11 results on '"Hu, Shuibo"'
Search Results
2. Variability of Marine Particle Size Distributions and the Correlations with Inherent Optical Properties in the Coastal Waters of the Northern South China Sea.
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Wang, Zuomin, Hu, Shuibo, Li, Qingquan, Liu, Huizeng, and Wu, Guofeng
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TERRITORIAL waters , *OPTICAL properties , *WATER depth , *ATTENUATION coefficients , *PARTICLE size distribution , *REMOTE sensing - Abstract
Particle size distribution (PSD), which is an important characteristic of marine suspended particles, plays a role in how light transfers in the ocean and impacts the ocean's inherent optical properties (IOPs). However, PSD properties and the correlations with IOPs are rarely reported in coastal waters with complex optical properties. This study investigated the PSD variabilities both for the surface water and the water in vertical planes, and the correlations between PSD and the backscattering coefficient (bbp), scattering coefficient (bp), and attenuation coefficient (cp), based on in situ PSD observations (within a size range of 2.05–297 μm) and IOPs in the coastal northern South China Sea. The results show a large variety of PSDs, with a range of 41.06–263.02 μm for the median particle diameter (Dv50) and a range of 2.61–3.74 for the PSD slope. In addition, the predominance of small particles is most likely to appear in the nearshore shallow water and estuaries with a large amount of sediment discharge, and vice versa. For the variabilities of IOPs, the particle concentration in a cross-sectional area (AC) is the first driving factor of the variations of bbp, bp, and cp, and the product of the mean particle diameter (DA) and the apparent density (ρa) can explain most variations of the mass-specific bbp (bbp/SPM), bp (bp/SPM), and cp (cp/SPM). In this study, we found that particle size is strongly correlated with volume-specific bbp (bbp/VC), bp (bp/VC), and cp (cp/VC), and the 10th percentile diameter of the accumulated volume concentration (Dv10) can better explain the variations of bbp/VC. These findings suggest a potential PSD retrieval method utilizing the bbp or bp, which may be determined by remote sensing observations. [ABSTRACT FROM AUTHOR] more...
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- 2022
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3. Evaluation of Ocean Color Atmospheric Correction Methods for Sentinel-3 OLCI Using Global Automatic In Situ Observations.
- Author
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Liu, Huizeng, He, Xianqiang, Li, Qingquan, Hu, Xianjun, Ishizaka, Joji, Kratzer, Susanne, Yang, Chao, Shi, Tiezhu, Hu, Shuibo, Zhou, Qiming, and Wu, Guofeng
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OCEAN color ,REMOTE sensing ,IMAGE color analysis - Abstract
The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automatic in situ observations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI. [ABSTRACT FROM AUTHOR] more...
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- 2022
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4. Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes.
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Hu, Shuibo, Liu, Huizeng, Zhao, Wenjing, Shi, Tiezhu, Hu, Zhongwen, Li, Qingquan, and Wu, Guofeng
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MACHINE learning , *PHYTOPLANKTON , *BIOGEOCHEMICAL cycles , *BIG data , *HIGH performance liquid chromatography - Abstract
The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs), in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD) high performance liquid chromatography (HPLC) database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS), artificial neural networks (ANN), support vector machine (SVM) and random forests (RF), and feature selection techniques, including genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination based on support vector machine (SVM-RFE), for inferring PSCs from remote sensing data. Results showed that: (1) SVM-RFE worked better in selecting sensitive features; (2) RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3) machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4) sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5) the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing. [ABSTRACT FROM AUTHOR] more...
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- 2018
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5. Satellite-observed variability of phytoplankton size classes associated with a cold eddy in the South China Sea.
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Lin, Junfang, Cao, Wenxi, Wang, Guifen, and Hu, Shuibo
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PHYTOPLANKTON ,CYCLONES ,CLIMATE change ,NATURAL satellites ,ECOLOGICAL models - Abstract
Highlights: [•] We reparameterize and validate a model of phytoplankton size class (PSC). [•] We observe variability of PSC associated with a cyclonic eddy. [•] The cold eddy is characterized by enhanced productivity and a shift in the PSC. [•] Changes in PSC are mainly influenced by physical and biological processes. [Copyright &y& Elsevier] more...
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- 2014
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6. A Four-Step Method for Estimating Suspended Particle Size Based on In Situ Comprehensive Observations in the Pearl River Estuary in China.
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Wang, Zuomin, Hu, Shuibo, Li, Qingquan, Liu, Huizeng, Liao, Xiaomei, and Wu, Guofeng
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TERRITORIAL waters , *OPTICAL properties , *SEAWATER , *ESTUARIES , *REMOTE sensing - Abstract
The suspended particle size has great impacts on marine biology environments and biogeochemical processes, such as the settling rates of particles and sunlight transmission in marine water. However, the spatial–temporal variations in particle sizes in coastal waters are rarely reported due to the paucity of appropriate observations and the limitations of particle size retrieval methods, especially in areas with complex optical properties. This study proposed a remote sensing-based method for estimating the median particle size Dv50 (calculated with a size range of 2.05–297 μm) that correlates Dv50 with the inherent optical properties (IOPs) retrieved from in situ remote sensing reflectance above the water's surface (Rrs(λ)) in the Pearl River estuary (PRE) in China. Rrs(λ) was resampled to simulate the Multispectral Instrument (MSI) onboard Sentinel-2A/B, and the wavebands in 490, 560, and 705 nm were utilized for the retrieval of the IOPs. The results of this method had a statistical performance of 0.86, 18.52, 21.28%, and −1.85 for the R2, RMSE, MAPE, and bias values, respectively, in validation, which indicated that Dv50 could be estimated by Rrs(λ) with the proposed four-step method. Then, the proposed method was applied to Sentinel-2 MSI imagery, and a clear difference in Dv50 distribution which was retrieved from a different time could be seen. The proposed method holds great potential for monitoring the suspended particle size of coastal waters. [ABSTRACT FROM AUTHOR] more...
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- 2021
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7. Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine.
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Deng, Lin, Zhou, Wen, Cao, Wenxi, Zheng, Wendi, Wang, Guifen, Xu, Zhantang, Li, Cai, Yang, Yuezhong, Hu, Shuibo, and Zhao, Wenjing
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SUPPORT vector machines ,PHYTOPLANKTON ,CHLOROPHYLL ,REMOTE sensing ,ALGORITHMS - Abstract
The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (a
t-w (λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from at-w (λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r2 between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea. [ABSTRACT FROM AUTHOR] more...- Published
- 2019
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8. Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea.
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Hu, Shuibo, Zhou, Wen, Wang, Guifen, Cao, Wenxi, Xu, Zhantang, Liu, Huizeng, Wu, Guofeng, and Zhao, Wenjing
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PHYTOPLANKTON , *REMOTE sensing , *OCEAN color , *COMPARATIVE studies - Abstract
Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (aph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data [ABSTRACT FROM AUTHOR] more...
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- 2018
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9. Determining switching threshold for NIR-SWIR combined atmospheric correction algorithm of ocean color remote sensing.
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Liu, Huizeng, Zhou, Qiming, Li, Qingquan, Hu, Shuibo, Shi, Tiezhu, and Wu, Guofeng
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OCEAN color , *REMOTE sensing , *WELL water , *ALGORITHMS , *NEAR infrared radiation - Abstract
Accurate atmospheric correction is decisive for ocean color remote sensing applications. Near infrared (NIR)-based algorithm performs well for clear waters; while shortwave infrared (SWIR)-based algorithm can obtain good results for turbid waters, however, it tends to produce noisy patterns for clear waters. A practical strategy is to apply NIR- and SWIR-based algorithm for clear and turbid waters, respectively, which is called NIR-SWIR combined atmospheric correction algorithm. However, the currently applied switching scheme for the NIR-SWIR algorithm undermines the atmospheric correction performance. This study aimed to find an applicable switching scheme for NIR-SWIR algorithm. Four MODIS land bands were used to switch the NIR- and SWIR-based algorithms. A simulated dataset was used to evaluate atmospheric performance of NIR- and SWIR-based algorithm. The switching threshold for each MODIS land band was determined as an R rs value at which SWIR-based algorithm performed better than NIR-based algorithm. The switching scheme was evaluated using matchups of simultaneous MODIS Aqua images and AERONET-OC data, and then tested with a MODIS Aqua image over the western Pacific Ocean. Results showed that the switching threshold for R rs (469), R rs (555), R rs (645) and R rs (859) were 0.009, 0.016, 0.009 and 0.0006 sr−1, respectively; R rs (645) with a threshold of 0.009 sr−1 and R rs (555) with a threshold of 0.016 sr−1 worked well for NIR-SWIR algorithm, while R rs (469) and R rs (859) produced worse performance. Therefore, R rs (555) > 0.016 sr−1 or R rs (645) > 0.009 sr−1 was recommended as the switching scheme for NIR-SWIR algorithm. Considering contrasted estuarine, coastal and some inland waters, combining NIR- and SWIR-based atmospheric correction algorithm with the proposed switching scheme should be useful for remote sensing monitoring over these waters. [ABSTRACT FROM AUTHOR] more...
- Published
- 2019
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10. Estimating ultraviolet reflectance from visible bands in ocean colour remote sensing.
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Liu, Huizeng, He, Xianqiang, Li, Qingquan, Kratzer, Susanne, Wang, Junjie, Shi, Tiezhu, Hu, Zhongwen, Yang, Chao, Hu, Shuibo, Zhou, Qiming, and Wu, Guofeng
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REMOTE sensing , *REFLECTANCE , *CHLOROPHYLL in water , *OPTICAL properties , *OCEAN , *VISIBLE spectra - Abstract
In recent years, ultraviolet (UV) bands have received increasing attention from the ocean colour remote sensing community, as they may contribute to improving atmospheric correction and inherent optical properties (IOPs) retrieval. However, most ocean colour satellite sensors do not have UV bands, and the accurate retrieval of UV remote sensing reflectance (Rrs) from UV satellite data is still a challenge. In order to address this problem, this study proposes a hybrid approach for estimating UV Rrs from the visible bands. The approach was implemented with two popular ocean colour satellite sensors, i.e. GCOM-C SGLI and Sentinel-3 OLCI. In situ Rrs collected globally and simulated Rrs spectra were used to develop UV Rrs retrieval models, and UV Rrs values at 360, 380 and 400 nm were estimated from visible Rrs spectra. The performances of the established models were evaluated using in situ Rrs and satellite data, and applied to a semi-analytical algorithm for IOPs retrieval. The results showed that: (i) UV Rrs retrieval models had low uncertainties with mean absolute percentage differences (MAPD) less than 5%; (ii) the model assessment with in situ Rrs showed high accuracy (r = 0.92–1.00 and MAPD = 1.11%–10.95%) in both clear open ocean and optically complex waters; (iii) the model assessment with satellite data indicated that model-estimated UV Rrs were more consistent with in situ values than satellite-derived UV Rrs; and (iv) model-estimated UV Rrs may improve the decomposition accuracy of absorption coefficients in semi-analytical IOPs algorithm. Thus, the proposed method has great potentials for reconstructing UV Rrs data and improving IOPs retrieval for historical satellite sensors, and might also be useful for UV-based atmospheric correction algorithms. [Display omitted] • A scheme is developed to estimate UV Rrs from visible ocean colour bands. • The scheme works well for both clear oceanic and optically complex waters. • Model-estimated UV Rrs are even better than satellite-sensed values. • The method holds potentials for improving and extending satellite data. [ABSTRACT FROM AUTHOR] more...
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- 2021
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11. Estimation of cell abundances of picophytoplankton based on the absorption coefficient of phytoplankton in the South China sea.
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Zheng, Wendi, Zhou, Wen, Cao, Wenxi, Deng, Lin, Wang, Guifeng, Xu, Zhantang, Li, Cai, Yang, Yuezhong, Zeng, Kai, Zhang, Yu, and Hu, Shuibo
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ABSORPTION coefficients , *CARBON cycle , *STANDARD deviations , *PHYTOPLANKTON , *TERRITORIAL waters - Abstract
Picophytoplankton are essential components of phytoplankton in the oligotrophic South China Sea (SCS). Understanding the variation in the picophytoplankton community structure will provide important information about primary production and the biogeochemical cycling of carbon in the SCS. Based on a field dataset from the SCS , we developed empirical algorithms using the absorption coefficient of phytoplankton at 443 nm [ a ph (443)] as the input to estimate the cell abundances of picophytoplankton, including Prochlorococcus (Pro), Synechococcus (Syn), and autotrophic picoeukaryotes (PE), in the SCS. Evaluation of algorithm performances demonstrated good agreement with field measurements. The root mean square errors and the mean absolute errors (MAE s) between the algorithm derivations and measurements were 0.44, 0.35, and 0.29, and 2.67, 2.02, and 1.88 for the cell abundances of Pro , Syn , and PE , respectively. The match-up comparisons showed that the satellite-derived cell abundances of picophytoplankton (e.g., MAE s ranged from 1.73 to 2.76) also agreed with the field data. We also analyzed the influences of both temperature and nutrient concentration on algorithm performance. The influence of temperature on the algorithms was not significant because the data were mainly collected in the summer, and the analysis should be repeated in the future with data from other seasons. The algorithm for estimating cell abundance of Syn was sensitive to variations in nutrient levels and herbivory pressure in coastal waters where nano- or microphytoplankton dominated. The input of our algorithms, a ph (443), was easily obtained from field measurements and remote sensing. These algorithms provide a relatively easy way to estimate the cell abundances of picophytoplankton and provide data for studying the picophytoplankton community structure in the SCS. • We developed new empirical algorithms for remote cell abundances of picophytoplankton estimations in the South China Sea. • The influences of temperature on the algorithms were not significant as the data were mainly collected in summer. • The nutrient and herbivory pressure had a certain influence on the Synechococcus algorithm. • The 10-years averaged distributions of cell abundances of picophytoplankton in the SCS were explored using MODIS images. [ABSTRACT FROM AUTHOR] more...
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- 2021
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