scholarly journals Remote-Sensing Estimation of Phytoplankton Size Classes From GOCI Satellite Measurements in Bohai Sea and Yellow Sea

2017 ◽  
Vol 122 (10) ◽  
pp. 8309-8325 ◽  
Author(s):  
Deyong Sun ◽  
Yu Huan ◽  
Zhongfeng Qiu ◽  
Chuanmin Hu ◽  
Shengqiang Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Satellite Measurements ◽  
Size Classes ◽  
Phytoplankton Size

scholarly journals Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China’s Seas: Impacts of Cloud Coverage and Sediment Variations

2020 ◽  
Vol 12 (12) ◽  
pp. 1945
Author(s):  
Liqiao Tian ◽  
Xianghan Sun ◽  
Jian Li ◽  
Qianguo Xing ◽  
Qingjun Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
East China Sea ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Ocean Color ◽  
Suspended Sediments ◽  
The East China Sea ◽  
Cloud Coverage ◽  
Satellite Sensor

Satellite-based ocean color sensors have provided an unprecedentedly large amount of information on ocean, coastal and inland waters at varied spatial and temporal scales. However, observations are often adversely affected by cloud coverage and other poor weather conditions, like sun glint, and this influences the accuracy associated with long-term monitoring of water quality parameters. This study uses long-term (2013–2017) and high-frequency (eight observations per day) datasets from the Geostationary Ocean Color Imager (GOCI), the first geostationary ocean color satellite sensor, to quantify the cloud coverage over China’s seas, the resultant interrupted observations in remote sensing, and their impacts on the retrieval of total suspended sediments (TSS). The monthly mean cloud coverage for the East China Sea (ECS), Bohai Sea (BS) and Yellow Sea (YS) were 62.6%, 67.3% and 69.9%, respectively. Uncertainties regarding the long-term retrieved TSS were affected by a combination of the effects of cloud coverage and TSS variations. The effects of the cloud coverage dominated at the monthly scale, with the mean normalized bias (Pbias) at 14.1% (±2.6%), 7.6% (±2.3%) and 12.2% (±4.3%) for TSS of the ECS, BS and YS, respectively. Cloud coverage-interfering observations with the Terra/Aqua MODIS systems were also estimated, with monthly Pbias ranging from 6.5% (±7.4%) to 20% (±13.1%) for TSS products, and resulted in a smaller data range and lower maximum to minimum ratio compared to the eight GOCI observations. Furthermore, with approximately 16.7% monthly variations being missed during the periods, significant “missing trends” effects were revealed in monthly TSS variations from Terra/Aqua MODIS. For the entire region and the Bohai Sea, the most appropriate timeframe for sampling ranges from 12:30 to 15:30, while this timeframe was narrowed to from 13:30 to 15:30 for observations in the East China Sea and the Yellow Sea. This research project evaluated the effects of cloud coverage and times for sampling on the remote sensing monitoring of ocean color constituents, which would suggest the most appropriate timeframe for ocean color sensor scans, as well as in situ data collection, and can provide design specification guidance for future satellite sensor systems.

Remote Sensing of Suspended Particle Size in Yellow Sea and Bohai Sea

2015 ◽  
Vol 35 (9) ◽  
pp. 0901008 ◽  
Author(s):  
陈亚慧 Chen Yahui ◽  
丘仲锋 Qiu Zhongfeng ◽  
孙德勇 Sun Deyong ◽  
王胜强 Wang Shengqiang ◽  
何宜军 He Yijun
Keyword(s):  
Remote Sensing ◽  
Particle Size ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Suspended Particle ◽  
Suspended Particle Size

scholarly journals Remote Sensing of Particle Cross-Sectional Area in the Bohai Sea and Yellow Sea: Algorithm Development and Application Implications

2016 ◽  
Vol 8 (10) ◽  
pp. 841 ◽  
Author(s):  
Shengqiang Wang ◽  
Yu Huan ◽  
Zhongfeng Qiu ◽  
Deyong Sun ◽  
Hailong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Cross Sectional Area ◽  
Sectional Area ◽  
Cross Sectional ◽  
Algorithm Development ◽  
The Bohai Sea

scholarly journals A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

2019 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Deyong Sun ◽  
Zunbin Ling ◽  
Shengqiang Wang ◽  
Zhongfeng Qiu ◽  
Yu Huan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Refractive Index ◽  
Spatial Distribution ◽  
Yellow Sea ◽  
Bohai Sea ◽  
Suspended Particles ◽  
In Situ Observation ◽  
Observation Data ◽  
Estimation Model

The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bp → cp → j, bbp and bp → Bp, and j and Bp → np. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate np in other coastal optically complex water bodies.

Twenty‐Year Variations in Satellite‐Derived Chlorophyll‐a and Phytoplankton Size in the Bohai Sea and Yellow Sea

2019 ◽  
Vol 124 (12) ◽  
pp. 8887-8912 ◽  
Author(s):  
Xuerong Sun ◽  
Fang Shen ◽  
Robert J.W. Brewin ◽  
Dongyan Liu ◽  
Rugang Tang
Keyword(s):  
Chlorophyll A ◽  
Yellow Sea ◽  
Bohai Sea ◽  
The Bohai Sea ◽  
Phytoplankton Size
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