Secchi depth, an important optical characteristic of water, is a useful index of water quality and is widely used in many
environmental studies. The Yellow Sea and the East China Sea are typical case 2 waters, where concentrations of
suspended matter, phytoplankton pigments, and colored dissolved organic matter are higher than those in other open
oceans. Two cruises were conducted to investigate the water optical characteristics in the Yellow Sea and the East China
Sea in May and June, 2009. 62 water sampling stations of Secchi disk depth were measured in situ in day time, and their
values were in the range of 0.0112 to 15.6 m with the mean of 6.72 m and a standard deviation of 3.18 m. In this paper,
we adapted a quasi-analytical algorithm to estimate the Secchi depth from satellite ocean data in both coastal and oceanic
waters. The development of the algorithm is based on the use of in situ measurements and 8-day MODIS-Aqua remote
sensing reflectance data with 4 km spatial resolution. More than 39 matchups were compiled for the MODIS sensor by
spatial-temporal matching. The comparison between water transparency retrievals from remote sensing data and in situ
measurements yields showed that the determination coefficient was 0.60 and a root mean square error of 8.4 m. This
study suggests that the quasi-analytical algorithm provide a promising result on in situ data. In the future, maps of ocean
transparency for this area will be derived using this algorithm.
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.
It is challenging that accurate assessment of chlorophyll-a concentration by remote sensing in coastal waters. Chla
concentration is commonly retrieved by blue-green ratio in open ocean waters. And this method is efficient in open
ocean waters. But this method is confined when applied to coastal or inland waters, because of abundant variable CDOM
and tripton. It is very difficult to retrieve chla of coastal or estuary waters because of overlap of absorption and
backscattering caused by CDOM and tripton. Dall’Olmo et al put forward a semi-analytical retrieval model of chla,
three-band model. The conceptual three-band model has been successfully applied to estimate chla in turbid and
eutrophic waters by tuning the band position in accordance with the spectral properties.The aim of this paper is to testify
the three-band model that could resolve this problem. The three-band model was tuned in accord with optical properties
and the bands were optimized for accurate estimation. Finally, we found a good linear relationship between chlorophyll-a
and three-band model, with the determination coefficient of 0.63 and the RMSE of 2.22μg·L-1. Furthermore, the in situ
spectral data was averaged to the band range of MERIS (band7, band9 and band10) and developed a simulated threeband
model. A good linear relationship could be found between [(B7-1-B9-1)×B10] and chlorophyll-a, with the
determination coefficient of 0.59 and the RMSE of 0.72μg·L-1. The findings demonstrated that the three-band model of
MERIS could be applied to retrieve chlorophyll-a concentration of Yantai coastal waters.
Remote sensing image classification is an important and complex problem. Conventional remote sensing image
classification methods are mostly based on Bayesian subjective probability theory, but there are many defects for its
uncertainty. This paper firstly introduces evidence theory and decision tree method. Then it emphatically introduces the
function of support degree that evidence theory is used on pattern recognition. Combining the D-S evidence theory with
the decision tree algorithm, a D-S evidence theory decision tree method is proposed, where the support degree function is
the tie. The method is used to classify the classes, such as water, urban land and green land with the exclusive spectral
feature parameters as input values, and produce three classification images of support degree. Then proper threshold
value is chosen and according image is handled with the method of binarization. Then overlay handling is done with
these images according to the type of classifications, finally the initial result is obtained. Then further accuracy
assessment will be done. If initial classification accuracy is unfit for the requirement, reclassification for images with
support degree of less than threshold is conducted until final classification meets the accuracy requirements. Compared
to Bayesian classification, main advantages of this method are that it can perform reclassification and reach a very high
accuracy. This method is finally used to classify the land use of Yantai Economic and Technological Development Zone
to four classes such as urban land, green land and water, and effectively support the classification.
Hyperspectral technique is considered as one promising tool to solve the problems in monitoring optically-complex waters, which can be applied in optical sensors on board bouy, plane and satellite. In order to apply the technique in the in-situ chlorophyll monitoring of estuarine turbid waters, two cruises were carried out in May, 2004 and August, 2006, respectively, in Pearl River Estuary, China. In the cruises, water samples were collected at each sample station, a portable field spectroradiometer was used simultaneously to measure the downwelling sky radiance, and upwelling radiance of water and reference plaque, and the reflectance was calculated out. Further, the original reflectance spectra with 0.38 nm spectral resolution were resampled to 10 nm resolution, and then derivative spectra were processed. The results of correlation analysis between the chlorophyll-a concentrations and derivative spectra indicate that the second derivative spectra especially at 670 nm can be used to estimate chlorophyll-aconcentration of turbid estuarine waters, which suggests a new way for the in-situ chlorophyll measurement in the optically complex waters.
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