Evolution of river delta is highly related to the deposition and re-suspension of sediments. At the interacting zone of fresh river discharge and seawater, suspended sediments concentration (SSC) can vary sharply from a few mg/L to thousands of mg/L; thus, mapping the distribution of SSC will provide the first information about sediments transportation. The high spatial resolution (30 m) and high revisit frequency (2 day) of CCD imager on board the Chinese environment-monitoring satellite constellation: HJ-1A and HJ-1B, enable an effective observation of the fine dynamics of suspended sediments. In this work, three intensive cruises in the flooding season and dry season of Yellow River, were carried out to explore the SSC retrieval algorithms on the basis of HJ-1 CCD imageries. Quasi-simultaneous in-situ SSC data were collected with the pass of HJ-1 over the Yellow River Estuary and its vicinity waters, and a local empirical retrieval algorithm of SSC was established against the TOA (top of atmosphere) reflectance of HJ-1 CCD bands with the correction of Rayleigh scattering. This algorithm can be applied to very turbid waters with thousands of mg/L of SSC.
Knowing the inherent optical properties (IOPs) of water bodies is useful for many water environment studies and applications. To derive the IOPs from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was modified and validated for the highly turbid Poyang Lake in China. In order to supplement and expand the dynamic variation range of the measured water optical properties, a Hydrolight simulated dataset was generated to develop a regional QAA (QAA710) for this area. The QAA710 model was then validated with simulated data, simulated data with random noise, and in situ data. The results show that the effects of random noise (within ±20%) of remote-sensing reflectance on the derived total absorption coefficients (at) and the particulate backscattering coefficients (bbp) by the QAA710 model are insignificant (a band-averaged mean relative error of 4.1% and 12.0%, respectively). The validation of in situat shows a 28.6% mean relative difference. The model process, modeling data, and validation data introduce uncertainties into the derived results. These analyses demonstrate that the QAA710 model, based on the characterization of local environments, performs well in retrieving Poyang Lake’s IOPs.
In the ocean color remote sensing, about 90% of sensor-measured signals contributed by atmosphere need to be removed
by implementing atmospheric correction, which is a key procedure for quantitatively retrieving water color parameters
from the remotely sensed observations. In the coastal and inland turbid waters (Case II waters), the standard atmospheric
correction algorithm embedded in SeaDAS frequently yields negative water-leaving radiance values, it is partly because
of the overestimation of atmospheric aerosol influence. A modified atmospheric correction method was proposed to
correct the atmospheric aerosol effect in Case II waters in this paper. The default NASA atmospheric correction scheme
was used to retrieve the primal aerosol information and identify the standard atmospheric correction algorithm failure
area. The aerosol information derived from CALIPSO LIDAR and sun photometer on the same day was utilized to
retrieve the water-leaving radiance in Case II waters. From the comparison with the results from SeaDAS and the in-situ
data, the results showed that the modified atmospheric correction method could be effective in Case II waters.
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