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All Journal Applied Engineering: Jurnal Rekayasa dan Sains Terapan JOMAFISH
Jihan Nur Fadhilah
Universitas PGRI Ronggolawe

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Google Earth Engine for Sea Surface Temperature Mapping in Marine Environments: A Review Jihan Nur Fadhilah; Luhur Moekti Prayogo
JOMAFISH: Journal of Marine and Fisheries Science Vol. 1 No. 1 (2026): November
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/jomafish.v1i1.27

Abstract

Sea surface temperature (SST) is a fundamental oceanographic variable for describing air–sea exchange, ocean circulation, fisheries habitat, coral thermal stress, coastal processes, and climate variability. The rapid expansion of satellite archives has improved SST observation, but conventional desktop workflows remain constrained by data volume, preprocessing requirements, and reproducibility. This review evaluates the role of Google Earth Engine (GEE) as a cloud-computing environment for SST mapping in marine and coastal research. A structured narrative review was conducted using peer-reviewed literature and authoritative dataset documentation published mainly between 2017 and 2026, with emphasis on studies that employed GEE, satellite-derived SST products, validation procedures, or marine applications. The synthesis shows that GEE substantially reduces data-handling barriers and supports scalable time-series analysis through readily accessible products such as MODIS Aqua/Terra Ocean Color L3, NOAA OISST v2.1, NOAA WHOI SST, and JAXA GCOM-C/SGLI SST. However, dataset choice remains application-dependent. Coarse, gap-filled products are effective for climate-scale trends and anomalies, whereas thermal infrared sensors with kilometre- to hectometre-scale observations are more appropriate for coastal gradients but require stricter cloud screening, atmospheric correction, and validation. The principal methodological risks are confusion between land surface temperature and true marine SST, inadequate quality-flag filtering, nearshore mixed pixels, inconsistent temporal compositing, and weak validation. A reproducible workflow is proposed that integrates dataset selection, quality assurance, temporal compositing, anomaly analysis, in-situ or multi-product validation, and uncertainty reporting. GEE is therefore best regarded as an analytical infrastructure rather than an SST algorithm itself; scientific reliability ultimately depends on sensor physics, product quality, validation design, and transparent code-based processing.
Dinamika Suhu Permukaan Laut MODIS Aqua di Kragan Tahun 2016–2025 Berbasis Google Earth Engine (GEE) Jihan Nur Fadhilah; Luhur Moekti Prayogo
Applied Engineering: Jurnal Rekayasa dan Sains Terapan Vol. 1 No. 2 (2026): October
Publisher : GRM Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67710/appliedengineering.v1i2.18

Abstract

Suhu permukaan laut (SPL) merupakan parameter penting untuk menggambarkan dinamika perairan pesisir. Penelitian ini menganalisis variabilitas spasial dan temporal SPL di Perairan Kragan, Kabupaten Rembang, selama 2016–2025 menggunakan citra MODIS Aqua Level-3 Standard Mapped Image (L3SMI) yang diolah dengan Google Earth Engine (GEE). Penelitian menggunakan pendekatan kuantitatif-deskriptif melalui ekstraksi SPL pada region of interest perairan, penghitungan rata-rata tahunan, penelaahan pola spasial, serta pengujian kesesuaian terhadap 12 pasangan pengamatan lapangan menggunakan root mean square error (RMSE). Rata-rata SPL tahunan berkisar 28,8–29,9°C; rata-rata terendah terjadi pada 2018 dan tertinggi pada 2016. Suhu minimum yang dilaporkan adalah 26,71°C pada September 2019, sedangkan maksimum 32,42°C pada Oktober 2020. Secara umum, suhu di dekat pantai lebih tinggi daripada perairan yang lebih jauh dari pantai. Variasi temporal dibahas dalam kaitannya dengan perubahan musim monsun. RMSE terhadap pengamatan lapangan dilaporkan sebesar 0,3°C. Temuan ini menyajikan gambaran variabilitas SPL selama satu dekade berdasarkan satu produk satelit yang konsisten, dengan interpretasi hasil validasi tetap memperhatikan keterbatasan jumlah data lapangan.
Co-Authors Luhur Moekti Prayogo