Estimation of Total Suspended Sediment and Chlorophyll-A Concentration from Landsat 8-Oli: The Effect of Atmospher and Retrieval Algorithm

Lalu Muhamad Jaelani, Resti Limehuwey, Nia Kurniadin, Adjie Pamungkas, Eddy Setyo Koenhardono, Aries Sulisetyono

Abstract


Total Suspended Sediment (TSS) and Chlorophyll-a (Chl-a) are globally knows as a key parameters for regular seawater monitoring. Considering the high temporal and spatial variation of water constituent, remote sensing technique is an efficient and accurate method for extracting water physical parameter. A high accurate estimated data derived from remote sensing depends on an accurate atmospheric correction algorithm and physical parameter retrieval algorithms. In this research, we evaluated the accuracy of atmospheric corrected product of NASA as well as develop algorithms for estimating TSS and Chl-a concentration over Poteran and Gili Iyang island water using Landsat-8 OLI data. The data used in this study was collected from Poteran’s waters (9 stations) on April 22, 2015 and Gili Iyang’s waters (six stations) on October 15, 2015. Low correlation between in situ and Landsat Rrs(λ) (R2= 0.106) indicated that atmospheric correction algorithm performed by NASA has a limitation. The TSS concentration retrieval algorithm produced acceptable accuracy both over Poteran’s Waters (RE of 4.60% and R2 of 0.628) and over Gili Iyang’s waters (RE of 14.82% and R2 of 0.345). Although the R2 lower than 0.5, the relative error was more accurate than the minimum requirement of 30%. Whereas, The Chl-a concentration retrieval algorithm produced acceptable result over Poteran (RE of 13.87% and R2 of 0.416) and failed over Gili Iyang’s waters (RE of 99.140 and R2 of 0.090). The low correlation between TSS or Chl-a measured and estimated TSS or Chl-a concentration were caused not only by performance of the developed TSS and Chl-a estimation retrieval algorithm but also the effect and accuracy of atmospheric corrected reflectance of Landsat product.

Keywords


ocean colour; remote sensing; water quality; landsat; Chl-a; TSS

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DOI: http://dx.doi.org/10.12962/j20882033.v27i1.1217

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