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.
<h4>Peran dan manfaat ekosistem lamun secara ekologis merupakan hal yang harus diamati secara terperinci, terlebih potensi kerusakan yang dapat terjadi. Pemantauan kondisi ekosistem tersebut dapat dilakukan dengan memanfaatkan teknologi penginderaan jauh. Dalam penelitian ini, algoritma Lyzenga digunakan untuk mengolah nilai piksel yang ada pada citra satelit Landsat-8 dan Sentinel-2. Aplikasi algoritma Lyzenga akan menghasilkan nilai yang menunjukkan identitas dari objek yang terdapat di perairan pesisir. Hasil tersebut kemudian diproses melalui pengkelasan terbimbing dengan dilakukannya pemotongan kerapatan dalam rentangan Lyzenga, yang kemudian dihitung akurasinya. Hasil perhitungan akurasi menunjukkan angka 60,81% untuk citra Landsat-8 terkoreksi <em>T</em><em>op </em><em>o</em><em>f </em><em>A</em><em>tmosphere</em> (<em>ToA</em>) dengan titik acuan berjarak 30 meter, dan 60,18% untuk citra Sentinel-2 terkoreksi ToA dengan titik acuan berjarak 10 meter. Kedua nilai akurasi tersebut menunjukkan bahwa kedua jenis citra satelit mampu digunakan sebagai data untuk pemantauan objek pesisir, namun dengan kriteria acuan yang berbeda.</h4>
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.
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