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Estimation of Sea Surface Temperature (SST) Using Split Window Methods for Monitoring Industrial Activity in Coastal Area

Authors:
Agung B. Cahyono at Institut Teknologi Sepuluh Nopember
Agung B. Cahyono
Dian Saptarini at Institut Teknologi Sepuluh Nopember
Dian Saptarini
Cherie Bhekti Pribadi
Cherie Bhekti Pribadi
Cherie Bhekti Pribadi
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Haryo Dwito Armono at Institut Teknologi Sepuluh Nopember
Haryo Dwito Armono
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Abstract and Figures

The three drivers of environmental change: climate change, population growth and economic growth, result in a range of pressures on our coastal environment. Coastal development for industry and farming are a major pressure on terrestrial and environmental quality. In their process most of industry using sea water as cooling water. When water used as a coolant is returned to the natural environment at a higher temperature, the change in temperature decreases oxygen supply and affects marine ecosystem. This research is presents results from ongoing study on application of Landsat 8 for monitoring the intensity and distribution area of sea surface temperature changed by the heated effluent discharge from the power plant on Paiton coast, Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to determine the intensity and distribution of temperature changes. Estimation of sea surface temperature (SST) using remote sensing technology is applied to provide ease of marine temperature monitoring with a large area coverage. The method used in this research using the Split Window Algorithm (SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature (SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8. Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the brightness temperature value (°C) Band 11. The result of this algorithm shows the good performance with Root Mean Square Error (RMSE) amount 0.406.
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Estimation of Sea Surface Temperature (SST) Using Split Window
Methods for Monitoring Industrial Activity in Coastal Area
AGUNG Budi Cahyono1, a*, DIAN Saptarini2, b, CHERIE Bhekti Pribadi1, c,
HARYO D. Armono3, d
1Geomatics Engineering, Faculty of Civil Engineering and Planning,
Institut Teknologi Sepuluh Nopember, Indonesia
2Biology, Faculty of Mathematics and Science, Institut Teknologi Sepuluh Nopember, Indonesia
3Ocean Engineering, Faculty of Marine Technology,
Institut Teknologi Sepuluh Nopember, Indonesia
aagungbudicahyo@gmail.com, bdianssa@gmail.com, ccherriepribadi@gmail.com,
dharyoda@gmail.com
Keywords: Sea Surface Temperature; Split Windows Algorithm
Abstract. The three drivers of environmental change are climate change, population growth and
economic growth. This Changes result in a range of pressures on our coastal environment. Coastal
development for industry and farming are a major pressure on terrestrial and environmental quality.
In their process, most of industry using sea water as cooling water. When water used as a coolant is
returned to the natural environment at a higher temperature, the change in temperature decreases
oxygen supply and affects marine ecosystem. This research is presents results from ongoing study
on application of Landsat 8 for monitoring the intensity and distribution area of sea surface
temperature changed by the heated effluent discharge from the power plant on Paiton coast,
Probolinggo, East Java province. Remote sensing technology using a thermal band in Operational
Land Imager (OLI) sensor of Landsat 8 sattelite imagery (band 10 and band 11) are used to
determine the intensity and distribution of temperature changes. Estimation of sea surface
temperature (SST) using remote sensing technology is applied to provide ease of marine
temperature monitoring with a large area coverage. This research use the Split Window Algorithm
(SWA) methods which is an algorithm with ability to perform extraction of sea surface temperature
(SST) with brigthness temperature (BT) value calculation on the band 10 and band 11 of Landsat 8.
Formula which was used in this area is Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (Ts is the
surface temperature value (°C), BT10 is the brightness temperature value (°C) Band 10, BT11 is the
brightness temperature value (°C) Band 11. The result of this algorithm shows the good
performance with Root Mean Square Error (RMSE) amount 0.406.
Introduction
Coastal development for industry and farming are a major pressure on terrestrial and
environmental quality. In their process, most of industry use sea water as cooling water. When
water used as a coolant is returned to the natural environment at a higher temperature, the change in
temperature decreases oxygen supply and affects marine ecosystem.
The coastal sea surface temperature (SST) is one of the important oceanic environmental factors
in determining the change of marine environments and ecological activities [2]. Data with spatial
resolution finer than 1 km have been used to interpret circulation and front movement [3].
Water heat (‘air bahang’) generated by the activities of the electricity industry in the region
Paiton, Situbondo Regency – East Java, Indonesia potentially affecting the availability of marine
organisms in coastal waters Paiton. The Paiton power generation is operated by PT. Jawa Power
owns a 1,220 MW coal fired power station. Jawa Power is one of the largest IPPs in Indonesia with
a 30-year power purchase agreement with PT. PLN (Persero), the state-owned electric utility
company. The power station supplies electricity into the Java-Bali 500 kV grid, which is owned and
operated by PLN.
Applied Mechanics and Materials Submitted: 2016-06-24
ISSN: 1662-7482, Vol. 862, pp 90-95 Revised: 2016-08-10
doi:10.4028/www.scientific.net/AMM.862.90 Accepted: 2016-11-01
© 2017 Trans Tech Publications, Switzerland Online: 2017-01-18
All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans
Tech Publications, www.ttp.net. (#73420991, Iowa State University, Ames, USA-31/01/17,00:04:33)
The spatial distribution of thermal power plant exhaust heat water studied by processing the
image data of Landsat 8 thermal band. So that, we can know the direction and broad distribution
and the impact of water heat from the electricity industry activities in Paiton.
Methods
To develop a new split window algorithm for sea surface temperature (SST), we collected in situ
and Landsat 8 data from Paiton power plants area, Probolionggo district, East Java with
geographical location being on 7°43'30" south latitude and 113°32'32" east longitude, on March 06,
2015.
Figure 1. Paiton Power Plants Area (RGB 432)
The in situ data were measured and collected at 10 stations (were measured using termometer in a
depth of 15 cm) as shown in Fig. 2 and Table 1.
Figure 2. Field Measurements Location at Paiton Power Plant
Table 1. Field Measurements Data
Time Series (WIB)
Station
Depth in 15 cm
10:10
9-1
33
10:17
9-2
32
10:30
9-3
32
10:24
9-4
30,5
10:54
9-5
30
10:40
9-6
31
10:49
9-7
29
11:02
9-8
29
11:36
9-9
31
11:13
9-10
28
The estimated data was obtained by the Landsat 8 Thermal Infrared Sensor (TIRS). Landsat 8
provides metadata of the bands such as thermal constant, rescaling factor value, etc. shown at table
1, 2, and 3.
Applied Mechanics and Materials Vol. 862 91
Table 2. K1 and K2 value
Thermal Constant
Band 10
Band 11
Path/Row
Acquisition Date
K1
774.89
480.89
118/65
March, 28 2015
K2
1321.08
1201.14
Table 3. Rescaling Factor
Band 10
Band 11
Path/Row
Acquisition Date
3.3420E-04
3.3420E-04
118/65
March, 28 2015
0.10000
0.10000
First, pre-proccessing sattelite imagery step is a process to improve the visual quality of the
image, in terms of improving the pixel values that do not correspond to the value of emission
spectral reflectance or the actual object. Digital Number (DN) to the value of the spectral radiant on
satellite images Landsat-8 using dedicated calibration parameters in satellite imagery processing
Level 1 :
Lλ = ML*Qcal + AL (1)
Where,
Lλ = Spectral Radian Value (W/(m2 * sr * μm))
ML = Radiance multiplicative scaling factor for the band (RADIANCE_MULT_BAND_n from the
metadata),
AL = Radiance additive scaling factor for the band (RADIANCE_ADD_BAND_n from the
metadata),
Qcal = Level 1 pixel value in D
Since, spectral radian value of Landsat 8 data has to be converted to the effective temperature value
(°C) as the brightness temperature value (Bt). In the process of conversion of the value of the
spectral radiant effective temperature value (°K), the equation is used as follows :
T =

 (2)
Where,
T = Brightness Temperature (°Kelvin)
L = Spectral Radian Value (Watts/(m2 * sr * μm))
K2 = Temperature Constant (°Kelvin)
K1 = Temperature Constant (°Kelvin)
The process of conversion of the effective temperature value (°K) to effective temperature value
(°C) using the following equation :
T (°C) = T (°K) -273 (3)
Where,
T (°C) = Brightness Temperature Value (°C)
T (°K) = Brightness Temperature Value (°K)
The measured data was obtained by using the termometer on Friday, March 06, 2015. For this
study, we just used 10 points in developing and validating the algoritm, Table 4 shows the statistical
attribute of those point.
Table 4. Statistical Information of in situ data
Thermal Constant
For Development
For Validation
Total Points
10
10
Mean
30.55
32.2
Maximum
33
33
Minimum
28
27
92 Ocean Science and Coastal Engineering
Split window methods has been develop to retrieve cloud-free, sea and land surface temperature
(SST and LST) automatically from sattelite derived radiances. In this study, we use split window
algorithm for paiton area, the equation is used as follows :
Ts = BT10 + (2.946*(BT10 - BT11)) - 0.038 (4)
Where,
Ts = Surface Temperature (°C)
BT10 = Brightness Temperature Value (°C) Band 10
BT11 = Brightness Temperature Value (°C) Band 11
To assess the accuarcy of Landsat 8 thermal sensor brightness temperature, we calculate
coefficient of determination (R2) and Root Mean Square Errorr (RMSE). the equation is used as
follows :

 (5)
Where,
x= The estimated sea surface temperature
y = The measured sea surface temperature
n = Total point
  
 (6)
Where,
 = The estimated value of sea surface temperature using the algorithm
 = The measured value of sea surface temperature using termometer
N = Total point
Result And Discussion
To assess the accurate of algorithm by validating it using 10 data points had a value of RMSE
amount 0.406, it shows a good performance which below 1.000.
Table 5. Value of Sea Surface Temperature Using Split Window Algorithm and Single Channel
Algorirthm
Station
In Situ Sea Surface
Temperature (SST)
Estimated Sea Surface Temperature
(SST) using Split Window
Algorithm (SWA)
Estimated Sea Surface
Temperature (SST) using
Single Channel Algorithm
(SCA)
9-1
33
27
29.8
9-2
32
33
29.6
9-3
32
33
29.9
9-4
30,5
33
29.8
9-5
30
33
29.8
9-6
31
33
29.7
9-7
29
33
29.7
9-8
29
32
29.8
9-9
31
32
29.6
9-10
28
33
29.9
Average
Temperature (°C)
30,55
32,2
29.76
Maximum
Temperature (°C)
33
33
29.9
Minimum
Temperature (°C)
28
27
29.6
Relationship between Landsat 8 thermal sensor brightness temperature and sea surface
temperature using termometer have a value of R2 amount 0.2634 that indicate a relationship but still
weak because it was below 0.500 however there is still potential to be developed as alternative
method to obtain sea surface temperature value, and it has a value of R amount 0.5132. Figure 3
shows the graphic of relationship between both of them.
Applied Mechanics and Materials Vol. 862 93
Figure 3. Graphic of Relationship Between Landsat 8 Thermal Sensor Brightness Temperature And
Sea Surface Temperature Using Termometer
Table 5 shows value of sea surface temperature by in situ, split window algorithm, and single
channel algorithm. In average temperature (°C), value of estimated sea surface temperature (SST)
using SWA has a higher value than measured sea surface temperature (SST) and estimated sea
surface temperature (SST) using SCA.
Figure 4. Sea Surface Temperature by using split window algorithm
Figure at the appendix 1 tell us about value of sea asurface temperature by using split window
algorithm that minimum temperature amount 28°C - 29°C, and maximumn temperature more than
32°C .
y = -0,6119x + 50,894
R² = 0,2634
25
26
27
28
29
30
31
32
33
34
35
26 28 30 32 34
Estimate Sea Surface Temperature Using SWA (Celcius)
In Situ Sea Surface Temperature (Celcius)
Relationship Between In Situ and
Estimate SST Using SWA
In Situ SST and
SWA
94 Ocean Science and Coastal Engineering
Figure 5. Sea Surface Temperature by using single channel algorithm
Figure at the appendix 2 tell us about value of sea asurface temperature by using single channel
algorithm that minimum temperature less than 25°C, and maximumn temperature more than 32°C.
Conclusions
The result for this research indicate that there is relationship between Landsat 8 thermal sensor
brightness temperature and sea surface temperature at the 10 station which was below 0.500 with
the value of R2 amount 0.2634.
References
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Temperature Estimation from DAIS Data, The Digital Airborne Spectrometer Experiment
(DAISEX), Proceedings of the Workshop held July, 2001.
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[3] M.A. Syariza, L.M. Jaelani, L. Subehi, A. Pamungkas, E.S. Koenhardono, A. Sulisetyono,
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Applied Mechanics and Materials Vol. 862 95
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Retrieval of sea surface temperature over poteran island water of indonesia with Landsat 8 tirs image: A preliminary algorithm
Conference Paper
Full-text available
  • Oct 2015
The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (R2) and Root Mean Square Error (RMSE) of 0.912 and 0.028, respectively.
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A Practical Split-Window Algorithm for Retrieving Land Surface Temperature from Landsat-8 Data and a Case Study of an Urban Area in China
Article
Full-text available
  • Apr 2015
This paper proposes a practical split-window algorithm (SWA) for retrieving land surface temperature (LST) from Landsat-8 Thermal Infrared Sensor (TIRS) data. This SWA has a universal applicability and a set of parameters that can be applied when retrieving LSTs year-round. The atmospheric transmittance and the land surface emissivity (LSE), the essential SWA input parameters, of the Landsat-8 TIRS data are determined in this paper. We also analysed the error sensitivity of these SWA input parameters. The accuracy evaluation of the proposed SWA in this paper was conducted using the software MODTRAN 4.0. The root mean square error (RMSE) of the simulated LST using the mid-latitude summer atmospheric profile is 0.51 K, improving on the result of 0.93 K from Rozenstein (2014). Among the 90 simulated data points, the maximum absolute error is 0.99 °C, and the minimum absolute error is 0.02 o°C. Under the Tropical model and 1976 US standard atmospheric conditions, the RMSE of the LST errors are 0.70 K and 0.63 K, respectively. The accuracy results indicate that the SWA provides an LST retrieval method that features not only high accuracy but also a certain universality. Additionally, the SWA was applied to retrieve the LST of an urban area using two Landsat-8 images. The SWA presented in this paper should promote the application of Landsat-8 data in the study of environmental evolution.
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A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data
Article
Full-text available
  • Jan 2015
This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.
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Split-Window Methods for Surface Temperature Estimation from DAIS Data, The Digital Airborne Spectrometer Experiment (DAISEX)
  • Jul 2001
  • J Sobrino
  • J C Jiménez
  • S Laporta
  • F Nerry
Sobrino, J., Jiménez, J. C., Laporta, S., & Nerry, F. 2001. Split-Window Methods for Surface Temperature Estimation from DAIS Data, The Digital Airborne Spectrometer Experiment (DAISEX), Proceedings of the Workshop held July, 2001.
Retrieval Of Sea Surface Temperature Over Poteran Island Water Of Indonesia With Landsat 8 Tirs Image: A Preliminary Algorithm, The International Archives of the Photogrammetry
  • Jan 2015
  • M A Syariza
  • L M Jaelani
  • L Subehi
  • A Pamungkas
  • E S Koenhardono
  • A Sulisetyono
M.A. Syariza, L.M. Jaelani, L. Subehi, A. Pamungkas, E.S. Koenhardono, A. Sulisetyono, 2015. Retrieval Of Sea Surface Temperature Over Poteran Island Water Of Indonesia With Landsat 8 Tirs Image: A Preliminary Algorithm, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W4, 2015
Data and a Case Study of an Urban Area in China
  • 4371-4390
Landsat-8 Data and a Case Study of an Urban Area in China. Open access Journal of Remote Sensing, Vol 7 Issue 4, p.4371 -4390 ; doi : 10.3390/rs70404371 http://www.mdpi.com/ 2072-4292 /7/4/4371
Split-window Algorithm For Estimating Land Surface Temperature From Landsat 8 Tirs Data, Geoscience and Remote Sensing Symposium (IGARSS)
  • Jan 2014
  • D Chen
  • R Huazhong
  • Q Qiming
  • M Jinjie
  • L Jing
Chen D, Huazhong R, Qiming Q, Jinjie M, Jing L. 2014. Split-window Algorithm For Estimating Land Surface Temperature From Landsat 8 Tirs Data, Geoscience and Remote Sensing Symposium (IGARSS). IEEE International.