AbstractTSS and Chl-a are globally known as a key parameter for regular seawater monitoring. Considering the high temporal and spatial variations of water constituent, the remote sensing technique is an efficient and accurate method for extracting water physical parameters. The accuracy of estimated data derived from remote sensing depends on an accurate atmospheric correction algorithm and physical parameter retrieval algorithms. In this research, the accuracy of the atmospherically corrected product of USGS as well as the developed algorithms for estimating TSS and Chl-a concentration using Landsat 8-OLI data were evaluated. The data used in this study was collected from Poteran's waters (9 stations) on April 22, 2015 and Gili Iyang's waters (6 stations) on October 15, 2015. The low correlation between in situ and Landsat Rrs(λ) (R 2 = 0.106) indicated that atmospheric correction algorithm performed by USGS has a limitation. The TSS concentration retrieval algorithm produced an acceptable accuracy both over Poteran's waters (RE of 4.60% and R 2 of 0.628) and over Gili Iyang's waters (RE of 14.82% and R 2 of 0.345). Although the R 2 lower than 0.5, the relative error was more accurate than the minimum requirement of 30%. Whereas, the Chl-a concentration retrieval algorithm produced an acceptable result over Poteran's waters (RE of 13.87% and R 2 of 0.416) but failed over Gili Iyang's waters (RE of 99.14% and R 2 of 0.090). The low correlation between measured and estimated TSS or Chl-a concentrations were caused not only by the performance of developed TSS and Chl-a estimation retrieval algorithms but also the accuracy of atmospherically corrected reflectance of Landsat product. Keywordsremote sensing; water quality; TSS; Chl-a.Abstrak TSS dan Chl-a secara global dikenal sebagai parameter utama dalam pemantauan kualitas air laut. Mengingat tingginya variasi temporal dan spasial dari konstituen perairan, teknik penginderaan jauh adalah metode yang efisien dan akurat untuk mengekstrak parameter fisik air tersebut. Akurasi dari parameter fisik yang diturunkan dari data penginderaan jauh tergantung pada algoritma koreksi atmosfer dan algoritma estimasi parameter fisik yang akurat. Dalam penelitian ini, akurasi dari produk USGS yang terkoreksi secara atmosfer serta algoritma yang dikembangkan untuk menghitung konsentrasi TSS dan Chl-a menggunakan Landsat 8-OLI data telah dikaji. Data yang digunakan dalam penelitian ini dikumpulkan dari Perairan Poteran (9 stasiun) pada tanggal 22 April 2015, dan Perairan Gili Iyang (6 stasiun) pada tanggal 15 Oktober 2015. Korelasi yang rendah antara data in situ dan Landsat Rrs(λ) (R 2 = 0,106) menunjukkan algoritma koreksi atmosfer yang digunakan oleh USGS memiliki keterbatasan. Algoritma estimasi konsentrasi TSS menghasilkan akurasi yang dapat diterima di Perairan Poteran (RE sebesar 4,60% dan R 2 sebesar 0,628) dan di perairan Gili Iyang (RE sebesar 14,82% dan R 2 sebesar 0,345). Meskipun R 2 lebih rendah dari 0,5, kesalahan relatifnya lebih akurat dari persyaratan minimum seb...

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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