Colby Dernis is an incoming M.S. student in the Jarvis Coastal Plant Ecology Lab at the University of North Carolina Wilmington. His research focuses on integrating satellite remote sensing with ecological field methods to better understand and monitor vulnerable coastal and marine ecosystems. As an undergraduate at the University of Florida, he conducted research on seagrass habitat restoration and water quality monitoring across estuaries in the Gulf of Mexico and Gulf of Maine.
Attending the Cornell Summer Remote Sensing Workshop was an incredible opportunity to strengthen my understanding of satellite ocean color remote sensing while building lasting connections with students and researchers across the marine science community. The course provided valuable hands-on experience with the theory, coding, and processing of satellite data used to support a wide range of oceanographic research. I especially appreciated the balance between lectures and computer exercises, which allowed us to immediately apply the concepts we learned each day. Dr. Bruce Monger and TA Izzie Fatland were incredibly knowledgeable, patient, and supportive throughout the course. I have no doubt that the skills and confidence I gained will be invaluable as I begin graduate school and continue pursuing research in aquatic remote sensing.
Anna Zhu
I am a PhD Candidate in the Graduate School of Geography at Clark University. My research examines how phytoplankton are changing with ongoing environmental change in the Pacific Arctic Region using both field based and remotely sensed data.
I am extremely grateful for the opportunity from OCB to attend Dr. Bruce Monger's Satellite Remote Sensing Course at Cornell this summer. This course was extremely approachable and informative, introducing the data and tools needed for ocean remote sensing. As someone's whose strengths do not lie in remote sensing and coding, I found this class to be very rewarding, and I now feel more confident in my remote sensing and Python skills. Thank you again to Bruce and OCB!
Sachithma (Sachi) Edirisinghe
I am a graduate student in Oceanography at the University of Maine, working on the ocean biological carbon pump. I have been using in-situ observational methods for my research, and will be incorporating satellite remote sensing observations as well.
The 2026 Cornell Remote Sensing Course was a great experience. The lectures in the morning covering background followed by hands-on practical sessions in the lab working with scripts and datasets on JupyterHub, contributed to my understanding of the concepts covered. I came away with the knowledge and skills to start to apply remote sensing in my own research. I really enjoyed the teaching style and atmosphere of the classroom as well.
Lillian Miller
Lillian recently completed her first year as a PhD student in Earth and Environmental Science at the University of Pennsylvania, where she also earned dual bachelor’s degrees in Chemistry and Earth and Environmental Science in 2025. She works in Dr. Irina Marinov’s Climate Dynamics Lab, studying ocean ecology and biogeochemistry in climate models, with a particular focus on the role of the Southern Ocean in the global carbon cycle.
Her research combines large-scale climate model and observational datasets to investigate phytoplankton dynamics and carbon cycling, as well as evaluating model performance. She is especially interested in applying emerging tools and datasets, including remote sensing approaches, to better understand high-latitude ocean ecosystems and their response to climate change.
Beyond research, Lillian is actively involved in science policy, communication, and education initiatives. She serves as a Local Science Partner with the American Geophysical Union and has contributed to science policy efforts through the Penn Science Policy and Diplomacy Group. She is passionate about connecting scientific research with policy and public engagement to support evidence-based environmental solutions.
Participating in Cornell’s Satellite Remote Sensing workshop was an incredible opportunity and gave me valuable hands-on experience working with data from a variety of satellite sensors, including PACE, MODIS, and SeaWiFS. I learned how to process satellite data using SeaDAS to generate different ocean products, such as chlorophyll concentrations and phytoplankton size classes, while also gaining a better understanding of the algorithms and methods behind these datasets. I am excited to take this new knowledge into my research to better understand changing ecology in regions of the Southern Ocean.
Beyond providing valuable technical skills and knowledge, Bruce was an incredible mentor who was always willing to help troubleshoot challenges and fostered a welcoming and collaborative environment throughout the course. This made it easy to connect with others, build professional relationships, and form friendships that I hope to maintain throughout my career.
I am very grateful to OCB for funding this opportunity and making this experience possible!
Israt Jahan Mili is a Ph.D. candidate at the School for Marine Science and Technology, University of Massachusetts Dartmouth, option area: Marine and Atmospheric System Modeling and Analysis. She is also affiliated as a teaching faculty member in the Department of Oceanography and Hydrography at Bangladesh Maritime University, Dhaka, Bangladesh, and is currently on study leave to pursue her doctoral research at UMassD, which she began in September 2022.
Her doctoral research focuses on developing a hyperspectral phytoplankton absorption-based, wavelength-resolved primary production algorithm for the northern Gulf of Mexico using in situ light profiles, photosynthesis–irradiance measurements, and filterpad absorption data. This algorithm is applied to hyperspectral ocean color satellite observations, such as PACE-OCI data, and will also be applicable to upcoming hyperspectral satellite observations, such as GLIMR.
The Cornell Satellite Remote Sensing Workshop was an excellent fit for my research goals, as it provided hands-on experience in satellite data retrieval, subsetting, processing, visualization, interpretation, and particularly processing satellite ocean color data from Level-1 to Level-2 and Level-3 using NASA SeaDAS prewritten python scripts.
During the workshop, I worked with a wide range of satellite products, including PACE-OCI, MODIS, VIIRS, OLCI, SeaWiFS, GHRSST SST, QuikSCAT vector winds, SSMI, ASCAT, WindSat, and altimetry data. Learning alongside peers from diverse research backgrounds was another valuable part of the program, as it helped me expand my professional network and feel more connected to the broader satellite remote sensing and ocean color scientific community. I greatly appreciate Dr. Bruce Monger’s tireless guidance and support throughout the workshop, which helped build my confidence in processing satellite data independently. Since much of our primary production algorithm development has been carried out in MATLAB, the workshop also helped me become more comfortable using Python scripts to improve reproducibility and accessibility for future users. I highly recommend this workshop to anyone seeking hands-on experience with satellite data processing and its application in oceanographic research.
Since the early to mid-20th century, shipboard marine ecological time series (METS), or sustained repeat measurements of physical, biogeochemical, and biological processes, have documented changes in ocean biogeochemistry and marine ecosystems. To date, numerous disconnected databases and interfaces for accessing data have made it difficult for prospective users to find and use these datasets. Limited data access, discoverability, reporting, and interoperability represent a barrier to analyses within and across METS. Standardized guidelines on METS sampling and analytical methods, data and metadata reporting protocols, data sharing culture, and data citation practices are needed to track and expand time series data usage and facilitate comparative studies across METS stations. It is this set of data-related challenges that the METS Research Coordination Network set out to address. This report is a summary of METS RCN activities and efforts, including a culminating community workshop that included representation from a globally distributed set of METS. The report provides informatics tools and resources that are specific to METS datasets and includes actionable recommendations for strengthening a global METS network and broadening applications and users of METS datasets.
On behalf of the GLODAP Reference Group and hundreds of seagoing oceanographers who have tirelessly collected data all over the ocean for several decades, it is our pleasure to announce the release of the new Global Ocean Data Analysis Project version 3, GLODAPv3!
