Ms. Yanlong Chen Profile

Yanlong Chen

at National Marine Environmental Monitoring Ctr.

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Publications (3)

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Proceedings Article | 28 March 2022 Paper

Validation of MODIS C6.1 3km aerosol products over Yuandao region in the North Yellow Sea

Yujuan Ma, Lin Wang, Xiang Wang, Yanlong Chen

Proceedings Volume 12169, 121699U (2022) https://doi.org/10.1117/12.2625710

KEYWORDS: Aerosols, MODIS, Atmospheric particles, Spatial resolution, Pollution, Atmospheric monitoring

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This study evaluates MODIS C6.1 3km aerosol optical thickness (AOD) product with different solutions and different quality control strategies against ground-based observations over Yuandao in the North Yellow Sea. AOD data obtained from datasets “Effective_Optical_Depth_Best_Ocean”, “Effective_Optical_Depth_Average_Ocean”, “Optical_Depth_Land_And_Ocean” and “Image_Optical_Depth_Land_And_Ocean” are evaluated separately. Results showed that they are all well correlated with ground based observations, and AOD data from “Optical_Depth_Land_And_Ocean” with “average solution” and QAC<0 perform best. Seasonal variation are obvious and similar for the 4 datasets, AOD for autumn show the best performance while spring show the worst. Combined effect of dust aerosol, urban pollution and biomass burning aerosol may be the reason for the lower precision in spring.

Proceedings Article | 14 October 2019 Paper

An unsupervised feature learning method to distinguish Sargassum

Feng Ye, Zengzhou Hao, Yanlong Chen, Fang Gong, Haiqing Huang, Difeng Wang

Proceedings Volume 11150, 1115012 (2019) https://doi.org/10.1117/12.2532576

KEYWORDS: Satellites, Remote sensing, Neural networks, Environmental monitoring, Feature extraction

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In recent years, the golden tide, which is caused by the explosive proliferation of Sargassum, has occurred frequently in China Seas. It has made a great negative impact on the marine ecosystem, aquaculture, and coastal tourism. Fortunately, satellite observation can monitor and track the growth of large algae such as Sargassum in a timely and effective manner, providing scientific basis for disaster prevention and mitigation in fisheries and environmental protection departments. Most of traditional extraction methods of macroalgae are pixel-oriented. Although these methods can be performed easily, they loss the rich texture information of the natural objects. The Sargassum seen from remote sensing imageries tends to aggregate in groups, like strips, covering several to dozens of pixels. Therefore, this paper considered distinguishing Sargassum from a certain area based on scene by utilizing contextual relationships among pixels and the diversity of spatial and structural features. In this paper, the image acquired by GF-1 during the golden tide disaster in the sea area near Jiangsu Province of China on December 31, 2016 were used. We adopter an unsupervised feature learning method to distinguish Sargassum. The Voting method was used to divide the original image into small image blocks guided by the corresponding saliency image. After 0-meanization and ZCA whitening, the initial weights were obtained by training the sparse autoencoder, then these weights were convolved as the convolution kernel to obtain the local features of the image, the features convoluted were passed. We pooled them to reduce the eigenvectors of the convolutional layer output so that the global statistical features of the image could be extracted. Finally, the Softmax classifier was used to distinguish the regions of Sargassum in the original image. The experimental accuracy was 77.79% and superior to the threshold extraction methods compared with the result of manual labeling.

Proceedings Article | 12 December 2018 Paper

Variations in the optical backscattering properties of phytoplankton of different cell sizes

Lingling Jiang, Yanlong Chen, Lin Wang, Wang Xiang, Xiangyu Guo

Proceedings Volume 10850, 108500B (2018) https://doi.org/10.1117/12.2504856

KEYWORDS: Backscatter, Oceanography, Particles, Ocean optics, Remote sensing, Water, Optical testing, Environmental monitoring

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The backscattering properties of four marine phytoplankton from three size-classified taxa at different chlorophyll concentrations were directly measured in the laboratory using a Hydroscat-6 instrument. The ancillary parameters measured included the absorption and scattering coefficients, chlorophyll-a concentration, cell size, and cell concentration. We found that the value of the backscattering coefficients at each band increased with an increase in chlorophyll concentration for each alga, and the spectral shape at the blue bands were changed for the picophytoplankton and nanophytoplankton while it remained the same for microphytoplankton with changes in the chlorophyll-a concentration. We also found that the backscattering variation range changed with the increase in the chlorophyll concentration: the larger the cell size, the smaller its range of change. In addition, the smaller particles had relatively higher backscattering at shorter wavelengths, but no relationship between the cell size and contribution to the backscattering was found. Moreover, we also found a relationship between size and both the backscattering ratio and backscattering cross-section, in accordance with previous results. The results from this work provide a good foundation for improving the accuracy of identifying red tide algae using water color remote sensing.

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