and 672 nm, and assessed Chl-a concentrations ranging from 3 to 185 mg/m3 at these wavelengths [16]. Jiao et al. used the reflectance ratio between 719 nm and 667 nm to estimate Chl-a concentration in Taihu Lake [17]. Thiemann and Kaufman used the ratio between 705 and 678 nm to assess Chl-a in Mecklenburg Lake [18]. Similar algorithms use the ratio between the reflectance peak (Rmax) and the reflectance at 670 nm (R670), or the ratio R705/R670 [19-21]. All of these algorithms used the ratio of the near-infrared (NIR) peak reflectance to the reflectance near 675 nm, which is the red Chl-a absorption band and assumed that optical parameters including Chl-a specific absorption coefficient and Chl-a fluorescence quantum yield are constant [22,23]. However, the Chl-a fluorescence quantum yield depends on several factors, such as phytoplankton taxonomic composition, illumination conditions, nutritional status, and so on. This makes the bands chosen to estimate Chl-a by the algorithms mentioned above vary in different study areas. The uncertainty of modeling bands is the biggest problem of practical application of the algorithms. Some other semi-analytical algorithms were developed to estimate chlorophyll concentration in turbid waters. Dall’ Olmo revised a three- band reflectance model, originally developed for pigment content estimation in terrestrial vegetation, to assess Chl-a in turbid productive waters [22,24,25]. Le extended the model to four-bands and applied it to estimate Chl-a in Taihu Lake [26]. Although the influential factors of the algorithm are analyzed, it is also difficult to choose optimal band positions for Chl-a estimation in different lakes, because the spectrum of lakes is varying with different bandwidths, lakes and seasons. For all the limitations mentioned above, identification of the best indices for Chl-a monitoring is a current research issue. The objective of this study was to assess the potential of hyperspectral indices for Chl-a concentration detection in Tangxun Lake. The specific goals were: (1) to select the most appropriate bands for calculating several types of hyperspectral indices; (2) to evaluate different types of models in terms of their sensitivity to Chl-a concentration. 2. Study Area and Measurements 2.1. Study Area In this study, Tangxun Lake was selected as the study area. Located between 30°22′N and 30°30′N and between 114°15′E and 114°35′E, in Wuhan, Central China, Tangxun Lake, as the second largest lake in Wuhan City, has a storage capacity of 32.85 million km3 and a catchment area of 240.38 km2. Its current surface area is approximately 32.85 km2, accounting for 28.7% of the total lake area in Wuhan. Tangxun Lake is a typical shallow lake with a mean water depth of 18.5 m. With the industrialization and urbanization of Wuhan, an enormous amount of untreated wastewater and sewage was discharged into the lake without treatment, resulting in an incessant increase of nutrient concentration in the lake. Consequently, water eutrophication has become a serious environmental problem, and the water quality of Tangxun Lake, as a result, can barely satisfy the ecological and