The urban air temperature has gradually increased in almost all cities in the world, including Kendari City. This is indicated by the increase in building materials and reduced vegetation biomass in urban areas that have consequences of increasing surface temperatures and forming a micro-climate phenomenon called urban heat island (UHI). The aim of this study is to analyse of UHI intensity in Kendari region for the periods 2001-2014-2019, based on the distribution of land surface temperature (LST), which was analysed through thermal infrared (TIRS) and operational land imager (OLI) sensors onboard Landsat-7 and Landsat-8, each image has an atmospheric correction and brightness temperature. The results show the intensity of UHI during the 2001 to 2014 period increased by 2.44 °C, while in 2019 the intensity decreased by 2.27 °C from the previous period. These fluctuations are closely related to the land cover (LC) changes especially in built-up areas, vegetation, and bare soil as the effects of the urbanization process, and parameters of the normalized difference vegetation index (NDVI).

Atmospheric correction is very important process to determine of land and ocean surface properties measured from satellite data, especially optical remote sensing satellite system, because passive satellite instruments will always be contaminated by the influence of the atmosphere. The result of this processing is the surface reflectance (sr) product, and it is a necessary process when quantitatively monitoring environmental quality parameters from space. The goal of this study is to assessing of the spectral remote sensing reflectance satellite (Rrs (λ) by the image correction for atmospheric effects (iCOR) tools on total suspended solid (TSS) concentration from the MultiSpectral Instrument (MSI) sensor on-board Sentinel-2 and the Operational Land Imager (OLI) sensor on-board Landsat-8. Involvement of 25 in-situ TSS stations in Kendari bay waters is to assess the results of iCOR-S2 and iCOR-L8. An assessment of the sr results reduced to Rrs (λ) on the MSI and OLI data respectively, affected the value of R 2 where the highest value R 2 = 0.665 is shown on red band OLI data. Meanwhile, the assessment of three TSS algorithms models is built on Rrs (λ), all of them showed mean relative error (MRE) < 30% and were considered capable of defining TSS concentrations in the study area.

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