scholarly journals RETRIEVING COASTAL SEA SURFACE TEMPERATURE FROM LANDSAT-8 TIRS FOR WANGI-WANGI ISLAND, WAKATOBI, SOUTHEAST SULAWESI, INDONESIA

2019 ◽  
Vol 16 (1) ◽  
pp. 13
Author(s):  
Eko Susilo ◽  
Rizki Hanintyo ◽  
Adi Wijaya
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coastal Zone ◽  
Single Band ◽  
Sea Surface ◽  
Superior Performance ◽  
Landsat 8 ◽  
Infrared Sensors ◽  
In Situ Data

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 SST was measured by using Portable Multiparameter Water Quality Checker – 24. We used the coefficient of correlation (r) and root mean square error (RMSE) to determine the best algorithm performance by incorporating in-situ data and the estimated SST map. The results showed that there were differences in brightness temperature retrieved from TIRS band10 and band 11. The single-band algorithm based on band 10 for Poteran Island clearly showed superior performance (r = 69.28% and RMSE = 0.7690°C). This study shows that the split-window algorithm has not yet produced a accurate result for the study area.

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.

scholarly journals ANÁLISE DE TEMPERATURA DE PELE DA SUPERFÍCIE DO MAR EM REGIÕES DE ALTA LATITUDE SIMULADA COM O MODELO WRF

2016 ◽  
Vol 38 ◽  
pp. 11
Author(s):  
Alcimoni Nelci Comin ◽  
Otávio Costa Acevedo
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Wrf Model ◽  
Sea Surface ◽  
Average Wind Speed ◽  
Shetland Islands ◽  
In Situ Data ◽  
Average Wind ◽  
Nested Grids

The in situ data of sea surface temperature (SST) were measured onboard the Polar Ship Almirante Maximiano in the southern Shetland Islands between 5 and 23 February 2011. For the simulations, three concentric nested grids have been used at the 9 km, 3 km and 1 km spatial resolution in the simulations of the skin sea surface temperature (SSST) with WRF model. The grids are displaced every day, always centered in the middle position of the ship (latitude/longitude) during transect. The SSST is underestimated in comparison with SST on average 1.5°C. The real average wind speed observed was 8.7 ms-1. Therefore the amount of mixing between SST and SSST is greater, and the temperature difference between the two layers is smaller, on average 0.5°C. The underestimation of the model is mean 1°C. This underestimation directly interfere on the amount of ocean evaporation for the atmosphere, which may cause error in the energy balance. The correlation of the SSST with real SST data was 0.84 and root mean square error 1.87. 

scholarly journals Sea‐surface temperature and salinity product comparison against external in situ data in the A rctic O cean

2015 ◽  
Vol 120 (11) ◽  
pp. 7223-7236 ◽  
Author(s):  
J. N. Stroh ◽  
Gleb Panteleev ◽  
Sergey Kirillov ◽  
Mikhail Makhotin ◽  
Natalia Shakhova
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
In Situ Data ◽  
Product Comparison

scholarly journals Reinitializing Sea Surface Temperature in the Ensemble Intermediate Coupled Model for Improved Forecasts

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 189
Author(s):  
Sittisak Injan ◽  
Angkool Wangwongchai ◽  
Usa Humphries ◽  
Amir Khan ◽  
Abdullahi Yusuf
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Sea Surface ◽  
In Situ Data ◽  
The Pacific ◽  
Intermediate Coupled Model ◽  
Sea Surface Temperature Anomalies

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis.

scholarly journals Long-term sea surface temperature variability in the Aegean Sea

2011 ◽  
Vol 2 (2) ◽  
pp. 125 ◽  
Author(s):  
Nikolaos Skliris ◽  
Sarantis S. Sofianos ◽  
Athanasios Gkanasos ◽  
Panagiotis Axaopoulos ◽  
Anneta Mantziafou ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Aegean Sea ◽  
Sea Surface ◽  
Surface Warming ◽  
In Situ Data ◽  
Nao Index ◽  
Decadal Scale

The inter-annual/decadal scale variability of the Aegean Sea Surface Temperature (SST) is investigated by means of long-term series of satellite-derived and in situ data. Monthly mean declouded SST maps are constructed over the 1985–2008 period, based on a re-analysis of AVHRR Oceans Pathfinder optimally interpolated data over the Aegean Sea. Basin-average SST time series are also constructed using the ICOADS in situ data over 1950–2006. Results indicate a small SST decreasing trend until the early nineties, and then a rapid surface warming consistent with the acceleration of the SST rise observed on the global ocean scale. Decadal-scale SST anomalies were found to be negatively correlated with the winter North Atlantic Oscillation (NAO) index over the last 60 years suggesting that along with global warming effects on the regional scale, a part of the long-term SST variability in the Aegean Sea is driven by large scale atmospheric natural variability patterns. In particular, the acceleration of surface warming in the Aegean Sea began nearly simultaneously with the NAO index abrupt shift in the mid-nineties from strongly positive values to weakly positive/negative values.

scholarly journals An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements

2020 ◽  
Vol 12 (7) ◽  
pp. 1140
Author(s):  
Dimitrios N. Androulakis ◽  
Andrew Clive Banks ◽  
Costas Dounas ◽  
Dionissios P. Margaris
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Close Correlation ◽  
Coastal Ocean ◽  
Sea Surface ◽  
Human Society ◽  
In Situ Data ◽  
Satellite Sst ◽  
Near Shore

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.

An Integrated Global Observing System For Sea Surface Temperature Using Satellites and in Situ Data: Research to Operations

2009 ◽  
Vol 90 (1) ◽  
pp. 31-38 ◽  
Author(s):  
H.-M. Zhang ◽  
R. W. Reynolds ◽  
R. Lumpkin ◽  
R. Molinari ◽  
K. Arzayus ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
In Situ Data
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