Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll-a (chl-a) concentration. Twenty-eight spectral reflectance curves were standardized and correlated with chl-a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl-a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R 667 ), chl-a concentration was not accurately estimated at R 2 50.494. Accuracy of estimation was improved to R 2 50.817 using the maximum reflectance. A higher accuracy of 0.

A B S T R A C T Remote sensors designed specifically for studying the atmosphere have been widely used to derive timely information on air pollution at various scales. Whether the satellite-generated aerosol optical thickness (AOT) data can be used to monitor air pollution, however, is subject to the effect of a number of meteorological parameters. This study analyses the influence of four meteorological parameters (air pressure, air temperature, relative humidity, and wind velocity) on estimating particulate matter (PM) from MODIS AOT data for the city of Nanjing, China during [2004][2005][2006]. After the PM data were correlated with the AOT data that had been divided into four chronological seasons, a minimum correlation coefficient of 0.47 was found for the winter season, but a much stronger correlation (r > 0.80) existed in summer and autumn. Similar analyses were carried out after all observations were clustered into four groups based on their meteorological similarity using the K-Means analysis. Grouping caused more observations to be useable in the monitoring of air pollution than season-based analysis. Of the four groups, three had a correlation coefficient higher than 0.60. Grouping-based analysis enables the pollution level to be determined more accurately from MODIS AOT data at a higher temperature and relative humidity, but a lower air pressure and wind velocity. The accuracy of monitoring air pollution is inversely related to the pollution level. Thus, remote sensing monitoring of air pollution has its limits.

In order to extract quantitative water-leaving information from the Thematic Mapper (TM) image accurately in inland waters, atmospheric correction is a necessary step. Based on former researchers' results, the paper presents two atmospheric correction algorithms based on meteorological data (MD) and on Moderate Resolution Imaging Spectroradiometer (MODIS) Vicarious Calibration (MVC) for TM image in inland waters according to the theory of radiative transfer. Studying Taihu lake, China, in this paper we derived water remote sensing reflectance from a TM image of 26 July 2004 by these two atmospheric correction algorithms and we compare the results with that of dark object subtraction (DOS) and 6S code. The results show that the effect of atmospheric correction based on meteorological data and MODIS Vicarious Calibration is much better than that of DOS and 6S code. Although the MD is more accurate, MVC may be an ideal choice for TM images in inland water because TERRA MODIS images can be acquired easily than collecting meteorological data at the time of satellites passing over.

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Haze is an undesirable meteorological and environmental phenomenon that can cause enormous harm to the environment, people's lives and health, and economic activities. This study focuses on Nanjing, Yangzhou and Suzhou in the lower reaches of the Yangtze River valley, China, which have suffered from the adverse effects of hazy weather in recent years. The spectral influence of haze on surface features was determined through analysis of the spectral variations of surface covers between hazy and haze-free days. On the basis of the established relationship, a new index called the normalized difference haze index (NDHI) was derived using moderate resolution imaging spectroradiometer (MODIS) data from winter 2008-2009. Correlation analysis of the derived NDHI with in situ observed PM 10 (particulate matter with diameter <10 µm) data reveals that NDHI over water bodies has a coefficient of 0.74, 0.57 and 0.67 with PM 10 for Nanjing, Yangzhou and Suzhou, respectively. It is concluded that NDHI is a reliable indicator of air pollution. It can be used as a new method of effectively monitoring air pollution from remotely sensed data.