Estuary is an important part of the coastal zone. It is the junction of rivers and ocean. It is not only the end of the material in rivers, but also the beginning of the material in ocean, and an important place for the exchange of material and energy between continents and oceans. The Yongding New River flows into the Bohai Bay, which is an important area for the construction of the Bohai Rim Economic Zone. The migration law of chlorophyll concentration and its influencing factors are the core issues of estuarine research. Remote sensing inversion can realize dynamic, continuous and synchronous observation of large areas of water, and quickly obtain the spatiotemporal distribution of chlorophyll concentration. In this study, the Sentinel-2 MSI data from 2017 to 2021 were used to retrieve the chlorophyll concentration in the study area. Based on these data, the spatiotemporal variation of chlorophyll concentration in different seasons and its influencing factors were analyzed. The results show that the chlorophyll concentration in the study area has obvious temporal and spatial distribution rules, which is higher in spring and winter, lower in Summer and Autumn. The three elements of Sea Surface Temperature (SST), Photosynthetically Active Radiation (PAR) and Wind Speed (WS) all have an impact on the spatiotemporal distribution of chlorophyll concentration.
The extraction of seafloor substrate information based on remote sensing is the inevitable requirement of carrying out the survey of the basic habitat elements around islands and islands ecological environment protection. However, due to the influence of sun glint and water attenuation, the extraction accuracy of seafloor substrate information based on remote sensing has been poor. Therefore, the purpose of this study is to explore the extraction method of coral reefs based on remote sensing data, aiming to improve the feasibility and accuracy of detection. High resolution WorldView- 2 remote sensing images were used in this study as data source and waters around one of the Xiasha Islands were selected as study area Our research can be summarized into two part, one is to obtain bottom radiance from remote sensing data and another one is to identify different species from bottom radiance ,such as coral reefs, sand and so on. The following results have been obtained.(1) The overall accuracy of the classification results was 80.6%, and the consistency coefficient, also called Kappa coefficient, was 71.5%.(2) Among all the bottom sediment classification results, coral reefs has the highest production accuracy, which is 88.1%, mud has the lowest production accuracy, which is 59.0%, sand was the bottom sediment type whose user accuracy was the highest, which is 94.1%, and the mud has the lowest user accuracy, which is 65.1%.
Many studies have indicated that spectrum is mainly decided by substratum and water depth in shallow water,so spectrum above one kind of substrate is only decided by water. According to this idea we studied the technology of substrate classification, as well as analyzed the impacts of various water-depth extraction factors on the inversion accuracy. The following results have been obtained. (1) SVM has the highest classification accuracy, whose Kappa coefficient was 0.86 and overall accuracy was 92.34%, which is higher than that of neural network and maximum likelihood. (2) Correlation coefficient between factors based on spectral shape and water depth were over 70%, which is higher than that based on spectral amplitude. (3) SA and SGA are all have an exponential correlation with water depth and their inversion accuracy was almost the same. The mean relative error and mean absolute error for two factors were 9.9%,0.61m and 7.3%,0.74m, respectively. But they have different performance in various substrate area and depth.
With the rapid development of marine satellites in China, several marine satellites are about to be launched in recent planning. It is important to evaluate the payload performance of marine satellites, but the research on satellite load assessment is almost carried out for terrestrial satellites. The imaging region of the Coastal Zone Imager includes marine water bodies, so some assessment methods for estimating the performance of terrestrial satellites may not be applicable. In this paper, by analyzing and comparing the advantages and disadvantages of the current calculation methods of load performance evaluation, combined with the characteristics of the Coastal Zone Imager, the load performance evaluation scheme for Ocean color satellite is selected, and the real remote sensing data is used to verify the results.
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