Sea Surface Temperature Mapping at Medium Scale Using Landsat 8 -TIRS Satellite Image (original) (raw)
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ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 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 (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.
COMPARISON OF SEA SURFACE TEMPERATURE RETRIEVAL METHODS ON LANDSAT 8 TIR BANDS
ICOIRS 1st, 2015
Sea surface temperature (SST) has been studied intensively and concurrently with the development of remote sensing imageries, especially the ones that own broader bands including Thermal Infra-red (TIR). The recent Landsat 8 which provides two TIR bands, Band 10 and 11, is indicated unstable if they are used individually. Therefore, some algorithms exist to accommodate those two bands to provide better accuracy of SST. On the other hand, USGS also provides indexes and algorithms to allow user proceed a single TIR band to produce SST. This paper, thus, try to compare the SST calculated from those algorithms to the real time field measurement at two different environments. This study employs two algorithms provided by USGS for both Landsat 8 TIRS bands, and Split Window Algorithm (SWA) method. The field measurement was conducted in Lombok strait, covering both sea-shore and estuary areas. The result shows significant different between the SST calculated using single TIR band and both TIRS bands algorithm, ranging between 0.10-0.80oC. The accuracy level of SST measured from SWA is higher compared to the one did from single band algorithm, when they are examined to the field measurement result. Key words: Sea surface temperature, Landsat 8, thermal infra-red, split window algorithm
International Journal of Remote Sensing and Earth Sciences (IJReSES)
The new Landsat generation, Landsat-8, is equipped with two bands of thermal infrared sensors (TIRS). The presence of two bands provides for improved determination of sea surface temperature (SST) compared to existing products. Due to its high spatial resolution, it is suitable for coastal zone monitoring. However, there are still significant challenges in converting radiance measurements to SST, resulting from the limitations of in-situ measurements. Several studies into developing SST algorithms in Indonesia waters have provided good performance. Unfortunately, however, they have used a single-band windows approach, and a split-windows approach has yet to be reported. In this study, we investigate both single-band and split-window algorithms for retrieving SST maps in the coastal zone of Wangi-Wangi Island, Wakatobi, Southeast Sulawesi, Indonesia. Landsat-8 imagery was acquired on February 26, 2016 (01: 51: 44.14UTC) at position path 111 and and row 64. On the same day, in-situ SS...
Development of sea surface temperature retrieval algorithm for INSAT-3D
2006
Radiative transfer simulation based study was carried for developing sea surface temperature algorithms for ISRO's next geostationary satellite INSAT-3D that will be similar to GOES-9 configuration. Characterization of Indian tropical marine atmosphere was done by utilising the surface and atmospheric parameters like temperature, pressure and humidity observed onboard research vessels, covering entire Indian oceans. These parameters were further perturbed in order to achieve the full temporal and spatial variability in the Indian region. 1392 such atmospheric profiles were generated as input to the radiative transfer model. Brightness temperatures for INSAT-3D imager and sounder channels were simulated for these profiles. Various combinations of the channels suitable for sea surface temperature and total water vapor estimation were considered and depending on the statistical parameters and retrieval errors, daytime and nighttime SST retrieval equations were finalised. These equations were applied to GOES-9 data over eastern pacific and the retrieved SST fields were validated with insitu ship observations. The rms error achieved was ~ 0.68 K. Finally SST retrieval equations were suggested for INSAT-3D. The advantage of frequent sampling by geostationary satellites was also demonstrated by studying the diurnal variability of SST and improving the cloud free SST fields using INSAT-3A data. It was found that cloud free fields can be increased to ~ 25% in a day by compositing eight images for that day.
Geographia Technica, 2021
Climate change has worsened and has widespread impact. This is partly due to the release of greenhouse gases from human activities, which cause the greenhouse effect. This leads to the global temperature rising to an unusual level, otherwise known as global warming. This study aimed to use a split-window algorithm to retrieve the land surface temperature via Landsat-8 OLI/ TIRS data in the Roi Et province area. The research methodology included 1) separating the Landsat-8 OLI data into four types of land use, i.e. the agricultural, forest, urban and water areas and 2) the data for Landsat-8 OLI bands 4 and 5 and Landsat-8 TIRS (bands 10, 11) being analysed to retrieve the land surface temperature using a split-window algorithm. The results from the land-use separation showed that the total area of Roi Et was 8,299.46 km 2 divided into a 4,787 km 2 agricultural area, which accounted for 60.81%; a 1,555 km 2 forest area, accounting for 19.75%; a 1,212 km 2 urban area, accounting for 15.39% and a 317.44 km 2 water area, accounting for 4.03%. The land surface temperature analysis result using a split-window algorithm indicated that the average temperature of Roi Et was 34.74°C. Moreover, it was found that the land surface temperature of the urban area had the highest mean land surface temperature, followed by the forest area, the agricultural area and the water source area, respectively.
Surface Temperature Distribution Analysis Using Remote Sensing System in Spermonde Estuary
2018
Research on the phenomenon of climate change on the region Makassar Strait waters that are part of the oceanographic activities that require observation of the sea surface temperature (SST), surface currents and ocean color imagery from satellite imagery. In this case, satellite remote sensing data using Aqua-Modis analyze both visually and raw-surface temperature parameter data in order to study a variety of other related phenomena that take place in the ocean. The existence of satellite imagery data for the observation of parameters and / or oceanographic phenomena will be more profitable in terms of time and cost and high accuracy. The purpose of this study was to review based on the description of the SST analysis using Aqua-Modis image data (non-commercial) and open source software in providing the information cepatdan applicable. Bodies Spermonde been considering the seas is quite unique because it is located right in the path of the current meeting of the Pacific Ocean throug...
Journal of Remote Sensing & GIS, 2017
Temperature is one of the principal controls on all physical, chemical and biological processes in the environment. Therefore, temperature data play an important role in earth resources management activities, including managing the effects of climate change. In this study, the sea surface temperatures (SSTs) of Weda Bay, Halmahera Island, Indonesia were mapped and monitored, from January to November 2007, using thermal infrared (TIR) band 30 and 31 of Terra-and Aqua MODIS satellites. The empirical prediction SST model developed, using TIR band and in-situ measurement SST, showed that the model was sufficient to predict and to map the SST within the bias ranges of ± 0.5°C. Daily SST, averaged 10 days SST, and monthly SST maps were made using 109 available Terra-and Aqua-MODIS images. The ranges of daily and 10-day average SSTs in Weda Bay were narrow, about 2°C (28-30°C) throughout the year, while the range for monthly SSTs was only 1°C (28.75-29.75°C). Accordingly, no indication of upwelling phenomena occurred in this bay during the observation (2007), but it is possible that upwelling could have happened in the past or may in the future. Long-term monitoring from space should continue in order to get a clearer understanding of the water characteristics in Weda Bay, not only using TIR, but also using ocean color bands of MODIS.
Remote Sensing, 2015
The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for OPEN ACCESS Remote Sens. 2015, 7 4269 the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.
An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset
South African Journal of Geomatics
Soil moisture, surface temperature, and vegetation are variables that play an important role in our environment which in turn increases the demand for accurate estimation of certain geophysical parameters such as weather, flooding, and land classification. However, for accurate Land Surface Temperature (LST) estimation, remotely sensed data of key environmental forms were considered and applied in this research. The goal of this study was to apply a suitable algorithm for LST estimation from the Landsat-8 dataset that gives a great accuracy when compared with in-situ observations. Spatial and temporal Landsat-8 data were acquired which provided the analytical structure for linking specific data successfully due to fine resolutions. The data were then applied to determine brightness temperatures, vegetation cover, and surface emissivity which demonstrated the effectiveness of the Split-Window Algorithm as an optimum method for LST retrieval from satellite. The results show temperature variation over a long period of time can be used in observing varying temperature values based on terrain i.e. High temperatures in fully built up areas and low temperatures in the well-vegetated regions. Finally, accurate LST estimation is important for land classification, energy budget estimations as well as agricultural production.
West Banda Sea is area that high potential resources of marine and fisheries in Indonesia. Oceanographic observation factor such as Sea Surface Temperature (SST) will improve knowledge of some oceanographic phenomena (e.g. thermal fronts) which is useful for fishing ground forecast. The aim of this study was to observe SST by using satellites image in West Banda Sea. There were the Aqua-MODIS datasets composite 8-day level 3, which provide a SST data in 4 km pixel size. Results showed that based on the observation of SST in 2013 that the highest temperature in west season and the lowest SST was founded in east season until east-west season. There are tend to occur upwelling in west of Banda Sea. Based on result of the research, it is necessary to do the next research for verifying and observing the upwelling phenomena in West Banda Sea.