Li Junsheng

Identification of algae-bloom and aquatic macrophytes in Lake Taihu from in-situ measured spectra data

Identification of algae-bloom and aquatic macrophytes plays a significant role in inland water quality monitoring by remote sensing, which can be used to reflect the water quality status indirectly, and then the result of water can be used to retrieve water quality parameters. However, the mostly used multi-spectral remote sensing data cannot accurately identify algae-bloom and water grass. Only hyperspectral remote sensing data, as the data can be distinguished the subtle differences of the spectral characteristics between algae-bloom and water grass, can be used to identify algae-bloom and water grass with high accuracy. Unfortunately, there have been few of profound researches on the identification of algae-bloom and water from hyperspectral remote sensing data. Lake Taihu is selected to be the study area. Two experiments were carried out in Lake Taihu in July and October of 2006. Reflectance spectra of the floating vegetation, submerged vegetation, algae-bloom, and water were measured. Based on the analysis of the measured spectra, four spectral indexes were defined to build up formulas for identification of the four items. Reflectance spectra measured in October 2006 were used to determine the threshold values in the identification formulas, and reflectance spectra measured in July 2006 were used to validate the identification formulas. The identification results were very good.

Retrieval of three kinds of representative water quality parameters of Lake Taihu from hyperspectral remote sensing data

Lake Taihu, which has been in serious eutrophic pollution status, was selected to be the study area. In Lake Taihu, two-time experiments of airborne hyperspectral remote sensing were carried out, covering seven airborne strips over Lake Taihu in both winter and summer. Besides of the two times of experiments, the in-situ inherent and apparent optical properties of Lake Taihu water were measured and analyzed for additional four times. The specific inherent optical property database of Lake Taihu was built. Based on the database and bio-optical model, analytical approaches were developed to retrieve chlorophyll, total suspended matter, and yellow substance. To validate these analytical approaches, airborne hyperspectral remote sensor WHI image and spaceborne hyperspectral remote sensor CHRIS image were used to retrieve water quality parameters, and the results were good.

A simple correction method for the MODIS surface reflectance product over typical inland waters in China

ABSTRACT The Moderate Resolution Imaging Spectroradiometer (MODIS) has the advantage of providing continuous, global, near-daily spatial measurements, and has greatly aided in understanding physical, optical, and biological processes in the global ocean biosphere. However, little research has been implemented for the remote-sensing monitoring of global inland waters. One important factor is that there is no operational atmospheric correction method designed for global inland waters. The MODIS surface reflectance product (MOD09) provides surface reflectance data for land at the global scale, but it does not offer accurate atmospheric correction over inland waters because of the constraints of its primary correction algorithm. The purpose of this article is to provide a simple and operational correction method for the MOD09 product to retrieve the water-leaving reflectance for large inland waters larger than 25 km2. The correction method is based on an analysis of additive noises in MOD09 data over inland waters and on the adoption of two assumptions. Field-measured data collected in three typical inland waters in China were used to assess the performance of the correction method to ensure its applicability for waters in different conditions. The results show acceptable agreement with field data over the three inland waterbodies, with a mean relative error of 17.1% in visible bands. Our study demonstrates that the MOD09 correction method is moderately accurate when compared with the optimal method for specific waterbodies, but it has the potential for use in operational data-processing systems to derive water-leaving reflectance data from MOD09 data over inland waters in a variety of conditions and large regions.

A Study on Retrieval Algorithm of Black Water Aggregation in Taihu Lake Based on HJ-1 Satellite Images

The phenomenon of black water aggregation (BWA) occurs in inland water when massive algal bodies aggregate, die, and react with the toxic sludge in certain climate conditions to deprive the water of oxygen. This process results in the deterioration of water quality and damage to the ecosystem. Because charge coupled device (CCD) camera data from the Chinese HJ environmental satellite shows high potential in monitoring BWA, we acquired four HJ-CCD images of Taihu Lake captured during 2009 to 2011 to study this phenomenon. The first study site was selected near the Shore of Taihu Lake. We pre-processed the HJ-CCD images and analyzed the digital number (DN) gray values in the research area and in typical BWA areas. The results show that the DN values of visible bands in BWA areas are obviously lower than those in the research areas. Moreover, we developed an empirical retrieving algorithm of BWA based on the DN mean values and variances of research areas. Finally, we tested the accuracy of this empirical algorithm. The retrieving accuracies were89.9%, 58.1%, 73.4%, and 85.5%, respectively, which demonstrates the efficiency of empirical algorithm in retrieving the approximate distributions of BWA.

Retrieval of chlorophyll-a and suspended matter concentration in water supply sources of Wuxi and Suzhou using multi-spectral remote sensing images

The problem of water source pollution has become more and more serious in Wuxi and Suzhou district. It is urgent to monitor water quality widely and rapidly, which is the advantage of remote sensing. However, water sources around cities are inland waters in which chlorophyll-a and suspended matter concentrations are hard to retrieve accurately from remote sensing just by using empirical methods. To overcome this problem, this study has developed an analytical method based on inherent optical parameters to retrieve chlorophyll-a and suspended matter concentrations. To validate this method, we have collected a dataset as follows: CBERS CCD image in Taihu Lake around Wuxi and Suzhou, in situ measured water reflectance spectra,and inherent optical parameters, and the simultaneously measured aerosol data from Wuxi. Based on two approximate premises, we apply the red and the near infrared images to get total suspended matter concentration and chlorophyll-a concentration. The retrieved concentrations of total suspended matter and chlorophyll-a are close with in situ measured ones. This study is helpful for monitoring water quality of water supply sources from multi-spectral remote sensing images.

Study on level-1 requirements of hyperspectral remote sensor for inland waters

The existing remote sensors might be limited to meet the requirement of inland aquatic environments for that they mainly focus on imaging of ocean ecosystem and coastal regions. Inland water regions should be paid more attention for that they are always seriously polluted and affect human life directly. Hence, the paper discussed performance which the next generation of water color hyperspectral sensors may have to meet the demand of inland waters monitoring. It could capture the spectral curve of inland water, meanwhile, avoid waste of bands for storing and memory. The paper referred assignments of existing sensors, analyzed measured Rrs in typical inland water, simulated radiances at top of atmosphere, and considered different applications such as algae bloom monitoring, atmospheric correction. We proposed bands with the spectral width and position, dynamic range, noise-equivalent radiance NEΔL and number of bits in each band. The results of the study may be helpful in designing the next generation remote sensors for inland waters monitoring.