Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an essential climate variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at higher (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE at their native resolution. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making OC data at native resolution readily available in GEE.

Sea surface temperature (SST) and chlorophyll-a (Chl-$a$) are key indicators of ecosystem variability in temperate coastal-open ocean environments, reflecting both physical and biological responses. This study investigates the spatiotemporal variability of SST and Chl-$a$ in Sagami Bay using level 2 ocean color satellite data from Second generation Global Imager (SGLI)/Global Change Observation Mission-Climate (GCOMC) from 2018 to 2024. To address substantial data gaps caused by frequent cloud cover and sensor limitations such as sun glint, atmospheric interference, and land adjacency effects, we reconstructed missing values using the Data INterpolating Empirical Orthogonal Functions (DINEOF) method. Validation against randomly masked original satellite pixels and in-situ measurements confirmed that DINEOF shows significant correlation in filling gaps in both SST band Ch-$a$. Empirical Orthogonal Function (EOF) analysis was conducted on the DINEOF-reconstructed monthly datasets to extract dominant spatiotemporal modes of variability. SST showed strong seasonal signals in the leading EOF mode, with subsequent modes reflecting mesoscale variability, including Kuroshio intrusions. EOF modes of Chl-$a$ revealed seasonal bloom patterns and spatially complex responses to physical forcing, particularly in nearshore and river-influenced areas. The results highlight the importance and effectiveness of satellite data gap-filling for capturing short-term events and mesoscale dynamics in coastal waters. Our findings provide an insight into the spatiotemporal variability of physical and biological parameters in Sagami Bay and demonstrate a scalable approach for monitoring coastal systems under environmental change.

Data from ocean color (OC) remote sensing are considered a cost-effective tool for the study of biogeochemical processes globally. Satellite-derived chlorophyll, for instance, is considered an Essential Climate Variable since it is helpful in detecting climate change impacts. Google Earth Engine (GEE) is a planetary scale tool for remote sensing data analysis. Along with OC data, such tools allow an unprecedented spatial and temporal scale analysis of water quality monitoring in a way that has never been done before. Although OC data have been routinely collected at medium (~1 km) and more recently at high (~250 m) spatial resolution, only coarse resolution (≥4 km) data are available in GEE, making them unattractive for applications in the coastal regions. Data reprojection is needed prior to making OC data readily available in the GEE. In this paper, we introduce a simple but practical procedure to reproject and ingest OC data into GEE. The procedure is applicable to OC swath (Level-2) data and is easily adaptable to higher-level products. The results showed consistent distributions between swath and reprojected data, building confidence in the introduced framework. The study aims to start a discussion on making high resolution OC data readily available in GEE.