DINEOF reconstruction for creating long-term cloud-free sea surface temperature data records: A Case study in Lombok Strait, Indonesia

peer reviewedA long-term reliable sea surface temperature (SST) satellite data record is requisite resources for monitoring to understand climate variability. Creating a long-term data record especially for climate variability requires a combination of multiple satellite products. Consequently, missing data issues are inevitable. Hence, DINEOF (Data Interpolating Empirical Orthogonal Functions) has been applied to attain a complete and coherent multi-sensor SST data record with EOF-based technique by reconstructing the missing data. Unfortunately, the technique can lead to large discontinuities in the data reconstruction due to images depiction within long time series data. For that reason, filtering the temporal covariance matrix had been applied to reduce the spurious variability and more realistic reconstructions are obtained. However, this approach has not yet tested in tropical region with higher evaporation which cause incomplete satellite image coverage. Therefore, the objective of this research is to reconstruct SST of Lombok strait with data gaps up to 58.16% in one year. It is successfully reconstructed until the last iteration of 42 optimal EOF modes with the convergence achieved up to 0.9806×10-3, including previous set-aside data for internal cross-validation. The results highlight that the DINEOF method can effectively reconstruct SST data in Lombok Strait

DISTRIBUTION OF SALINITY AND TEMPERATURE IN MUSI ESTUARY: USING VERTICAL SALINITY GRADIENT FOR ESTUARY CLASSIFICATION ZONE

Muara Musi merupakan muara sungai Telang dan Musi yang berbatasan langsung dengan Selat Bangka. Pada saat pasang (surut) kita melihat distribusi salinitas meningkat (menurun) yang diketahui melalui distribusi vertikal menggunakan CTD (Conductivity Temperature Depth). Diagram TS (Temperature-Salinity) digunakan untuk melihat karakteristik massa air di daerah penelitian. Metode DIVA (Data-Interpolating Variational Analysis) digunakan untuk interpolasi dan visualisasi data dari data vertikal dan spasial temperatur, salinitas dan densitas. Klasifikasi zona muara Musi diidentifikasi berdasarkan nilai sebaran salinitas yang memperhitungkan pertukaran salinitas yang bersirkulasi pada saat pasang dan surut. Densitas massa air secara signifikan dipengaruhi oleh salinitas yang terbukti bergradasi. Sementara distribusi suhu tidak berubah secara signifikan dengan kedalaman, distribusi spasial menunjukkan bahwa suhu di estuari lebih rendah daripada di daerah hulu dan laut. Distribusi spasial salinitas menunjukkan bahwa salinitas tinggi memasuki muara menuju sungai lebih jauh pada saat pasang dari pada saat surut. Distribusi salinitas berkisar antara 0,5–30 psu dan suhu antara 29–33 ℃ dari bagian horizontal dan vertikal. Pola sebaran salinitas di muara sungai Musi diidentifikasi, terdiri dari tiga zona yang mewakili kondisi salinitas di daerah penelitian, yaitu zona Polyhaline, Mesohaline, dan Olygohaline.Musi estuary is the mouth of the Telang and Musi rivers directly adjacent to the Bangka Strait. During flood (ebb) we see the distribution of salinity increases (decreases) which is known through the vertical distribution using CTD. The TS diagram is used to see the water mass characteristics the study area. Data-Interpolating Variational Analysis (DIVA) method is used to interpolate and visualize data from vertical and spatial temperature, salinity and density data. The classification of the Musi estuary zone is identified based on the value of the distribution of salinity, which considers the exchange of circulating salinity at flood and ebb. The density of the water mass is significantly affected by the proven graded salinity. While the temperature distribution does not change significantly with depth, the spatial distribution indicates that the temperature in the estuary is lower than in the upstream and ocean areas. The spatial distribution of salinity indicates that high salinity enters the estuary towards the river further at flood than at ebb. Salinity distribution ranges from 0.5 to 30 psu and temperatures between 29 and 33 oC from horizontal and vertical sections. The pattern of salinity distribution in the Musi river estuary was identified, consisting of three zones representing salinity conditions in the study area, namely the Polyhaline, Mesohaline, and Olygohaline zones

Environmental aspects of tuna catches in the Indian Ocean, southern coast of Java, based on satellite measurements

International audienceThe present work seeks to assess the relationship between daily Sea Surface Temperature (SST) and thermal front (Gradient Magnitude method) using AMSR-E, daily Sea Surface Chlorophyll-a (SSC) using MODIS, and daily long line (LL) bigeye (BG), albacore (ALB), yellowfin (YF), and southern bluefin (SBF) tuna catch data expressed as catch per unit effort (CPUE), which was calculated as the number of fish caught by 1000 hooks in 1° latitude by 1° longitude square grid, and integrated into a month of fishing activity for the period of March–December 2010 in South Java (Indian Ocean). Results obtained showed evidences of non-linear relationships between catch yields and environmental data. BG and ALB show largest CPUE values which occurred mainly in the area with SSTs of 26 – 27 °C and SSCs of 0.15 – 0.3mg/m3. CPUEs appear to be “randomly” dispersed and have a slight positive (negative) correlation with SSC (SST) i.e., 0.33 (-0.38) but not with GM values i.e., -0.02. The weak correlation between CPUE, SST, SSC and GM lead to assume that CPUEs might be linked to other influential parameters that are not assessed in the present study but are needed to give complete prediction of potential fishing ground areas, knowing that the length of observation period is less than 1 year