The Spratly(Nansha) Islands in the South China Sea have considerable economic and important militarily strategic status.Ocean color remote sensing is an effective mean of surveying and research and especially it is us...The Spratly(Nansha) Islands in the South China Sea have considerable economic and important militarily strategic status.Ocean color remote sensing is an effective mean of surveying and research and especially it is useful for areas that are difficult to access,such as Thitu Island and its reef in the Spratly Islands.The Hyper-spectral Optimization Process Exemplar(HOPE) model,developed by Lee et al.(1999) is a rapid and robust bathymetry method that uses hyper-spectral remote sensing.In this study,using Hyperion hyper-spectral sensor data and HOPE,we derive bathymetry and bottom albedo measurements around Thitu Island and its reef.We compare the distribution of bottom depths from C-MAP with that derived from the Hyperion data.The retrieved bathymetry results correlate well with the distribution obtained from the bathymetry contour from 2.0 to 20 m.The average difference between Hyperion and C-MAP for two selected transects was 17.1%(n=59,R=0.848,RMSE=2.342) and 10.9%(n=59,R2=0.834,RMSE=0.463).The retrieved bottom albedo is homogeneous in the lagoon and significantly non-homogeneous around the lagoon.These results indicate that HOPE could be very useful for bathymetry studies for the islands of the South China Sea.展开更多
A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorp...A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorption coefficient. The modified model performed well using in-situ data from the northern South China Sea, and the results were reliable and accurate. The relative errors of the size-fractioned chlorophyll-a concentration for each size class were: micro-:21%, nano-:41%, pico-:26%, and nano+pico:23%. The model was then applied on ocean color remote sensing data to examine the distribution and variation of phytoplankton size classes in northern South China Sea on a large scale.展开更多
In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been...In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been developed based on a constraint condition, which represents, to a certain degree, the correlation between suspended particulate matter (SPM) and pigment (CHL), coloured dissolved organic matter (CDOM) and CHL, as well as CDOM and SPM, found in Case Ⅱ waters. Compared with the ANN-based algorithm developed without a constraint condition, the performance of ANN-based algorithms developed with a constraint conditions is much better for the retrieval of CHL and CDOM, especially in the case of high noise levels; however, there is not significant improvement for the retrieval of SPM.展开更多
Based on the in situ optical measurements in the Bohai Sea of China, which belongs to a typical case-2 water area, we studied the characteristics of DCM (deep chlorophyll maximum) such as its spatial distribution, ver...Based on the in situ optical measurements in the Bohai Sea of China, which belongs to a typical case-2 water area, we studied the characteristics of DCM (deep chlorophyll maximum) such as its spatial distribution, vertical profile, etc. We found that when the depth of the chlorophyll maximum is comparatively small, even in turbid coastal water regions, there is always a good correlation between the concentrations of chlorophyll maximum and the satellite-received signals in blue-green spectral bands; the correlation is even better than that between the surface chlorophyll concentrations and the satellite-received signals. The strong correlation existing even in turbid coastal water regions indicates that an ocean color model to retrieve the concentration of DCM can be constructed for coastal waters if a comprehensive knowledge of the vertical distribution of chlorophyll concentration in the Bohai Sea of China is available.展开更多
基金Supported by the National Science Foundation for Young Scientists of China(No.40906087)
文摘The Spratly(Nansha) Islands in the South China Sea have considerable economic and important militarily strategic status.Ocean color remote sensing is an effective mean of surveying and research and especially it is useful for areas that are difficult to access,such as Thitu Island and its reef in the Spratly Islands.The Hyper-spectral Optimization Process Exemplar(HOPE) model,developed by Lee et al.(1999) is a rapid and robust bathymetry method that uses hyper-spectral remote sensing.In this study,using Hyperion hyper-spectral sensor data and HOPE,we derive bathymetry and bottom albedo measurements around Thitu Island and its reef.We compare the distribution of bottom depths from C-MAP with that derived from the Hyperion data.The retrieved bathymetry results correlate well with the distribution obtained from the bathymetry contour from 2.0 to 20 m.The average difference between Hyperion and C-MAP for two selected transects was 17.1%(n=59,R=0.848,RMSE=2.342) and 10.9%(n=59,R2=0.834,RMSE=0.463).The retrieved bottom albedo is homogeneous in the lagoon and significantly non-homogeneous around the lagoon.These results indicate that HOPE could be very useful for bathymetry studies for the islands of the South China Sea.
基金Supported by the National Natural Science Foundation of China (Nos.U0933005,41076014,40906021,41176035)the National High Technology Research and Development Program of China (863 Program)(No.2007AA092001-02)
文摘A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorption coefficient. The modified model performed well using in-situ data from the northern South China Sea, and the results were reliable and accurate. The relative errors of the size-fractioned chlorophyll-a concentration for each size class were: micro-:21%, nano-:41%, pico-:26%, and nano+pico:23%. The model was then applied on ocean color remote sensing data to examine the distribution and variation of phytoplankton size classes in northern South China Sea on a large scale.
文摘In the present paper, a method is proposed to improve the performance of Artificial Neural Network (ANN) based algorithms for the retrieval of oceanic constituents in Case Ⅱ waters. The ANN-based algorithms have been developed based on a constraint condition, which represents, to a certain degree, the correlation between suspended particulate matter (SPM) and pigment (CHL), coloured dissolved organic matter (CDOM) and CHL, as well as CDOM and SPM, found in Case Ⅱ waters. Compared with the ANN-based algorithm developed without a constraint condition, the performance of ANN-based algorithms developed with a constraint conditions is much better for the retrieval of CHL and CDOM, especially in the case of high noise levels; however, there is not significant improvement for the retrieval of SPM.
文摘Based on the in situ optical measurements in the Bohai Sea of China, which belongs to a typical case-2 water area, we studied the characteristics of DCM (deep chlorophyll maximum) such as its spatial distribution, vertical profile, etc. We found that when the depth of the chlorophyll maximum is comparatively small, even in turbid coastal water regions, there is always a good correlation between the concentrations of chlorophyll maximum and the satellite-received signals in blue-green spectral bands; the correlation is even better than that between the surface chlorophyll concentrations and the satellite-received signals. The strong correlation existing even in turbid coastal water regions indicates that an ocean color model to retrieve the concentration of DCM can be constructed for coastal waters if a comprehensive knowledge of the vertical distribution of chlorophyll concentration in the Bohai Sea of China is available.
