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%.
In order to ensure the reliability of satellite data, it is necessary to test the authenticity of satellite products during the operation of ocean color satellite in orbit. Therefore, it is significant to obtain accurate sea surface field data, which can provide source data for the authenticity test of satellite products. At present, the main means of acquiring data at sea in China is still large-scale voyage test on board ships. This method needs high cost and requires a lot of manual operation, and the efficiency of acquiring data is extremely limited. However, a large amount of observation data can be obtained by establishing long-term automatic observation stations at sea, and the cost is low. In this paper, the continuous observation data of atmospheric optical parameters obtained by CE318 solar photometer installed on Wenzhou offshore platform in Zhejiang Province are analyzed based on the data processing method of AERONET. Combined with the actual situation, the automatic observation data of atmospheric optical parameters at sea are qualitatively controlled and verified by satellite data. Finally, a data quality control scheme for automatic observation of atmospheric optical parameters at sea is proposed.
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.