Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Satellite observations offer a great approach to investigate the features of major marine parameters, including sea surface chlorophyll and temperature, sea surface height, and the factors derived from these parameters, such like fronts. Our study shows how to use satellite observations to describe major parameters and their relationships. Satellite data science from 2002 to 2017 were used to describe the surface features of the South China Sea.

The satellite observation of chlorophyll is factor, which is used to ocean protection. Factors related to chlorophyll variability were investigated using time series. The method can be applied to other global oceans and will be helpful for understanding marine dynamics and the ecosystem.

We show a step-by-step procedure for acquiring satellite data of different parameters describing the spatial and temporal variabilities and determining interrelationships among different factors. Spatial and temporal variabilities of parameters are obtained. They are empirical orthogonal function and the interrelationship among different factors are acquired by zero correlation coefficients.

For sea surface temperature and sea surface chlorophyll data acquisition, download a dataset of satellite observations from MODIS Aqua, for which the spatial resolution of both datasets is roughly 4.5 kilometers at daily intervals. Store the downloaded satellite files in the data folder and structure the directory of folders as shown. Add the path of the toolbox for NetCDF file in MATLAB and select add with subfolders to enclose the paths of the scripts folder.

The path for all of the required directories of the data and functions will appear in the MATLAB search path. Then load the sea surface temperature data into the analysis software. For sea level anomaly dataset acquisition, download daily sea level anomaly data with a 25 kilometer spatial resolution from the same timeframe and enter the command to load the single day sea level anomaly data.

To obtain the wind speed data, download the wind data from the same time period from an ERA interim re-analysis product and enter the command to read the one-month wind data. The obtained u, v, and time variables represent the zonal and meridian all speeds and the corresponding time respectively. To access the topography dataset, download the high-resolution topography data from the National Centers for Environmental Information website and enter the command to load the topography data into the analysis software.

The XX, YY, and ZZ variables indicate the latitude, longitude, and corresponding depth respectively. Due to the large cloud coverage in the sea surface temperature and sea surface chlorophyll data, use the command to replace the original data with the three-day average data. Because the spatial resolution is not consistent for different datasets, enter the command to interpolate the sea surface temperature and sea surface chlorophyll data into a spatial grid that is the same as the wind and sea level anomaly spatial grid.

Enter the command as indicated to calculate the wind stress and wind stress curl. To calculate the monthly sea surface temperature, wind, and sea level anomaly time series as 30-day averages in each pixel, enter the command as indicated. For spatial smoothing, enter the command to run the script to smooth the three-day average sea surface temperature data in each pixel.

To determine the sea surface temperature gradient, enter the command to run the script to calculate the zonal and meridian all sea surface temperature gradients as the sea surface temperature difference between the nearest two pixels divided by the corresponding distance. To identify a front by testing the value of the sea surface temperature gradient, label the pixel as a potential frontal pixel if the value was larger than a designated threshold. To calculate the monthly frontal probability of observing a front take place for a specific time span, enter the command.

To load the monthly data for analysis, enter the commands and apply an empirical orthogonal function to describe the spatial and temporal variabilities of the different parameters. The program will calculate the magnitude, eigenvalues, and amplitude of the empirical orthogonal functions for the dataset. To determine the correlation at the seasonal scale, enter the command to calculate the correlations between two factors using their time series at each pixel.

Then enter the command to calculate the correlations between the monthly anomalies of the sea surface chlorophyll and other factors. To display the satellite information, enter the command to run the script to generate a showcase of satellite information, including the sea surface chlorophyll, temperature and wind, and sea level anomaly and frontal distribution. Enter the command to display the empirical orthogonal function result.

Then enter the command as indicated to calculate the relationship between the chlorophyll and other factors at seasonal and anomalous fields. The topography has a prominent impact on the spatial distribution of sea surface chlorophyll with high sea surface chlorophyll mainly distributed along the coast of the South China Sea where the topography is shallow. Wind is also influenced by orography with the lease side of mountains characterized by weak wind and a prominent wind stress curl identified southwest of the South China Sea.

The thresholds applied here effectively capture the location of the front and ensure the depiction of the boundaries of entire water masses. In this analysis, empirical orthogonal function one captured a large variance in the northern section of the South China Sea. The corresponding monthly average of the time series showed that the sea surface chlorophyll was elevated during the winter and depressed during the summer.

The region next to the southwest coast was characterized by a weak magnitude, and the corresponding variability was mainly captured by empirical orthogonal function two. Sea surface chlorophyll values were high in the summer and low in the winter, which was mainly out of phase compared to the northern region. Indeed, the monthly time series for the empirical orthogonal functions demonstrated clear seasonal variability with empirical orthogonal function two leading empirical orthogonal function one by approximately four months.

The correlations between the chlorophyll and other factors represents the interrelationships of the factors. For example, in this analysis, the sea surface temperature is negatively correlated with the chlorophyll while the wind stress is positively correlated with the chlorophyll. Thus, a high chlorophyll was associated with a low temperature and strong wind for these data.

Identification the variability of ocean parameters and investigate their relationship with chlorophyll are critical and important for ocean dynamics and the marine ecosystem. Frontal activities are particularly important because high chlorophyll is usually associated with front. Modification may take place to change the threshold of front detection and the best approach to validate the front is to compare them with institute observations.

In summary, using satellite observations can accurately describe the spatial distribution and the temporal variability in ocean surface features. With the increasing resolution of more detailed features can be identified and investigated in the future.