Based on satellite remote sensing to study the effects of ocean color and Sea Surface Temperature (SST) is the leading
method at present. From 2000 to 2008, there were about 70 times storms or typhoons passed over the South China Sea
Area (108-120°E, 14-24°N). In this article, authors used SeaWiFS, MODIS and serials of NOAA satellite data to
statistic analysis the effects of chlorophyll-a (Chl-a) and SST in the study area caused by tropic storms and typhoons
near last decade and found: firstly, the Ekman Pumping Velocity (EPV) was up-to 1.4x10-3 m s-1 by typhoon
CHAUCHU in 2006, and was more ten times than that without typhoon. It pumped the nutrients to the euphotic zone
and improved the increment of Chl-a concentration with the rate of 58.33%. and this typhoon caused the maximum
decrease of SST was up to 7°C. At the same time, the average increment of Chl-a in the whole study area was about
21.13% but the decrease rate of SST was 6.36% by 16 typhoons. From the results of statistic analysis we found out that
the isopycnal displacement of the seasonal thermocline explains 53% and the weight of the typhoon explains 55% of the
variance of Chl-a by typhoons; In addition, the maximum amplitude of the increment of Chl-a in the study area was near
to the east of Vietnam, it was up to 53%. But near the pear river estuary area with high Suspended Sand Concentration
(SSC) the chl-a density decreased after typhoon and the descent rate of Chl-a was about 35%.
Using NOAA and MODIS (Terra & Aqua) satellites data, and mainly basing on 3×3 degree square as study area, the
authors systematically analysis the effects of Sea Surface Temperature (SST) caused by 37 typhoons which passed by the
Northwest Pacific Ocean from 2000 to 2008 and find: (1) In the Northwest Pacific Ocean area, the SST without typhoon
is averagely 26.10°C, but the SST is averagely 22.90°C during typhoon happened. The SST averagely decreases 3.20°C
with the drop rate of 11.55%, and the maximal fall of SST is 7.79°C by typhoon. At the same time, the sustaining time
with low SST is usually 2~5 days, and the time mainly lies on the lingering time of typhoon in the sea areas. After
typhoon, the SST comebacks to the normal level before typhoon had happened. This can be seen clearly from the change
of the SST before and after typhoon SAOMAI and LEKIMA .(2) In the study area of typhoon SAOMAI and LEKIMA
passed through, there are certain rightward bias of the distribution of the decrease of SST before and after typhoon. (3)
The decrease of SST during typhoon is positive correlation with the wind speed and negative correlation with the moving
speed of typhoon respectively and the correlation coefficient is less than 0.3, but it is better positive correlation with the
weight of typhoon and the correlation coefficient rises to 0.47.In conclusion, the effect of SST by typhoon is notable, and
based on remote sensing to study this effects is effective means.
Chlorophyll-a (Chl-a) retrieval in case II waters is of intense research now. And due to the high turbidity of case II
waters, most of the Chl-a information we have retrieved is the signal of suspended sediment concentrations. In order to
improve the accuracy, we not only study the new retrieval algorithm, but also get more in-situ data sets. Thus, this paper
studies the in-situ data in the Changjiang River Estuary and adjacent sea from Apr. 5th to May 5th in 2007, and the
results show that the Changjiang diluted water (CDW) extends offshore with a bimodal structure during the observation,
one extending toward the southeast, the other toward the northeast, the main axis of the CDW extending toward the
northeast. There exists two centers of higher Chl-a concentration near the Changjiang River mouth, (122.45E,31.75N)
and (123.2E, 30.5N), and the maximum concentrations have reached 6.5ug/L,6.3ug/L respectively. The Chl-a
concentration would be increased significantly by the continual strong winds. The horizontal Chl-a maximum
distribution is closely related to the position of CDW and the current structure.
Sea surface temperature (SST) is both an important variable for weather and ocean forecasting, but also a key indicator
of climate change. Predicting future SST at different time scales constitutes an important scientific problem. The
traditional approach to prediction is achieved through numerical simulation, but it is difficult to obtain a detailed
knowledge of ocean initial conditions and forcing. This paper proposes a improved prediction system based on SOFT
proposed by Alvarez et al and studies the predictability of SST at different time scales, i.e., 5 day, 10 day, 15 day, 20 day
and month ahead. This method is used to forecast the SST in the Yangtze River estuary and its adjacent areas. The period
of time ranging from Jan 1st 2000 to Dec 31st 2005 is employed to build the prediction system and the period of time
ranging from Jan 1st 2006 to Dec 31st 2007 is employed to validate the performance of this prediction system. Results
indicate: The prediction errors of 5 day,10 day,15 day, 20 day and monthly ahead are 0.78°C,0.86°C,0.90°C,1.00°C and
1.45°C respectively. The longer of time scales prediction, the worse of prediction capability. Compared with the SOFT
system proposed by Alvarez et al, the improved prediction system is more robust. Merging more satellite data and trying
to better reflect the real state of ocean variables, we can greatly improve the predictive precision of long time scale.
At the beginning of 2008, the south area of China suffered a rare heavy snow and low temperature weather, which
brought enormous economic lose and broke the environment. The abnormal weather also influenced the ocean color
environment. Through analysis of MODIS remote sensing 3A data during 2 months before and after the snowstorm and
low temperature weather, the author finds that, on the one hand,compared with same period of last year, the sea surface
temperature (SST) from the East China Sea to the South China Sea (18°N-32°N 108°E-126°E descended 2.57°C; the
average chlorophyll-a concentration(CHL-a)rose from 1.198 mg. m-3 to 1.75 mg. m-3 in the snowstorm and low
temperature weather period from January 11 to 31 of 2008 which was 1.46 times more than that of the same period of
last year. On the other hand, compared with the period before snowstorm,the SST decreased from 22.42°C to 18.34°C but the CHL-a rose from 1.32 mg. m-3 to 1.60 mg. m-3 after the snowstorm. In addition,the sea water transparency(SDD)
had a certain increase in the open sea of South China Sea, but the suspended sediment concentration(SSC) increased
significantly near the seashore, the Yangtse River Estuary and the Pearl River Estuary, which increased 200% compared
with the period before the snowstorm 2008. Through researching and indicating, the main reason of increase of the
CHL-a in the near seashore area(the I water) was more probably due to the increase of the SSC, and the CHL-a by
remote sensing has greater error, which rising from the high SSC led to the increase of CHL-a; but in the open sea the
increase of CHL-a is that the SDD improved and then the euphotic increased. As a result,this could promote the growth
of primary productivity. Therefore, it faces the better applied foreground to monitor the influence on the ocean
ecosystem environment caused by the snowstorm and low temperature weather through remote sensing.
A complete data set is crucial for many applications of satellite images. Therefore, this paper tries to reconstruct the
missing data sets by combining Empirical Orthogonal Functions(EOF) decomposition with Kriging methods. The
EOF-based method is an effective way of reconstructing missing data for large gappiness and can maintain the
macro-scale and middle-scale information of oceanographic variables. As for sparse data area (area without data or with
little data all the time), EOF-based method breaks down, while Kriging interpolation turns effective. Here are the main
procedures of EOF-Kriging(EOF-K) method: firstly, the data sets are processed by the EOF decomposition and the
spatial EOFs and temporal EOFs are obtained; then the temporal EOFs are analyzed with Singular Spectrum
Analysis(SSA); thirdly, the sparse data area is interpolated in the spatial EOFs by using Kriging interpolation; lastly, the
missing data is reconstructed by using the modified spatial-temporal EOFs. Furthermore, the EOF-K method has been
applied to a large data set, i.e. 151 daily Sea Surface Temperature satellite images of the East China Sea and its adjacent
areas. After reconstruction with EOF-K, comparing with original data sets, the root mean square error (RMSE) of
cross-validation is 0.58 °C, and comparing with in-situ Argo data, the RMSE is 0.68 °C. Thus, it has been proved that
EOF-K reconstruction method is robust for reconstructing satellite missing data.
A water wake detection method of airphotoes is proposed based on two-dimensional principal component analysis
(2DPCA) of the polar Fourier spectrum. This method improves the traditional Principal Components Analysis to obtain
the image direction from its Fourier power spectrum, transforms the Fourier spectrum to the polar coordinate based on
the image direction, so the polar Fourier spectrum is translation and rotation invariant. Compared to the previous method
of partitioning the Fourier spectrum to achieve texture features, the row 2DPCA, the column 2DPCA and the improved
2DPCA are used to analysis the polar Fourier spectrum. From experiment results of 40 images, it is proved that the
proposed algorithm can fetch the wake texture precisely.
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