Abstract
Abstract. The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (R2) and Root Mean Square Error (RMSE) of 0.912 and 0.028, respectively.
Highlights
The coastal sea surface temperature (SST) is one of the important oceanic environmental factors in determining the change of marine environments and ecological activities (Kang et al, 2014)
The algorithm based on band 11 polynomial-2 with coefficient of -0.0197, 0.2881, and 29.004 had a good performance
The Landsat 8 thermal sensor brightness temperature had a good relationship with the measured sea surface temperature
Summary
The coastal sea surface temperature (SST) is one of the important oceanic environmental factors in determining the change of marine environments and ecological activities (Kang et al, 2014). Ahn et al (2006) applied the algorithm by the NASA to the coastal SST retrieval without consideration of the atmospheric effects. Trisakti et al (2013) developed an algorithm to estimate SST by using the thermal band of Landsat. This model can be applied to obtain SST distribution from data with different seasonal condition in northern and southern water of Java and Bali. This is an alternative method in the case of lack of in-situ data.
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