A model to estimate an element on the earth's surface by remote sensing technique is known as estimation algorithm. Many researches have been conducted to develop estimation algorithm particularly on the elements of the sea surface using Landsat imagery data such as sea surface salinity, sea surface temperature, total suspended solids, chlorophyll-a, etc. This study aimed to develop estimation algorithm of sulfate concentration at the sea surface of Madura Strait waters. Knowing the sulfate concentration at the sea surface was very important for concrete planners to construct a mixture of concrete elements that best matches the existing environmental conditions based on SNI 2847-2013 about the class of sulfate exposure. Besides, it was beneficial for salt farmers as it makes them easier to know the process of precipitation of unnecessary elements in the process of producing salts such as magnesium sulfate (MgSO4). The algorithm was constructed using regression models both linear and nonlinear, including multiple regressions, in which RRS NIR (Band 5) of Landsat 8 OLI as predictor variable and sulfate as the response variable. The finding showed that nonlinear power regression model was the best algorithm to estimate the sulfate concentration at the sea surface than other models with error value (NMAE) 9.53% and residue value (RMSE) 320.84. In the model which was developed, the intercept value was 3055.5 and the slope value was 0.049.

Original languageEnglish
Pages (from-to)5741-5753
Number of pages13
JournalJournal of Theoretical and Applied Information Technology
Issue number17
Publication statusPublished - 15 Sept 2018


  • Data mining
  • Estimation algorithm
  • Landsat 8 OLI
  • Madura Strait
  • Reflectance remote sensing (Rrs)
  • Sulfate


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