scholarly journals Accuracy assessment for Landsat 8 thermal bands in measuring sea surface temperature over Kuwait and North West Arabian Gulf

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Jasem A Albanai ◽  
◽  
Sara A Abdelfatah ◽  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Arabian Gulf ◽  
Sea Surface ◽  
Landsat 8 ◽  
North West ◽  
Sensing Applications ◽  
Contour Lines ◽  
Thermal Band

Studying physical oceanography is one of the important fields of remote sensing applications. Previously, the thermal mapping of seas and oceans relied on primitive methods, such as the use of sensors installed on buoys, extracting contour lines, and deriving the values from the confluence of contour lines. Today's remote sensing provides more advanced methods for extracting sea surface temperature (SST) values for all bodies of water as a continuous raster model, through thermal sensors installed on satellites designated to monitor and observe the Earth. The Landsat program has facilitated a quantum leap by providing its data free for the public. What has become increasingly important is the inclusion, in Landsat 8, of a thermal band on the TIRS sensor through which SST can be extracted with a spatial resolution of 100 m2. In this article, the accuracy of the two thermal bands (band 10 and 11) of Landsat 8 was validated in estimating the SST of Kuwaiti and Northwest Arabian Gulf waters, through the use of 62 thermal images and 66 ground-truthing points (GTPs) taken from the field in the period from July 2013 to March 2020. This was achieved through a function provided by the ENVI 5.3 software - “brightness temperature” - to derive the surface temperature. The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The accuracy of the thermal band 10 was ± 2.03 degrees (7.9%), while the accuracy of the thermal band 11 was ± 3.13 degrees (13.7%). Therefore, this study demonstrated that the thermal band 10 of Landsat 8 is more accurate than the thermal band 11 in monitoring the SST of Kuwaiti and north-west Arabian Gulf waters, with a difference of ± 1.1 degrees (5.8%).

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

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 (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

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

2017 ◽  
Vol 862 ◽  
pp. 90-95 ◽  
Author(s):  
Agung Budi Cahyono ◽  
Dian Saptarini ◽  
Cherie Bhekti Pribadi ◽  
Haryo Dwito Armono
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Coastal Development ◽  
Sea Surface ◽  
Distribution Area ◽  
Landsat 8 ◽  
Remote Sensing Technology ◽  
Sensing Technology

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.

scholarly journals PENERAPAN TEKNOLOGI PENGIDERAAN JAUH DI BIDANG PESISIR DAN LAUTAN

OSEANA ◽  
2017 ◽  
Vol 42 (3) ◽  
pp. 56-64
Author(s):  
Muhammad Zainuddin Lubis ◽  
Oktavianto Gustin ◽  
Wenang Anurogo ◽  
Husnul Kausarian ◽  
Kasih Anggraini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Marine Habitat ◽  
Sensing Data ◽  
Sensing Applications ◽  
Marine Areas ◽  
Ocean Remote Sensing

APPLICATIONS OF REMOTE SENSING TECHNOLOGY IN COASTAL AND OCEAN Many remote sensing applications are devoted to the coastal and ocean sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of coastal and ocean”. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally sea surface temperature, marine habitat into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy in coastal and ocean. Remote sensing combined with geographic information system (GIS) can be used as a technology tool to obtain information about the object quickly and accurately, including objects in coastal and ocean areas. Remote sensing data on coastal and marine areas specifically for the region in Indonesia have been widely practiced. The use of remote sensing data and GIS in coastal and marine areas can be used to determine sea surface temperature, determination of fish catchment area, and coastline morphological changes by adding other influential parameters. It can also be used to monitor a regional change by using multi-temporal recording data such as disaster monitoring, monitoring of land cover changes in coastal areas etc. Remote sensing data essentially can be used as an alternative technology in obtaining information at a cheaper cost when compared with the conventional way.

scholarly journals Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

2021 ◽  
Vol 12 (5) ◽  
pp. 607-617
Author(s):  
A. Rajani, Dr. S.Varadarajan
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Andhra Pradesh ◽  
Temperature Data ◽  
Sensor Data ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band  contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

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