The 2nd Internasional Seminar on Science and Technology (ISST) for Sustainable Infrastructure
Empowering Research and Technology for Sustainable Infrastructure – 2nd August 2016
Geometics Engineering 379
Empirical Algorithm Modeling for
Estimating Total Suspended Solid
Concentration Using In-situ Data and
Atmospheric Corrected Landsat 8, Case
Study: Gili Iyang’s Waters
Postgraduate Program, Department of
Geomatics Engineering, Institut Teknologi
Lalu Muhamad Jaelani
Department of Geomatics Engineering,
Institut Teknologi Sepuluh Nopember
Abstract- TSS is one of frequently used
parameter for monitoring seawater quality.
Traditional measurement of TSS requiring
laborious laboratory work, which is often
expensive and time consuming. In this research,
remote sensing technology used for estimating
TSS concentrations over Gili Iyang’s waters by
a high accuracy algorithm models were
developed. The in situ data of TSS concentration
were taken on October 15, 2015 (6 stations) and
Landsat 8 data that has been atmospheric
corrected using FLAASH (Fast Line-of-sight
Atmospheric Analysis of Spectral Hypercube)
were used. From the analysis, algorithm model
produced determination coefficient (R2) of
0.661. The estimated TSS concentration by using
the developed algorithm model ranged from
12.50 to 16.97 g/m3.
Index Terms - Empirical algorithm, FLAASH,
Landsat 8, TSS
TSS is one of the important indexes for
evaluation of water quality. Traditional
measurement of TSS is spend a lot of time and
exhausting, sampling is often not enough to
measure all water bodies. Fortunately, remote
sensing can offer attentively, repetitive,
consistent, effective of cost and comprehensive
spatial and temporal views . Remote
sensing is the science of studying the information
retrieval about the Earth's surface, land and sea,
from the image obtained from a distance. This
method is usually dependent on the measurement
of electromagnetic energy reflected or emitted by
the object being observed . Physical parameter
retrieval algorithms and the accurate atmospheric
correction algorithm are the two factors which
influence the accuracy of estimated data derived
from remote sensing , . The goal of this
research is to develop accurate algorithm to
estimate TSS concentration using remote sensing
reflectance of Landsat 8 over Gili Iyang’s waters.
The data in this research were collected from
Gili Iyang’s waters, Dungkek, Sumenep, East
Java Province. A small island in the southeast of
Madura. Geographically located in 114.15o to
114.19o E and 6.96o to 7.01o S.
Landsat 8 image data with path/row of 117/065
and acquisition date on October 15, 2015 were
used. By using atmospheric correction FLAASH
model from image processing software, the
Landsat 8 image data were corrected from
atmospherically effect. The equation is as follow:
is the pixel surface reflectance,
an average surface reflectance for the pixel and a
surrounding region, S is the spherical albedo of
the atmosphere, La is the radiance back scattered
by the atmosphere, A and B are coefficients that
depend on atmospheric and geometric condition
but not on the surface.
While in-situ data of seawater samples (6
samples) collected at the same acquisition date of
Landsat 8 image data. Furthermore, laboratory
work was required for analyzing the water
samples to calculate TSS concentration.
Developing empirical algorithms using
regression algorithm from measured-TSS
concentration as well as single-band, two-band
Internasional Seminar on Science and Technology (ISST) for Sustainable Infrastructure
Empowering Research and Technology for Sustainable Infrastructure – 2
Geometics Engineering 380
ratios and combination of bands of remote sensing
reflectance (Rrs()) .
ESULT AND DISCUSSION
The empirical algorithm with the best
determination coefficient (R
= 0.661), two-band
were used as presented on
Equation (2) and Figure 1:
INIER REGRESSION FOR
OMPARISON BETWEEN MEASURED AND
Sta_01 16.15 16.00
Sta_02 16.20 16.00
Sta_03 16.97 18.00
Sta_04 14.16 14.00
Sta_05 12.50 14.00
Sta_06 14.02 12.00
TSS concentration over Gili Iyang’s waters
was estimated using developed empirical
algorithm with remote sensing reflectance as pure
input. Estimated-TSS concentration in ranged
12.50 to 16.97 g/m
. The estimation of TSS and
in-situ TSS were similar (Table 1). It was
indicated that the developed empirical algorithm
applicable over Gili Iyang’s water. Distribution
map of TSS concentration was shown at Figure 4.
ISTRIBUTION MAP OF
The empirical algorithm to estimate TSS
concentration with coefficient determination of
0.661 were developed and implemented in Gili
Iyang’s waters. The estimated-TSS from Landsat
8 image produced a distribution map of TSS in
ranged 12.50 to 16.97 g/m
 Y. Dingtian, P. Delu, Z. Xiaoyu, Z. Xiaofeng, H.
Xianqiang, and L. Shujing, “Retrieval of chlorophyll a
and suspended solid concentrations by hyperspectral
remote sensing in Taihu Lake, China,” Chinese J.
Oceanol. Limnol., vol. 24, no. 4, pp. 428–434, 2006.
 N. Laili, F. Arafah, L. M. Jaelani, L. Subehi, A.
Pamungkas, E. S. Koenhardono, and A. Sulisetyono,
“Development of Water Quality Parameter Retrieval
Algorithms for Estimating Total Suspended Solids and
Chlorophyll-a Concentration Using Landsat-8 Imagery
At Poteran Island Water,” ISPRS Ann. Photogramm.
Remote Sens. Spat. Inf. Sci., vol. II–2/W2, no. October,
pp. 55–62, 2015.
 R. Limehuwey and L. M. Jaelani, “Development
of Algorithm Model for Estimating Cholophyll-a
Concentration Using In-Situ Data and Atmospherically
Corrected Landsat-8 Image by 6SV, Case Study: Gili
Iyang’s Waters,” in Internasional Seminar of Basic
 James B. Campbell, Introduction to Remote
Sensing. New York: The Guilford Press, 1987.
 L. M. Jaelani, B. Matsushita, W. Yang, and T.
Fukushima, “An improved atmospheric correction
algorithm for applying MERIS data to very turbid
inland waters,” Int. J. Appl. Earth Obs. Geoinf., vol. 39,
pp. 128–141, 2015.
 L. M. Jaelani, R. Limehuwey, N. Kurniadin, A.
Pamungkas, E. S. Koenhardono, and A. Sulisetyono,
“Estimation of TSS and Chl - a Concentration from
Landsat 8 - OLI : The Effect of Atmosphere and
Retrieval Algorithm,” IPTEK, J. Technol. Sci., vol. 27,
no. 1, pp. 16–23, 2016.
 R. Aguirre-Gomez, “Detection of Total
Suspended Sediments in the North Sea using AVHRR
and Ship Data,” Int. J. Remote Sens., vol. 21, no. 8, pp.