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Internal solitary wave (ISW) is an underwater wave, formed due to the evolution of internal tides. The presence of breaking ISWs in coastal areas can potentially influence coastal ecosystems through cross-shelf exchange mechanisms. The southern waters of the Kangean Island and the eastern waters of the Madura Island, known also as part of the northern Bali waters, have been an active habitat for ISW propagation. This study aims to identify and characterize ISW which potentially breaking onshore the Southern Kangean Waters (SKW) and the Eastern Madura Waters (EMW) using Sentinel 1 SAR (Synthetic Aperture Radar) imagery. ISW characterization was carried out using the Korteweg-de Vries (KdV) model. The ISW amplitudes which propagate to EMW varies from 1 - 28 m with a propagation speed of 1.60 - 1.98 m/s, while the ISW which propagate to the SKW has an amplitude of 1 - 60 m with a propagation speed of 0.83 to 2.17 m/s. The results show that the ISW propagating to the SKW has stronger amplitude, phase velocity, and horizontal-vertical velocities than the ISW propagating to the EMW. The maximum speed of the horizontal current triggered by ISW leading to the EMW is 0.03 - 0.17 m/s and the vertical velocity 0.11 - 4.53 cm/s; the maximum speed of horizontal velocity leading to the SKW is 0.02 - 1.29 m/s and the maximum speed of the vertical velocity 0.04 - 19.03 cm/s.

The Halmahera Sea is part of the eastern entry route of the Indonesian throughflow (ITF) passages. The characteristics and transformation of the South Pacific water masses are presented based on the HYCOM model (Hybrid Coordinate Ocean Model) and direct observations of CTD (Conductivity, Temperature, Depth) acquired during the East Indonesian Expedition in October 2017 using RV Baruna Jaya VIII. This study is aimed at characterizing the typical water masses observed in the Halmahera Sea and tracking the transformation of the South Pacific water masses in the region. The results show that there was an indication of strong vertical mixing of maximum salinity of the South Pacific thermocline water masses, i.e., South Pacific Subtropical Water (SPSW), from ~35.2 PSU at isopycnal 24 – 25 kg m-3 becomes ~34.8 PSU in the Southern Halmahera Strait (around 200 km from the entry passage, the Northern Halmahera Strait), and rapidly decreases from the Southern Halmahera Strait and Obi Strait to become 34.6 PSU in the Maluku Sea. These preliminary findings suggest a strong mixing in the southern Halmahera Strait and Obi Strait.

Nitrate is an essential nutrient in phytoplankton's photosynthesis process. In addition, phytoplankton uses nitrate for their growth and reproduction. Nitrate abundance on the coast will affect primary productivity and biogeochemical cycles. The availability of nitrate observation data, especially around the Savu Sea coast, is minimal. In this study, the estimation of nitrate in the coastal area of the southern part of Sumba Island and the eastern part of Savu Island by using the generalized additive model (GAM). Seventy-one nitrate observation data were used to build the GAM model, and remote sensing data were used as input data for nitrate estimation. Sea Surface Temperature (SST) and chlorophyll-a data were obtained from Aqua-MODIS. Sea Surface Salinity (SSS) and Sea Surface Windspeed (SSW) data were obtained from a Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) Soil Moisture-Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT), respectively. This study uses the Generalized Additive Model (GAM) approach to predict the distribution of nitrate concentrations and determine the main driving factors associated with nitrate. Based on the result, temperature is the dominant factor in nitrate estimation, while chlorophyll-a has a relatively small influence. The best model to predict nitrate distribution uses four parameters, namely SST, SSS, SSW, and chlorophyll-a. The validation results of the expected nitrate value obtained from the model with the observed nitrate value obtained results with the same value range of 0 - 0.35; the difference is the value of the distribution. From the comparison results, the R2 value is 0.357.

Unmanned aerial vehicles (UAVs) can be used to assess the biophysical parameters of mangroves (particularly tree height and area). UAVs can produce high-resolution imagery, in this study UAV imagery was used to calculate tree height distribution and mangrove area. UAV imagery was taken using a commercial UAV with a planned flight path. The obtained UAV images are then compiled based on coordinate information and z-elevation values, and then 3D modelling is created from the point cloud. The 3D model will be detailed by a dense point cloud. The surface area and texture are obtained from the 3D mesh. Finally, a high-resolution DSM can be generated. DSM is the elevation of the overall surface including vegetation, buildings, and others, while DTM is the elevation data of the ground surface, so the next process is to separate bare ground and above-ground objects. Tree height is obtained from the intersection of DSM and DTM elevation data. The area of mangrove vegetation was obtained using the on-screen digitisation method. Based on the results of the analysis conducted by comparing tree height data from UAV images and in-situ measurement tree height data has a difference with a range of 0.24 m to 0.94 m, with an average of 0.49m. This remote sensing survey research using commercial UAVs can be concluded as one of the solutions to acquire the parameters of tree area and height distribution with good accuracy.

Range, the distance between the satellite and the ocean surface covered by the radar trace, is measured by tracking the received waveform. The range measurement errors may be caused by sea state bias (SSB) which is the main source of measurement errors in altimeter applications. Particularly, the error due to SSB is related to atmospheric dynamics and sea surface roughness. Sea surface height (SSH) is determined by advanced satellite remote sensing in the fields of earth sciences, such as geophysics, geodesy, and oceanography. The research aims to apply the parametric model on the SSB to estimate the SSH over the Savu Sea. We analyzed data along the cycle along the track across the line consisting of the GDR Ku Band. We concluded that the parametric model can be used to effectively improve the SSH correction on the Sentinel 3A altimeter.