Analyzing Chlorophyll-a Concentration and Sea Surface Temperature Changes in the Upper and Central Gulf of Thailand Based on Satellite Imagery Data during 2018-2024

Abstract

Background and Objectives : Changes in the marine environment such as sea surface temperature (SST) can influence the physical, chemical, and biological processes of the sea and directly affect the growth and distribution of marine phytoplankton. This study analyzes the changes in the concentration of chlorophyll-a (Chl-a) and sea surface temperature in the inner and central Gulf of Thailand between 2018 and 2024 and aims to identify any relationship between them. This information can be beneficial in informing how satellite imagery data can be applied to assess the risk of climate change and to plan and manage marine and coastal resources more effectively, both spatially and temporally.

Methodology : The study area was divided into 25 grids to facilitate spatial analysis, covering the upper and central Gulf of Thailand by using ArcGIS Pro 3.3.0. Each grid was 55×55 kilometers. The total area of the 25 grids was 57,949 square kilometers. Chl-a concentration data was obtained by Sentinel-3 Ocean and Land Colour Instrument (OLCI) Level-2 products which had previously performed an atmospheric correction. Prior to downloading, data were filtered to include only images with cloud coverage not exceeding 20 percent. Additionally, SST data were obtained from satellite imagery provided by the Group for High Resolution Sea Surface Temperature (GHRSST). Both Chl-a and SST datasets for the period of 2018-2024 were downloaded on a weekly basis and subsequently processed using Sentinel Applications Platform (SNAP) version 10.0.0. The datasets were aggregated into monthly means prior to analysis. Annual variations in chlorophyll-a concentration and SST were examined using descriptive statistics to assess the degree of seasonal clustering. The Gini coefficient was applied to quantify the extent of intra-annual variability in both chlorophyll-a concentration and SST. Furthermore, the relationship between chlorophyll-a concentration and SST was evaluated using Spearman’s rank correlation analysis.

Main Results : The average monthly concentration of Chl-a fluctuated month-to month throughout the year and tended to increase year over year. During 2018-2020, Chl-a averaged 1.276 mg/m³ (SD = 0.514) per month. After 2020, annual Chl-a concentration increased to 3.419 mg/m³ (SD = 1.616) per month. SST fluctuated less throughout the year than the Chl-a concentration and tended to be more stable year to year. During 2018-2024, there was no change in annual SST variability nor did there appear to be any seasonality.  The annual concentrations of Chl-a were found to have a Gini coefficient between 0.18 and 0.31. This reflects that Chl-a content has a greater annual variability than SST which has much lower Gini coefficients ranging from 0.014 to 0.022. This also demonstrates that the amount of Chl-a was concentrated in some months higher than SST, and is shows the consistency of SST values throughout the year.  The results of the spatial analysis of chlorophyll-a concentrations across all 25 grids revealed that the upper Gulf of Thailand, particularly the coastal areas of Samut Songkhram and Phetchaburi provinces (Grid 1), had a high Chl-a concentration nearly throughout the year because it is a coastal area with shallow water and high ecological abundance. It is also influenced by freshwater flowing from the Mae Klong and Phetchaburi rivers flowing into Bang Taboon Bay which can result in the deposit of a substantial amount of sediment and nutrients. Sediment and nutrients are conducive to phytoplankton growth, especially during April to July when the average Chl-a was more than 12.000 mg/m³. During 2018-2023, most SSTs were in the range of 29.000-30.000 degree Celsius, while in 2024, SST increased significantly to more than 30.000 degree Celsius in the central Gulf of Thailand. The relationship between Chl-a and SST from January 2018 to December 2024, were inversely related at a statistically significant level of 0.05 (Z =-5.130, p < 0.05), exhibiting a low significant relationship at (r_s=-0.076). In this regard, Spearman Rank Correlation was used to analyze the relationship between Chl-a concentration and SST during 2018-2020. It was found that they were inversely related at a statistically significant level of 0.05 (Z = -2.325, P = 0.020); increased SST reduce the density of the surface layer, enhancing thermal stratification. This stable stratification inhibits the vertical mixing and upwelling of nutrient-rich deep water to the sunlit surface layer. Consequently, the resulting nutrient limitation restricts phytoplankton growth and primary productivity exhibiting a low significant relationship at (r_s = -0.051) as SST increase, Chl-a concentration tends to decrease. In 2021-2024, they remained inversely related at (r_s = -0.063), reflecting a decreased relation level that is statistically significant level of 0.05 (Z = -3.129, p = 0.002). Conversely, a decrease in SST weakens the density gradient between the layers. This reduced stratification enhances vertical mixing, allowing nutrient-rich deep water to be transported to the surface. This nutrient enrichment (or nutrient replenishment) subsequently promotes phytoplankton growth leading to a corresponding increase in chlorophyll-a concentrations.

Conclusions : The results showed that during a given 12-month period, the monthly Chl-a average fluctuated throughout the year and tended to increase year-over-year. SST fluctuated less throughout a given year than the Chl-a concentration and tended to be more stable year-over-year during the time period that was subject to analysis. Chl-a concentration and SST had a low inverse relationship.  The study results demonstrate the utility of applying satellite imagery data, particularly to rapidly assess large fishery resource areas and fishing grounds with a potential for restoration.  Application of satellite imagery can assist in assessing fisheries or environmental circumstances, both spatially and temporally, and support planning for sustainable resource use in the future.