Evaluation of Sea Surface Temperature from the Satellite Observations and Model Output using In Situ Measurement dataset: Study area Panjang Island, Banten

Abstract

Abstract Sea surface temperature (SST) is a crucial parameter in climate change studies, and it can be obtained from various sources, including in-situ measurements, satellite observations, reanalysis, and models. However, due to the limited availability of in-situ data, researchers often rely on satellite observations and models, all of which are susceptible to errors. This study aims to improve the accuracy of SST data from satellite observations and models by evaluating and calibration the data through comparison with in-situ SST data using time series analysis. The study areas are Panjang Island, Banten, with a period from April 15, 2022, to February 7, 2023. The data was collected from satellite (OISST V2.1), model (Hycom GOFS V3.0-3.1), and in-situ (loggers HOBO U-24 and TidBit V2.1). The results show that satellite-derived SST values perform better than the model based on higher satellite correlation coefficient values (r = 0.853) compared to the model (r = 0.685). In addition, satellite data also have lower error rate ( x ¯ b i a s = 0.445°C; σ bias = 0.304°C; RMSE = 0.538°C) than model ( x ¯ b i a s = 0.472°C; σ bias = 0.32°C; RMSE = 0.57°C). The variance tests show no significant difference for satellite (p-value = 0.785) and model (p-value = 0.346) data against in-situ data, while the mean tests reveal disparities (p-value < 0.001). The calibration process reduces error values for both satellite ( x ¯ b i a s = 0.276°C; σ bias = 0.225°C; RMSE = 0.356°C) and model data ( x ¯ b i a s = 0.398°C; σ bias = 0.298°C; RMSE = 0.397°C), but the correlation coefficients remain stable at 0.853 (satellite) and 0.685 (model). In conclusion, satellite observations provide a more precise picture of the SST conditions on Panjang Island, Banten and the calibration process can improve the quality of satellite model data.