Shaoling Shang
Xiamen University
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Publication
Featured researches published by Shaoling Shang.
Journal of Geophysical Research | 2010
Chuanmin Hu; Zhongping Lee; Ronghua Ma; Kun Yu; Daqiu Li; Shaoling Shang
U.S. NASA [NNX09AE17G, NNX09AV56G]; NOAA [NA06NES4400004]; National Natural Science Foundation of China [40871168]; Ministry of Education of China [B07034]
Geophysical Research Letters | 2008
Shaoling Shang; Li Li; Fengqin Sun; Jingyu Wu; Chuanmin Hu; Dewen Chen; Xiuren Ning; Yun Qiu; Caiyun Zhang; Shaoping Shang
Subsequently, against a background level of 0.08 mg/m 3 , average Chla within the area of 12.60–16.49N, 112.17– 117.05E increased to 0.14 mg/m 3 on 11/12 and then to 0.37 mg/m 3 on 11/14. Dissolved organic matter and detritus were differentiated from Chla using a recent bio-optical algorithm. They contributed 64% to the increase of total absorption immediately after Lingling, while most of the changes later (74%) were due to phytoplankton. The area under Lingling’s impact covered ca. 3 latitude and 4 longitude, which is much greater than the two summer cases previously observed in the northern SCS. This event lasted for ca.15 days, and resulted in carbon fixation in the order of 0.4 Mt. Such a drastic response was attributed to the coupling of typhoon-induced nutrient pumping with the pre-established cyclonic gyre in the central SCS driven by the prevailing northeast monsoon. Citation: Shang, S., L. Li, F. Sun, J. Wu, C. Hu, D. Chen, X. Ning, Y. Qiu, C. Zhang, and S. Shang (2008), Changes of temperature and bio-optical properties in the South China Sea in response to Typhoon Lingling, 2001, Geophys. Res. Lett., 35, L10602, doi:10.1029/ 2008GL033502.
Ocean Science Journal | 2012
Kevin Ruddick; Quinten Vanhellemont; Jing Yan; Griet Neukermans; Guomei Wei; Shaoling Shang
This study assesses the performance of the Geostationary Ocean Imager (GOCI) for mapping of suspended particulate matter in the Bohai Sea, a turbid water region. GOCI imagery for remote sensing reflectance and Total Suspended Solids (TSS) is analysed in detail for two days in June 2011 (8 images per day). Both instantaneous and daily composite maps are considered and a comparison is made with corresponding reflectance and TSS products from MODIS-AQUA. Results show TSS distributions corresponding to previous studies of the region. The advantage of the higher acquisition frequency (8 images/day instead of 1) offered by GOCI is clearly demonstrated in the daily composite which is more complete during this period of scattered but moving clouds. Consideration of temporal variation over the day indicates low natural variability but some artificial variability from processing errors — this analysis provides a first indication of how the higher frequency of data from geostationary ocean colour could lead to improved data quality control via temporal coherency outlier detection. While there is room for improvement on the GOCI calibration, atmospheric correction and retrieval algorithms, the current study suggests that the GOCI data can already be used now to study qualitatively sediment dynamics except in the extremely turbid waters which are masked out of the current dataset. In a wider context, it is considered that the technical challenges of geostationary ocean colour have been met by the GOCI concept, and, notwithstanding potential improvements on the concept and data processing methods, it is recommended that this mission serve as a model for future geostationary ocean colour sensors over Europe/Africa and the Americas.
Applied Optics | 2014
Zhongping Lee; Shaoling Shang; Chuanmin Hu; Giuseppe Zibordi
Using 901 remote-sensing reflectance spectra (R(rs)(λ), sr⁻¹, λ from 400 to 700 nm with a 5 nm resolution), we evaluated the correlations of R(rs)(λ) between neighboring spectral bands in order to characterize (1) the spectral interdependence of R(rs)(λ) at different bands and (2) to what extent hyperspectral R(rs)(λ) can be reconstructed from multiband measurements. The 901 R(rs) spectra were measured over a wide variety of aquatic environments in which water color varied from oceanic blue to coastal green or brown, with chlorophyll-a concentrations ranging from ~0.02 to >100 mg m⁻³, bottom depths from ~1 m to >1000 m, and bottom substrates including sand, coral reef, and seagrass. The correlation coefficient of R(rs)(λ) between neighboring bands at center wavelengths λ(k) and λ(l), r(Δλ)(λ(k), λ(l)), was evaluated systematically, with the spectral gap (Δλ=λ(l)-λ(k)) changing between 5, 10, 15, 20, 25, and 30 nm, respectively. It was found that r(Δλ) decreased with increasing Δλ, but remained >0.97 for Δλ≤20 nm for all spectral bands. Further, using 15 spectral bands between 400 and 710 nm, we reconstructed, via multivariant linear regression, hyperspectral R(rs)(λ) (from 400 to 700 nm with a 5 nm resolution). The percentage difference between measured and reconstructed R(rs) for each band in the 400-700 nm range was generally less than 1%, with a correlation coefficient close to 1.0. The mean absolute error between measured and reconstructed R(rs) was about 0.00002 sr⁻¹ for each band, which is significantly smaller than the R(rs) uncertainties from all past and current ocean color satellite radiometric products. These results echo findings of earlier studies that R(rs) measurements at ~15 spectral bands in the visible domain can provide nearly identical spectral information as with hyperspectral (contiguous bands at 5 nm spectral resolution) measurements. Such results provide insights for data storage and handling of large volume hyperspectral data as well as for the design of future ocean color satellite sensors.
Journal of Geophysical Research | 2010
Zhongping Lee; Shaoling Shang; Chuanmin Hu; Marlon R. Lewis; Robert Arnone; Yonghong Li; Bertrand Lubac
NASA ; Naval Research Laboratory ; U.S. Office of Naval Research ; Canadian Natural Sciences and Engineering Research Council ; National High-Tech Research and Development Programme of China [2006AA09A302, 2008AA09Z108]; NSF-China [40821063]
Eos, Transactions American Geophysical Union | 2010
Zhongping Lee; Chuanmin Hu; Brandon Casey; Shaoling Shang; Heidi M. Dierssen; Robert A. Arnone
Knowledge of ocean bathymetry is important, not only for navigation but also for scientific studies of the oceans volume, ecology, and circulation, all of which are related to Earths climate. In coastal regions, moreover, detailed bathymetric maps are critical for storm surge modeling, marine power plant planning, understanding of ecosystem connectivity, coastal management, and change analyses. Because ocean areas are enormously large and ship surveys have limited coverage, adequate bathymetric data are still lacking throughout the global ocean.
Marine Pollution Bulletin | 1999
Huasheng Hong; Shaoling Shang; Bangqin Huang
Abstract Atmospheric deposition, benthic release, freshwater runoff and sewage discharge inputs of phosphorus to the Xiamen Western Sea were estimated. It was found that benthic release was probably most significant, having a flux rate of the order of 1 mmol P m−2 d−1. The Xiamen Western Sea is P limited, and the results of this study suggest that pulse inputs of P may play an important role in the triggering of red tides.
Journal of Geophysical Research | 2012
Young-Heon Jo; Minhan Dai; Weidong Zhai; Xiao-Hai Yan; Shaoling Shang
[1] Using a neural networking (NN) approach, we developed an algorithm primarily based uponsea surface temperature (SST) and chlorophyll (Chla) to estimate thepartial pressure of carbon dioxide (pCO2) at the sea surface in the northern South China Sea (NSCS). Randomly selected in situ data collected from May 2001, February and July 2004 cruises were used to develop and test the predictive capabilities of the NN based algorithm with four inputs (SST, Chla, longitudes and latitudes). The comparison revealed a high correlation coefficient of 0.98 with a root mean square error (RMSE) of 6.9 matm. We subsequently applied our NN algorithm to satellite SST and Chla measurements, with associated longitudes and latitudes, to obtain surface water pCO2. The resulting monthly mean pCO2 map derived from the satellite measurements agreed reasonably well with the in situ observations showing a generally homogeneous distribution in the offshore regions. The pCO2 exerts a very dynamic feature in nearshore regions, especially in the coastal upwelling and estuarine plume regions. We identified three low pCO2 zones (<330 matm), two of which are influenced by coastal upwelling: off Hainan island in the western part of the NSCS; and off Guangdong province in the eastern part of the NSCS. The path of the Pearl River plume on the shelf was another zone with low pCO2. For the monthly mean pCO2 variations estimated based on the MODIS-SST and -Chla values, an RMSE of � 6 matm may be attributable to the measurement errors associated with MODIS measurements. As a first order estimation, we used the same sampling periods of remote sensing and in situ measurements, and were able to estimate pCO2 with an accuracy of 12.05 matm for onshore regions and 13.0 matm for offshore regions, but with combined uncertainties associated with the NN Testing algorithm and MODIS SST and Chla measurements.
Optics Express | 2013
Shaohui Huang; Yonghong Li; Shaoping Shang; Shaoling Shang
Spectral optimization algorithm (SOA) is a well-accepted scheme for the retrieval of water constituents from the measurement of ocean color radiometry. It defines an error function between the input and output remote sensing reflectance spectrum, with the latter modeled with a few variables that represent the optically active properties, while the variables are solved numerically by minimizing the error function. In this paper, with data from numerical simulations and field measurements as input, we evaluate four computational methods for minimization (optimization) for their efficiency and accuracy on solutions, and illustrate impact of bio-optical models on the retrievals. The four optimization routines are the Levenberg-Marquardt (LM), the Generalized Reduced Gradient (GRG), the Downhill Simplex Method (Amoeba), and the Simulated Annealing-Downhill Simplex (i.e. SA + Amoeba, hereafter abbreviated as SAA). The Garver-Siegel-Maritorena SOA model is used as a base to test these computational methods. It is observed that 1) LM is the fastest method, but SAA has the largest number of valid retrievals; 2) the quality of final solutions are strongly influenced by the forms of spectral models (or eigen functions); and 3) dynamically-varying eigen functions are necessary to obtain smaller errors for both reflectance spectrum and retrievals. Results of this study provide helpful guidance for the selection of a computational method and spectral models if an SOA scheme is to be used to process ocean color images.
Geophysical Research Letters | 2014
Robert T. O'Malley; Michael J. Behrenfeld; Toby K. Westberry; Allen J. Milligan; Shaoling Shang; Jing Yan
Since June 2010, the Geostationary Ocean Color Imager (GOCI) has been collecting the first diurnally resolved satellite ocean measurements. Here GOCI retrievals of phytoplankton chlorophyll concentration and fluorescence are used to evaluate daily to seasonal changes in photophysiological properties. We focus on nonphotochemical quenching (NPQ) processes that protect phytoplankton from high light damage and cause strong diurnal cycles in fluorescence emission. This NPQ signal varies seasonally, with maxima in winter and minima in summer. Contrary to expectations from laboratory studies under constant light conditions, this pattern is highly consistent with an earlier conceptual model and recent field observations. The same seasonal cycle is registered in fluorescence data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer Aqua satellite sensor. GOCI data reveal a strong correlation between mixed layer growth irradiance and fluorescence-derived phytoplankton photoacclimation state that can provide a path for mechanistically accounting for NPQ variability and, subsequently, retrieving information on iron stress in global phytoplankton populations.