This study uses the MODIS-Aqua satellite data provided by the National Aeronautics and Space Administration (NASA) and matches with the time and location of the chlorophyll-a concentration data measured by SeaBASS to select the satellite data time and observation area. The number of matched data is 924. Firstly, remote sensing reflectance (Rrs) is used to classify satellite remote sensing data into different water bodies, and then the best chlorophyll-a concentration algorithm is established. The results show that the mean percentage difference (MPD) in Case 2 water is 131.2% through comparing the percentage of chlorophyll-a provided by MODIS with the in-situ observations. In addition, the chlorophyll-a concentration of the new algorithm compared with the in-situ chlorophyll-a concentration are also calculated. The mean percentage difference in Case 2 water is 26.6%, and the average chlorophyll-a is 6.16 mg/m3 , which is much closer to the in-situ value,7.22 mg/m3 than the average chlorophyll-a of MODIS, 13.7 mg/m3. The chlorophyll-a concentration deduced by the new algorithm of this study is consistent with the in-situ values in Case 2 water, and it is much more convergent than the data of MODIS. Obviously, the new algorithm established in this study can be used to improve the chlorophyll-a concentration estimation results in Case 2 water. When the new algorithm is applied to calculate the chlorophyll-a concentration of the marginal Northwestern Pacific, the value is still higher than the offshore waters. Additionally, the chlorophyll-a concentration calculated by this new algorithm is lower than the value provided by MODIS, but the difference between them in the offshore waters is small. However, the algorithm of this study can improve the overestimation of the original MODIS value.
Katabatic winds generally flow from mountains or hills down to their lee side in the paths of depression. If the mountain or hill is near a coast, the katabatic winds may cause imprints on the sea surface. The katabatic wind pattern shown on a synthetic aperture radar (SAR) image is a bright-dark region that mirrors the coastal mountain topography. In this study, bright regions on SAR images caused by katabatic winds are found in the west of Hengchun Peninsula where is located in the southern Taiwan. The katabatic winds cause the sea state variations and then the sea surface temperature changes. Relationships between normalized radar cross section (NRCS) and sea surface temperature (SST) as well as the temperature difference between air and sea in the west of Hengchun Peninsula are investigated to find out the air-sea heat transfer. The results show that 1) the SST decreases when the NRCS increases, that is, the higher wind speed would cause the SST lower; and 2) the gradient of linear relationship between NRCS and SST is related to the temperature difference between air and sea, that is, the higher temperature difference could increase the release of heat from the ocean to the atmosphere.
Tide gauge data provided by the University of Hawaii Sea Level Center and daily sea surface temperature (SST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) product are used in this study to analyze the influence of tide on the SST in the seas of Northwestern Pacific. In the marginal region, the climatology SST is lower in the northwestern area than that in the southeastern area. In the coastal region, the SST at spring tide is higher than that at neap tide in winter, but it is lower in other seasons. In the adjacent waters of East China Sea and Yellow Sea, the SST at spring tide is higher than that at neap tide in winter and summer but it is lower in spring and autumn. In the open ocean region, the SST at spring tide is higher than that at neap tide in winter, but it is lower in other seasons. In conclusion, not only the river discharge and topography, but also tides could influence the SST variations, especially in the open ocean region.
Changes of oceanic salinity are highly related to the variations of evaporation and precipitation. To understand the influence of rainfall on the sea surface salinity (SSS) in the waters adjacent to Taiwan, satellite remote sensing data from the year of 2012 to 2014 are employed in this study. The daily rain rate data obtained from Special Sensor Microwave Imager (SSM/I), Tropical Rainfall Measuring Mission’s Microwave Imager (TRMM/TMI), Advanced Microwave Scanning Radiometer (AMSR), and WindSat Polarimetric Radiometer. The SSS data was derived from the measurements of radiometer instruments onboard the Aquarius satellite. The results show the average values of SSS in east of Taiwan, east of Luzon and South China Sea are 33.83 psu, 34.05 psu, and 32.84 psu, respectively, in the condition of daily rain rate higher than 1 mm/hr. In contrast to the rainfall condition, the average values of SSS are 34.07 psu, 34.26 psu, and 33.09 psu in the three areas, respectively at no rain condition (rain rate less than 1 mm/hr). During the cases of heavy rainfall caused by spiral rain bands of typhoon, the SSS is diluted with an average value of -0.78 psu when the average rain rate is higher than 4 mm/hr. However, the SSS was increased after temporarily decreased during the typhoon cases. A possible reason to explain this phenomenon is that the heavy rainfall caused by the spiral rain bands of typhoon may dilute the sea surface water, but the strong winds can uplift the higher salinity of subsurface water to the sea surface.
A series of the Orbview-2/SeaWiFS (Sea-viewing Wide Field-of-view Sensor) images during the period from 1997
to 2003 is used to understand the spatial and temporal distribution of the chlorophyll-a concentration (Chl-a) in the
Taiwan Strait (TS). It is found that the area with higher Chl-a is mainly along the western TS; it extends more offshore in
cold seasons. The lowest Chl-a is always inside the deep Peng-Hu Channel, it can spread further northward in summer.
From mode 1 results of the Empirical Orthogonal Function (EOF) analysis, we find the Chl-a in La Nina years (during
the period from June 1998 to May 2001) showing greater variation than the other El Nino or normal years. The EOF1
results also indicate the highest Chl-a always in fall. Meanwhile, the peak in the 1997/1998 El Nino fall was the lowest
maximum, while the lowest Chl-a is mainly in winter, but its interannual variation is not so clear.
Synthetic Aperture Radar (SAR) images from European Remote Sensing (ERS) satellites are used to investigate oil spill from ship navigations in the water adjacent to Taiwan. A total number of 136 images taken from 1993 to 1997 are used in this study. On the 136 images, only 46 images showing the possibility of oil spill which are based on the position and the shape of the discharge, the path of the ship, the sea characteristics of the area, and the weather conditions. The result shows that oil spill occurs most frequently in spring and least in winter. The sea area off eastern Taiwan has a probability which far surpassed other areas, followed by the middle sea area, the northern sea area, and the southern sea area. Regarding the oil spills at different areas with the distance to the shore, the oil spills at the middle area, with an average distance of 50 km (28 nautical miles), is closer than those at other areas. The statistical analysis demonstrates that the oil spill around Taiwan mostly occurs over 44 km (24 nautical miles) away from shore. Therefore, it is obvious that the probability of oil spills occurring as a ship leaves or enters the harbor is not high. Instead, the majority of oil spills takes place from middle to long distance navigating fishing boats as well as from oil and cargo freighters navigating international waterways.
The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.
The sea surface temperature (SST) front we considered here is the boundary separating the northern colder water and southern warm water in the Taiwan Strait (TS). It always coincides with the northern edge of the permanent warm current along the deep Peng-Hu Channel northward into the TS. The NOAA/AVHRR SST data during 1997-2000 is used to see the variability of this SST front. From gradient empirical orthogonal function (GEOF) analysis of the SST data we found that the SST front is weaker in summer and much stronger in winter. Meanwhile, the SST front was weaker in 1997 El Nino summer than 1998 La Nina summer, while stronger in 1997/1998 El Nino winter than 1998/1999 La Nina winter. The ERS-2 scatterometer wind stress data is also applied in this study, it is suggested that the wind variation seems to be the main factor to induce the above variation of the SST pattern in the TS.
Sea surface temperature (SST) is an important parameter for the study of oceanic environment. In this study we used the SST data derived from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) series satellites to study the SST trends in the waters adjacent to Taiwan from 1982 to 2003. The Empirical Mode Decomposition (EMD) method was employed to retrieve the trend. Results show that the SST in the waters adjacent to Taiwan had a warming trend of about 0.50° C/decade and 0.47° C/decade in the Taiwan Strait and in the Kuroshio region east of Taiwan, respectively. The warming trend in winter is much higher than that in summer. This could be the reason why the people who live in Taiwan felt warm winter in recent years. The waters adjacent to Taiwan are also located at the high warming trend areas in the world. They are much higher than the mean trend of global ocean. Not only the waters around Taiwan are in the high warming areas, but also the oceans around China are. The high warming areas in the China oceans seem to be related to the distribution of atmospheric aerosols in the western Pacific Ocean.
Alternating dark-bright patterns along the coast on Landsat MSS and the ERS-1 SAR images were recognized to be the image of a coastal lee wave. Such waves are called coastal lee waves because they occur along the lee side of the coast. The first case was noted in the offshore area of the Delaware Bay in the middle Atlantic Bight shown as a wave- like cloud pattern on MSS images taken in December 14, 1975. The second case was detected by the ERS-1 SAR shown on the image of the Taiwan Strait taken on December 8, 1994. The average wavelength is 2 km, ranging from 0.3 km to 4.2 km. The crest lines with length from 20 km to more than 100 km are generally parallel to the coastline. The horizontal distribution range is a band 20 km wide, 20-100 km offshore. The vertical extent of the disturbance reaches from the ocean surface to the top of cumulus. The waves manifest solitary characteristics. The seasonal land-breeze circulation is being proposed as a major generation mechanism for the observed lee waves.