How do you personally and professionally relate to water and/or space technologies?
Professionally, my work is centered around using satellite observations to better understand aquatic and coastal environments. Water presents unique challenges for remote sensing because light behaves very differently in the atmosphere and within the water column. Personally, I am drawn to water because of its central role in ecosystems, livelihoods, and climate processes. Space technologies offer a way to observe these systems consistently and at scales that would otherwise be impossible.
What is your proudest professional moment or research project?
One of my proudest professional achievements was publishing research that evaluated the performance of atmospheric correction algorithms for coastal and aquatic remote sensing. In this work, I assessed widely used methods – including those distributed by NASA – and demonstrated that approaches designed primarily for land can introduce significant errors when applied over water, particularly in optically complex coastal environments. By identifying the sources of these biases and proposing water-appropriate correction strategies, my research helped improve the understanding of how atmospheric correction should be applied for aquatic applications. It was especially rewarding to see this work subsequently cited by NASA and others and contribute to improvements in operational atmospheric correction products used for water remote sensing.
Can you tell us about your current work as an independent remote sensing contractor?
As an independent remote sensing contractor, I work on satellite-derived bathymetry (SDB) and broader water-focused Earth observation projects that support navigation safety, coastal management, and environmental monitoring. My work spans satellite image preprocessing, atmospheric correction, radiative transfer modelling, and quality control to ensure results are scientifically robust and operationally reliable. I am currently investigating physics-informed machine learning (PIML) - an approach that embeds governing physical laws, such as radiative transfer in the atmosphere and water column and hydrodynamic constraints, directly into deep learning architectures. By coupling physics with data-driven methods, PIML can deliver more accurate, transferable, and uncertainty-aware predictions, particularly in data-scarce environments where purely empirical models tend to fail. Alongside this, I am exploring how retrieval-augmented generation (RAG) can translate complex scientific knowledge into accessible, decision-ready guidance for practitioners.
Please explain how Satellite-Derived Bathymetry (SDB) works. What are the advantages and disadvantages, and how are they best integrated with remote sensing data?
Satellite-Derived Bathymetry (SDB) estimates water depth from satellite imagery by analysing how sunlight is absorbed and scattered as it passes through the atmosphere, the water column, and reflects from the seabed. There are two main approaches. Empirical methods use statistical relationships between satellite reflectance and known depth measurements, while physics-based methods explicitly model light interactions using radiative transfer theory.
Empirical approaches are relatively simple to apply and can perform well when high-quality calibration data are available, but their accuracy is often site-specific, and they do not transfer well between locations or sensors. Physics-based methods are more physically grounded and transferable, but they require careful atmospheric correction and a greater understanding of optical conditions.
The main advantages of SDB are its wide spatial coverage, relatively low cost compared to field surveys, and ability to provide rapid mapping in shallow and remote coastal areas. Limitations include sensitivity to water clarity, bottom type, sun glint, and cloud cover, and a general restriction to shallow depths.
SDB is most effective when used alongside complementary data such as acoustic surveys and tide information to support validation and uncertainty assessment. When applied in this way, it provides valuable baseline information for navigation safety, coastal management, and water-related decision-making.
Are there any open bathymetry data sources available?
Yes - several open sources are available. The General Bathymetric Chart of the Oceans (GEBCO) provides a global digital bathymetric grid synthesizing ship soundings and satellite-derived gravity data, while the European Marine Observation and Data Network (EMODnet) supplies harmonized marine data for European waters.
NASA's ICESat-2 mission has more recently emerged as a transformative addition. Although designed primarily for ice and land-elevation measurement, its ATLAS photon-counting lidar penetrates clear water and is now widely used to derive depth in remote and data-sparse regions, and to calibrate satellite-derived bathymetry workflows worldwide.
Citizen science has also become a valuable contributor. The IHO Crowdsourced Bathymetry Database, hosted at NOAA's Data Centre for Digital Bathymetry, now contains more than 117 million points of depth data contributed by mariners globally. In Canada, the CHS Community Hydrography Program, launched in 2022, supports coastal and Indigenous communities in collecting nearshore bathymetric data - addressing the substantial survey gaps that remain across Canada's waters, particularly in the Arctic.
Initiatives such as ESA's Sentinel Coastal Charting Worldwide project also demonstrate how Sentinel-2 imagery can be processed to map shallow-water bathymetry. These satellite-driven methods are particularly useful where traditional survey data are sparse, though they often require calibration or validation with ground truth for operational use.
Your Ph.D. Thesis is titled “Physics-based satellite-derived bathymetry for nearshore coastal waters in North America”. What are your main findings?
My PhD research demonstrated that physics-based satellite-derived bathymetry can provide reliable and policy-relevant information for nearshore coastal waters when applied with appropriate care. A key finding was that the choice of atmospheric correction method strongly influences bathymetric results in optically complex coastal environments. By systematically comparing commonly used correction approaches, I showed that methods tailored for water applications produce more consistent and transferable outcomes across sensors and regions. The research highlighted the importance of clearly understanding method limitations and validating results when satellite-derived products are used to support evidence-based decision-making related to SDG 6 (Clean Water and Sanitation) and SDG 14 (Life Below Water).
Can you tell us about your contributions to Canada’s smartWhales initiative?
I contributed to Canada’s smartWhales initiative by supporting satellite-based analysis of oceanographic indicators relevant to whale presence, particularly in the Gulf of St. Lawrence and the Gulf of Maine. My role involved processing long-term satellite time series to derive indicators such as chlorophyll concentration and phytoplankton dynamics, which are linked to prey availability. These analyses helped inform predictive tools aimed at reducing ship-strike risk and supporting conservation-driven management decisions. The work demonstrated how Earth observation data can complement traditional monitoring approaches for marine conservation.
What role do space technology and data play in conservation efforts in the Bahamas and in other Small Island Developing States (SIDS)? What are the challenges?
Space technologies play a critical role in conservation efforts in the Bahamas and other Small Island Developing States by enabling consistent, large-area monitoring of coastal and marine environments. Satellite data are widely used for habitat mapping, shoreline change detection, shallow-water bathymetry, and monitoring coastal pressures that affect ecosystems and livelihoods. These observations support evidence-based planning related to SDG 14 (Life Below Water) and SDG 13 (Climate Action), particularly in regions where field surveys are costly or logistically challenging.
Key challenges include persistent cloud cover in tropical regions, limited availability of in-situ data for validation, and constraints in local technical capacity to process, interpret, and sustain the use of satellite information. Addressing these challenges requires not only improved data access but also targeted capacity building, long-term partnerships, and the integration of satellite-derived information with local knowledge and management priorities.
Based on your experience, how can we improve awareness and capacity building on the use of space technology for water? Where do you see challenges in this field?
Improving awareness and capacity building requires clear, practical guidance that goes beyond data access and focuses on how satellite information can be applied responsibly in real decision-making contexts. Training materials should emphasize fundamentals, common pitfalls, and good practices, supported by real-world case studies that are relevant to local water management challenges.
A major challenge is the gap between the growing availability of satellite data and the ability of institutions to interpret, validate, and sustain its use. Addressing this gap requires long-term capacity-building efforts, support for local expertise, and stronger links between data providers, practitioners, and decision-makers to ensure space-based information contributes meaningfully to water-related goals under SDG 6 (Clean Water and Sanitation) and related targets.
What do you need to innovate?
For me, innovation begins in my core area of satellite-derived bathymetry, where I am developing physics-informed machine learning (PIML) methods that embed radiative transfer and water-column physics directly into deep learning models. The goal is to produce depth predictions that are not only accurate, but also transferable across sensors and regions and accompanied by meaningful uncertainty estimates - qualities essential for SDG 6 (Clean Water and Sanitation) and SDG 14 (Life Below Water). Building on this foundation, I aim to extend the same approach to other domains where uncertainty drives consequential decisions: agriculture, where embedding crop and soil-water physics can strengthen food security under SDG 2; energy and grid modernization, where physics-constrained renewable forecasts support SDG 7 and SDG 13; and health, where coupling environmental drivers with epidemiological models can sharpen early-warning systems under SDG 3.
To advance this work, three enablers are essential: high-quality, well-documented validation data to anchor hybrid physics-AI models in trustworthy evidence; sustained collaboration with the domain scientists whose physics PIML embeds; and institutional support providing the time and computational resources required for careful method development and validation. With these in place, innovation produces approaches that are technically advanced, transparent, and ready to inform real-world decisions for sustainable development.
What is your favourite aggregate state of water?
Liquid water, because it is the state most directly linked to ecosystems, water resources, and human livelihoods.
