scholarly journals Evaluation of ocean color and sea surface temperature sensors algorithms using in situ data: a case study of temporal and spatial variability on two northeast Atlantic seamounts

2010 ◽  
Vol 4 (1) ◽  
pp. 043506 ◽  
Author(s):  
Ana Mendonca
Keyword(s):  
Spatial Variability ◽  
Surface Temperature ◽  
Ocean Color ◽  
Temperature Sensors ◽  
Sea Surface ◽  
In Situ Data ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

scholarly journals PEMETAAN WILAYAH PENANGKAPAN IKAN TERI (Stolephorus sp) BERDASARKAN DATA SATELIT OCEAN COLOR DI PERAIRAN KABUPATEN BATANG (Mapping of Anchovy (Stolephorus sp) Fishing Ground based on Ocean Color Satellite Data in the Batang Regency Waters)

2018 ◽  
Vol 13 (2) ◽  
pp. 133
Author(s):  
Rosyid Paundra Gamawan ◽  
Suryanti Suryanti ◽  
Teja Arief Wibawa
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Ocean Color ◽  
Sea Surface ◽  
Fishing Ground ◽  
Zooplankton Abundance ◽  
In Situ Data ◽  
Water Sampler ◽  
Terra Modis

Kelimpahan Ikan Teri banyak ditemukan di perairan laut Kabupaten Batang saat musim timur. Penggerak ekonomi masyarakat pesisirnya pada musim timur tergantung hasil tangkapan Ikan Teri. Kegiatan penangkapan Ikan Teri oleh nelayan kurang efisien dalam hal waktu dan biaya operasional. Penginderaan jauh merupakan teknologi menghasilkan data observasi secara spasial dan time series. Penelitian ini bertujuan untuk mengetahui kelimpahan plankton dominan dan persebaran Ikan Teri di perairan Kabupaten Batang pada musim timur. Penelitian ini dilaksanakan pada bulan Juli - September 2017. Variabel penelitian yang diteliti adalah adalah suhu permukaan laut, klorofil-a, kelimpahan fitoplankton, kelimpahan zooplankton dan hasil tangkapan Ikan Teri. Pengambilan data kelimpahan plankton menggunakan nansen water sampler. Pengambilan data hasil tangkapan Ikan Teri dengan mencatat hasil tangkapan setiap tripnya. Data SPL dan klorofil-a diunduh dari website Ocean Color. Kedua data tersebut berasal dari rerata nilai masing-masing sensor harian (Terra MODIS, Aqua MODIS dan SNPP VIIRS) yang dikomposit dari tiga hari yaitu, data satu hari sebelum waktu penangkapan, saat penangkapan, dan satu hari setelah penangkapan. Semua data variabel diuji outlier, uji normalitas, tranformasi data, dan uji kolinearitas. Data hasil 4 uji tersebut digunakan untuk membuat persamaan pemodelan rantai makanan Ikan Teri, yaitu persamaan kelimpahan fitoplankton, kelimpahan zooplankton dan kelimpahan Ikan Teri. Persamaan tersebut digunakan untuk menduga pesebaran Ikan Teri. Hasil kelas fitoplankton yang paling dominan adalah Bacillariophyceae, lalu zooplankton adalah Copepoda. Persebaran Ikan Teri bulan Juni 2017 menyebar rata dari timur ke barat perairan Kabupaten Batang. Persebaran Juli 2017, lebih cenderung di bagian timur perairan Kabupaten Batang.  Persebaran Agustus 2017, persebaran Ikan Teri yang hampir rata di setiap perairan Kabupaten Batang. Anchovy abundance is commonly found in the marine waters of Batang Regency during the east season. The economy of coastal communities in the east season depends on the catch of Anchovy. Fishing activities by fishermen are less efficient in terms of time and operational costs. Remote sensing is a technology to produce spatial observation data and time series. This study aims to determine the abundance of plankton (phytolankton and zooplankton) and to determine the distribution of Anchovy fishing ground in the eastern seasons of 2017 based on food chains and oceanographic satellite imagery observations. This research was conducted in July-September 2017. The research variables are sea surface temperature, chlorophyll-a, phytoplankton and zooplankton abundance and fish catch. Plankton abundance is taken by nansen water sampler, while the Anchovy catch data is taken from the catch . Data of sea surface temperature and chlorophyll-a are obtained from Ocean Color website. They are average value of each daily sensor (Terra MODIS, Aqua MODIS and SNPP VIIRS) compiled from three days (one day before taking in situ data, the day taking in situ data, and one day after taking in situ data). Variable data was tested outlier, normality test, data transformation, and cholinearity test. Furthermore, the result data of the four tests are used to make some modeling equations of Anchovy food chain, thats are phytoplankton abundance equation, zooplankton abundance and Anchovy abundance. The equation of Anchovy abundance is used to estimate the distribution of anchovies. This research showed that dominant phytoplankton species at Anchovy fishing ground in Batang Regency is Bacillariophyceae, then, the  zooplankton is Copepoda. Distribution of Anchovy at Batang Regency waters in June 2017 is spread evenly from east to west of waters; in July 2017 is wider spread in the eastern part of the waters; in August 2017 is almost equally in each of the waters.

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 Optimal Configuration Method of Sampling Points Based on Variability of Sea Surface Temperature

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Chang Liu ◽  
Yuning Lei ◽  
Feng Gao ◽  
Meizhen Zhao
Keyword(s):  
Spatial Variability ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimal Configuration ◽  
In Situ Observation ◽  
Distribution Method ◽  
Sampling Points ◽  
Compression Sensing

In situ observation is one of the most direct and efficient ways to understand the ocean, but it is usually limited in terms of spatial and temporal coverage. The determination of optimal sampling strategies that effectively utilize available resources to maximize the information content of the collected ocean data is becoming an open problem. The historical sea surface temperature (SST) dataset contains the spatial variability information of SST, and this prior knowledge can be used to optimize the configuration of sampling points. Here, a configuration method of sampling points based on the variability of SST is studied. Firstly, in order to get the spatial variability of SST in the ocean field to be sampled, the historical SST data of the field is analyzed. Then, K-means algorithm is used to cluster the subsampled fields to make the configuration of sampling points more suitable. Finally, to evaluate the sampling performance of the new configuration method of sampling points, the SST field is reconstructed by the method based on compression sensing algorithm. Results show that the proposed optimal configuration method of sampling points significantly outperforms the traditional random sampling points distribution method in terms of reconstruction accuracy. These results provide a new method for configuring sampling points of ocean in situ observation with limited resources.

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 Reconstruction of super-resolution ocean <i>p</i>CO<sub>2</sub> and air–sea fluxes of CO<sub>2</sub> from satellite imagery in the southeastern Atlantic

2015 ◽  
Vol 12 (17) ◽  
pp. 5229-5245 ◽  
Author(s):  
I. Hernández-Carrasco ◽  
J. Sudre ◽  
V. Garçon ◽  
H. Yahia ◽  
C. Garbe ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Partial Pressure ◽  
Sea Surface Temperature ◽  
Ocean Color ◽  
Super Resolution ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Co2 Fluxes ◽  
Partial Pressure Of Co2

Abstract. An accurate quantification of the role of the ocean as source/sink of greenhouse gases (GHGs) requires to access the high-resolution of the GHG air–sea flux at the interface. In this paper we present a novel method to reconstruct maps of surface ocean partial pressure of CO2 ( pCO2) and air–sea CO2 fluxes at super resolution (4 km, i.e., 1/32° at these latitudes) using sea surface temperature (SST) and ocean color (OC) data at this resolution, and CarbonTracker CO2 fluxes data at low resolution (110 km). Inference of super-resolution pCO2 and air–sea CO2 fluxes is performed using novel nonlinear signal processing methodologies that prove efficient in the context of oceanography. The theoretical background comes from the microcanonical multifractal formalism which unlocks the geometrical determination of cascading properties of physical intensive variables. As a consequence, a multi-resolution analysis performed on the signal of the so-called singularity exponents allows for the correct and near optimal cross-scale inference of GHG fluxes, as the inference suits the geometric realization of the cascade. We apply such a methodology to the study offshore of the Benguela area. The inferred representation of oceanic partial pressure of CO2 improves and enhances the description provided by CarbonTracker, capturing the small-scale variability. We examine different combinations of ocean color and sea surface temperature products in order to increase the number of valid points and the quality of the inferred pCO2 field. The methodology is validated using in situ measurements by means of statistical errors. We find that mean absolute and relative errors in the inferred values of pCO2 with respect to in situ measurements are smaller than for CarbonTracker.

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