Spatial distribution of tuna (Thunnus.sp) in Indian Ocean south of Java has been investigated. Tuna was scientifically known as thermo-conformer species, thus their distribution were strongly influenced by sub-surface temperature. Tuna species in this study comprise of bigeye tuna (Thunnus obesus), albacore tuna (Thunnus alalunga), yellowfin tuna (Thunnus albacares) and southern bluefin tuna (Thunnus maccoyii). The study was conducted in the area between 100 o E -127 o E and 7 o S -20 o S during 2013 covering of southeast monsoon (April -September) and northwest monsoon (October -March) data. About 1200 coordinate of ARGO Float data and actual catch of tuna from fishing fleet in the same day were processed to obtain the polynomial equation and correlation coefficient. ARGO Float data were processed using kriging method. Correlation coefficient method that used in the study was Empirical Cumulative Distribution Function (ECDF), while spatial distribution equation was developed by polynomial regression equation. Sub-surface temperature in Indian Ocean south of Java fluctuates seasonally. Temporal distribution of dataset indicates that sub-surface temperature was warmer in northwest monsoon than in southeast monsoon. Seasonal fluctuation of sub-surface temperature may vary due to occurrence of upwelling. T. alalunga, T. Albacares and T. Obesus were found to be more favour in the depth around 150m with optimum temperature between 16 o C -21 o C, while T. maccoyii were found in the dept around 250m with optimum temperature between 13 o C -16 o C. Potential fishing zone for Thunnus.sp in southeast monsoon was wider than in northwest monsoon. This condition was according to seasonal variability of sub-surface temperature.

Metode Interpolasi pada umumnya digunakan untuk menghasilkan data permukaan dan mengisi data. Metode interpolasi yang banyak digunakan dalam pengolahan Suhu Permukaan Laut (SPL) adalah Inverse Distance Weighted (IDW), Kriging, Natural Neighbor Interpolation (NNI). Dan Spline. Pada penelitian ini, empat metode interpolasi yang umum digunakan pada pengolahan SPL ditinjau dan dibandingkan untuk menemukan metode interpolasi yang baik untuk pengolahan SPL. Data yang digunakan adalah data Argo Float yang berupa titik SPL, dan data citra Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) sebagai data pedoman atau pembanding. Metode penilaian yang digunakan adalah tampilan hasil dari interpolasi citra, perbandingan nilai maksimum dan minimum, perbandingan rerata, perbandingan RMSE (Root Mean Square) dan perbandingan Standard Deviation Difference. Hasil dari perbandingan tersebut menunjukkan bahwa metode interpolasi IDW merupakan metode yang cocok untuk melakukan interpolasi data SPL yang dihasilkan oleh Argofloat.

Variance errors of Himawari-8, buoy, and Multi-scale Ultra-high Resolution (MUR) SST in Savu Sea have been investigated. This research used level 3 Himawari-8 hourly SST, in situ measurement of buoy, and daily MUR SST in the period of December 2016 to July 2017. The data were separated into day time data and night time. Skin temperature of Himawari-8 and subskin tempertaure of MUR SST were corrected with the value of 15∆Tdept">  before compared with buoy data. Hourly SST of Himawari-8 and buoy data were converted to daily format by averaging process before collocated with MUR SST data. The number of 2,264 matchup data are obtained. Differences average between Himawari-8, buoy and MUR SST were calculated to get the value of variance (Vij).  Using three-way error analysis, variance errors of each observation type can be known. From the analysis results can be seen that the variance error of Himawari-8, buoy and MUR SST are 2.5 oC, 0.28oC and 1.21oC respectively. The accuracy of buoy data was better than the other. With a small variance errors, thus buoy data can be used as a reference data for validation of SST from different observation type.

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ABSTRAKVariabilitas upwelling di perairan selatan Jawa telah diidentifikasi. Analisis multilayer dilakukan dengan menggunakan data ARGO Float periode 2016 hingga 2017. Variabilitas suhu permukaan laut (SPL) dan klorofil-a (klor-a) dianalisis dengan menggunakan data satelit MODIS Aqua periode 2007 hingga 2017. Pengaruh El Nino Southern Oscillation (ENSO) terhadap upwelling dilakukan dengan menggunakan indeks Oceanic Nino Index (ONI), sedangkan pengaruh Indian Ocean Dipole (IOD) direpresentasikan dengan menggunakan indeks Dipole Mode Index (DMI). Dari hasil penelitian diketahui bahwa ENSO mempengaruhi intensitas upwelling. Pada periode el nino intensitasnya mengalami peningkatan yang diikuti oleh penurunan SPL dan naiknya konsentrasi klor-a, sebaliknya pada periode la nina terjadi penurunan intensitas yang diikuti naiknya SPL dan turunnya konsentrasi klor-a. Peningkatan intensitas juga terdeteksi pada saat terjadi periode IOD positif, sedangkan penurunan intensitas terjadi pada periode IOD negatif. ABSTRACT Upwelling variability in the Indian Ocean South of Java has been identified. Multilayer analysis was performed using ARGO Float data during 2016 -2017. Variability of sea surface temperature (SST) and chlorophyll-a concentration (chlor-a) were analyzed using Aqua MODIS satellite data during 2007 -2017. The influence of El Nino Southern Oscillation (ENSO) on upwelling intensity was conducted using the Oceanic Nino Index (ONI), while the influence of Indian Ocean Dipole (IOD) was represented by using Dipole Mode Index (DMI).Result of the study show that ENSO give high impact on upwelling variability. During el nino period, the intensity has increased, followed by a decrease in SPL and increased in chlor-a concentration, on the other hand during la nina period there is a decrease of intensity followed by an increase in SPL and a decrease in chlor-a concentration. Increased intensity was also detected during a positive IOD period, while decreased intensity was occurred during a negative IOD period.

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

Penelitian ini ditujukan untuk mengetahui karakteristik oseanografis teluk Senggrong.  Data yang digunakan meliputi suhu permukaan laut, konsentrasi klorofil-a,  salinitas dan pH baik musim barat maupun musim timur.  Data time series suhu permukaan laut dan klorofil-a menggunakan data dari satelit Aqua dan Terra dengan sensor Moderate Resolution Imaging Spectroradiometer (MODIS) tahun 2007 hingga 2018. Data insitu teluk Senggrong diperoleh dari pengukuran langsung yang dilakukan pada bulan April  dan bulan September  dengan menggunakan water quality checker (WQC). Untuk menampilkan distribusi spasial masing-masing variabel dilakukan interpolasi Krigging. Hasil penelitian menunjukan bahwa karakteristik oseanografis teluk Senggrong dipengaruhi oleh perubahan musim baik musim barat maupun musim timur.  Suhu permukaan laut pada musim barat relatif lebih tinggi dibandingkan pada musim timur. Konsentrasi klorofil-a pada musim barat lebih rendah daripada musim timur. Salinitas pada musim barat lebih rendah dibandingkan pada musim timur, sedangkan pH pada musim barat lebih tinggi daripada musim timur. Pada musim barat teluk Senggrong memiliki suhu permukaan laut antara 28,1 ℃ – 31,6 ℃, konsentrasi klorofil-a sekitar 0,2 mg/m3– 0,5 mg/m3, salinitas sebesar 32 ppm – 33 ppm dan pH berkisar 8,4 – 8,7. Sedangkan pada musim timur suhu permukaan laut berkisar antara 24,9 ℃ – 30,7℃, konsentrasi klorofil-a sebesar 0,1 mg/m3 – 3,5 mg/m3, salinitas antara 34 ppm – 35 ppm dan pH sekitar 7,5 – 8,3.

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