Time filter

Source Type

Sun D.,Nanjing University of Information Science and Technology | Li Y.,Nanjing Normal University | Wang Q.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | And 3 more authors.
Hydrobiologia | Year: 2011

Our aim was to refine the optical classification of turbid waters in order to improve the performance of water color algorithms. Bio-optical measurements and sampling of optically active substances were performed in highly turbid lakes Taihu, Chaohu, and Dianchi, and in Three Gorges reservoir in China. Based on strong correlations between trough depths of remote sensing reflectance (Rrs(λ)) near 680 nm (denoted as TD680) and the ratios of inorganic suspended matter (ISM) to total suspended matter (TSM) concentrations, an empirical model was developed for water classification. In the 400-900 nm spectral range, different correlations between Rrs(λ), TSM and chlorophyll a (Chla) concentrations indicate discrepancies among the following ISM/TSM ranges: ISM/TSM ≤ 0.5, 0.5 < ISM/TSM < 0.8, and ISM/TSM ≥ 0.8. Corresponding findings support an important conclusion that only high ISM/TSM ratios, usually above 0.5, and using the more sensitive and stable near infrared spectral range (730-820 nm), can assure good performances of the TSM remote sensing algorithms. Meanwhile, the particulate absorption ap(λ) and scattering bp(λ) were strongly influenced by different ranges of ISM/TSM ratios. Typically the ap(675) peaks became more and more vague as ISM/TSM increased, and the bp(λ) values first decreased and then increased with a marked inflexion at ISM/TSM ≈ 0.5. The TD680 threshold values were derived to discriminate three types of turbid waters, i. e., Type 1 (TD680 ≥ 0.0082 sr-1), Type 2 (0.0082 sr-1 > TD680 > 0 sr-1), and Type 3 (TD680 ≤ 0 sr-1). This study provides a promising tool for identifying various types of highly turbid waters, and is significant for the development of class-based algorithms of water color remote sensing. © 2011 Springer Science+Business Media B.V. Source

Li Y.,Nanjing Normal University | Li Y.-M.,Nanjing Normal University | Wang Q.,Nanjing Normal University | Zhu L.,Satellite Environment Application Center | Guo Y.-L.,Nanjing Normal University
Aquatic Ecosystem Health and Management | Year: 2014

Data assimilation is a method to produce a description of the system state, as accurately as possible, under the control of observations by using all the available information and by taking into account the observation and model errors. We developed a framework for Observing System Simulation Experiments (OSSEs) based on the ensemble square root Klaman filter (EnSRF) technique, and the framework could assimilate two data sets of chlorophyll a retrieved from Environmental Satellite 1 (HJ-1) and moderate resolution imaging spectro-radiometer (MODIS) onboard the Terra platform, separately. We assumed that one of the retrieved results was the proxy “truth value” and the other one contained errors. Based on EnSRF technique, combined with the three dimensional numerical model of wind-driven circulation and pollutant transportation in a large-scale lake, we investigated the potential impact of location distributions of simulated observation stations in Taihu Lake (China) on the performance of data assimilation. In addition, the effectiveness of this method for evaluation and prediction of the concentration of chlorophyll a was validated. The results showed that the location of simulated observation stations not only influenced the accuracy of evaluating and forecasting results, but also the performance of data assimilation. We also discuss the impact of assimilation time and background error on the results. This study demonstrated that this method of data assimilation is effective for evaluation and prediction of the concentration of chlorophyll a in highly turbid case 2 waters. © 2014, Copyright © 2014 AEHMS. Source

Li S.,Nanjing Normal University | Lai Z.,Nanjing Normal University | Wang Q.,Satellite Environment Application Center | Wang Z.,Nanjing Normal University | And 2 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2013

Distributed hydrological modeling plays an important role in water resource management and regional non-point source pollution assessment. The Soil and Water Assessment Tool (SWAT) is a popular modeling tool for understanding regional hydrological processes. However, the general approach based on the SWAT model was only applicable to the mountain and hilly dominated area. There is no effective way to modeling the hydrologic process in plain river network regions, which is characterized by large flat areas, consisted of many lakes and artificially hydrological polders, and intersected stream networks, etc. The existing methods cannot effectively extract the channels in flat and pit areas, parallel channels or discontinuous rivers and the definition error of the catchment areas. To overcome these problems, we developed a novel method for modeling the distributed spatial discretization of the plain river network area based on the SWAT model. There are three key techniques are discussed: making the rings and crossed rivers to dendritic stream networks by cutting the river ways shortly, restoring the distribution of water between reaches by transferring water from one reach to another one on the basis of flow rate of each reach and simulating the exchange of water inside and outside of the polders according to the scheduled rules of the polder areas by adding a 'virtual reservoirs' within the SWAT model. In this paper, the typical plain river network region located in western Taihu watershed was chosen as the study area, and a large number of basic geographic data such as topography, soil, climate and land use were collected and parameterized. The modeling procedures were used to simulate the monthly runoff of the area of western Taihu Lake from the year of 2008 to 2010, and the applicability of the method to the plain river network region was also verified. The simulated results matched mostly well to the observed data of Rongdengqiao, Hujiawei, and Yixing hydrological stations. The calculated Nash-Sutcliffe efficiency coefficient and correlation coefficient of three hydrological stations were 0.84, 0.80, 0.67 and 0.94, 0.95, 0.93, respectively. It indicated that our developed framework for the SWAT model was practical and capable of representing the hydrological processes in the plain river network regions. Source

Guo Y.,Carbon Control | Li Y.,Carbon Control | Li Y.,Jiangsu Center For Collab Innovation In Geographical Information Resource Development And Applied | Zhu L.,Satellite Environment Application Center | And 3 more authors.
Remote Sensing | Year: 2015

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense-the radiance and the spectrum-the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (Cchla) in Taihu Lake. The result shows that the Cchla map obtained by IUBF fusion captures more detailed information than that of MERIS. Source

Lyu H.,Nanjing Normal University | Lyu H.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application | Zhang J.,Nanjing Normal University | Zha G.,Nanjing Normal University | And 3 more authors.
International Journal of Remote Sensing | Year: 2015

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern. © 2015 Taylor & Francis. Source

Discover hidden collaborations