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The Canadian Space Agency (CSA) has awarded $5 million total in grants to 13 academic institutions for the Research Opportunities in Satellite Earth Observation, smartEarth initiative, issued in October 2023.
The CSA defines the smartEarth initiative as follows: smartEarth is the CSA’s funding initiative related to EO applications development. It fosters a smart use of satellite data to develop solutions to key challenges on Earth and in our everyday lives.
Each project has three years to be completed and the maximum grant per project was $312,500. The CSA states that these smartEarth projects involve 144 highly qualified personnel and is expected to include 69 students.
The objectives of these smartEarth projects are:
- Advancing knowledge, research capacity and teaching innovation for the post-secondary academic community interested in remote sensing and/or Earth observation. In this regard, we encourage collaboration with other academic departments/faculties, post-secondary institutions, Canadian space industry, other government departments and international partners;
- Supporting the development of space solutions in the form of innovative applications (new applications, data products, models/prototypes, methodologies, teaching/learning methods and tools education material, systems, services, etc.) to meet today’s challenges resulting in a better future for all Canadians;
- Supporting research for innovative approaches to EO-focused teaching and learning in Canada, include new didactic concepts for EO-focused teaching and learning, such as the flipped classroom approach or problem-based learning, hybrid face-to-face/online interactive teaching and learning, as well as utilization of geospatial data cubes, AI, DL;
- Leveraging emerging opportunities such as digital technologies and capabilities (for example, cloud computing, machine learning, deep learning and artificial intelligence), as well as the growing sources of satellite remote sensing/Earth observation data to maximize opportunities and research potential.
In total 17 projects were funded and are outlined below.
The Governors of the University of Alberta
Project Title: Using Generative AI and Satellite EO Data for Uncertainty-Aware Downscaling of ALS Point Clouds with Case Study on Fuel Attribute and Fire Growth Modelling
This project aims to create detailed 3D models of Canada’s vast forests using satellite data and computer algorithms, improving the coverage of fine-detailed data across large areas. This innovative approach will aid in forest monitoring, wildfire mapping, and climate change impact assessment, offering a valuable tool accessible through an open data sharing portal for researchers and the public.
The Governors of the University of Calgary
Project Title: The joint Copernicus Expansion Missions Sea Ice Experiment (CEMSIE)
The Copernicus Expansion Missions Sea Ice Experiment (CEMSIE), led by a consortium of Canadian and European universities, with support from the European Space Agency, aims to enhance satellite monitoring of Arctic sea ice. This is to be achieved by simultaneously deploying multiple surface-based electromagnetic instruments in Dease Strait, near the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. These instruments mimic three soon-to-be-launched ESA Copernicus Sentinel Expansion mission satellites: CRISTAL, CIMR, and ROSE-L.CEMSIE’s primary objectives include demonstrating how data integration from these three sensors can provide more comprehensive information than the sum of their parts. This integration aims to reduce uncertainties and enhance the accuracy of microwave satellite estimates of sea ice concentration, snow depth, and sea ice thickness.
Carleton University (Ontario)
Project Title: Building Capacity in Satellite-Based EO and HQP Training
This project aims to enhance Canada’s training capacity in satellite-based EO to meet the growing demand for skilled professionals capable of handling large datasets and utilizing technologies like cloud computing and machine learning. Together with industry and government partners across various application areas, the project team will revise existing courses and develop new training materials to address emerging gaps, ensuring a mix of traditional university courses and flexible workshops accessible to professionals and students, ultimately providing long-lasting benefits to Canadians beyond the project’s duration.
Dalhousie University (Nova Scotia)
Project Title: Fine Resolution Classification of Sea Ice Based on Feature Selection from RADARSAT Constellation Mission
The project aims to develop machine-learning-based methods for automatic estimation of Arctic ice concentration, classification of sea ice types, and monitoring of pack ice leads in the Arctic Ocean, focusing on regions like the Beaufort Sea. By utilizing synthetic aperture radar (SAR) data from satellites such as RADARSAT Constellation Mission (RCM) and RADARSAT-2, along with future NISAR observations, the project aims to provide the Canadian Ice Service (CIS) with improved operational ice charts, enhancing marine nowcasts and forecasts in the Canadian Arctic without manual intervention.
Institut national de la recherche scientifique (INRS) (Quebec)
Project Title: Spatio-temporal mapping of slush and sea ice conditions using multimodal Earth observations to promote safe travel in Nunavik, Quebec
This project aims to provide vital spatio-temporal maps of sea ice roughness, thickness, and slush conditions for Nunavik communities; these maps are crucial for safe and efficient travel in the face of changing climate conditions. By combining traditional knowledge with satellite and ground-based observations, including drone imagery and Ground Penetration Radar, the project will offer near-real-time access to reliable satellite maps through collaboration with the Kativik Regional Administration, benefiting the safety and well-being of Nunavummiut.
McGill University (Quebec)
Project Title: Advancing validation/ upscaling methods and algorithms for spaceborne reflectance products of Canadian peatlands
This project focuses on developing advanced methodologies and artificial intelligence algorithms to validate satellite systems such as EnMAP, Sentinel-2, and PlanetScope, which are crucial for monitoring Canada’s vast peatlands. By leveraging ground-based measurements and hyperspectral drone technology at sites like the Mer Bleue Bog, the project aims to enhance Canada’s capability to monitor and understand peatland carbon storage and its implications for climate change mitigation.
Memorial University of Newfoundland (MUN)
Project Title: Satellite EO-based approaches to the creation of a framework and qualified workforce for synoptic study and prediction of one of Canada’s most important ocean resources
This project aims to develop a comprehensive mapping and predictive approach for Canada’s kelp beds using satellite remote sensing technologies, in order to address critical gaps in understanding scale-dependent processes affecting these ecologically and economically significant marine habitats. Through a multidisciplinary team and training program, the project will fulfill specific research objectives, including the creation of learning modules, community engagement, development of detection models using deep learning, and advanced visualization tools, ultimately enhancing Canada’s marine research capabilities and competitiveness on the global stage.
University of Saskatchewan (Two awards)
Project Title: Enhancing Woody Plant Encroachment Detection in Grasslands Using Multi-Source EO Data and Modern Data Processing Technologies Benefiting Canadian Environment and Economy
This project aims to address the rapid disappearance of grasslands, particularly due to woody plant encroachment (WPE), which has become the second most significant cause of grassland loss after land conversion to cropping. By leveraging advanced technology and diverse satellite imagery, the project seeks to develop methods to accurately estimate woody plant cover, detect WPE at an early stage, investigate driving factors, identify vulnerable regions, and assess the economic and environmental benefits of WPE detection on Canadian grasslands, ultimately providing a comprehensive understanding of and methodologies for WPE detection and impacts.
Project Title: Wall to Wall Mapping of N2O Emission Hotspots on Prairie Cropland
Nitrous oxide (N2O) emissions, primarily from agricultural activities, pose a significant challenge in terms of global warming, with Canada contributing substantially. By integrating satellite imagery and field-based measurements, this project aims to map high-risk areas for N2O emissions in western Canada, potentially reducing emissions by 40% through targeted mitigation strategies, with real-time recommendations for climate change adaptation.
Simon Fraser University (British Columbia)
Project Title: Remote predictive mapping of eskers
This project aims to enhance understanding of Canada’s surficial geology by developing an automated method using satellite imagery to map landforms like eskers and estimate their composition. By integrating machine learning and morphometric analysis, the project seeks to streamline natural resource projects, particularly those reliant on gravel from eskers, by providing precise remote estimates of aggregate deposits, benefiting infrastructure development initiatives.
University of Sherbrooke (Quebec) (Two awards)
Project Title: Adapting the products of the new SWOT satellite to the Canadian context
The Surface Water Ocean Topography (SWOT) satellite mission that was launched in 2022 offers an unprecedented ability to measure water levels in Canada’s vast network of lakes and rivers, which are essential to ecosystems as well as economic and cultural activities. Adapting SWOT data to the Canadian context as part of a research project, and in particular to the issue of ice cover, will improve water resource management for organizations such as Environment and Climate Change Canada and dam managers, and facilitate adaptation to the effects of climate change on water resources.
Project Title: AI-Driven Adjacency-Effect Corrections for Improved Remote Sensing of Inland Lakes
The objective of this project is to develop an AI-based algorithm that can improve atmospheric correction of satellite images of Canadian lakes. This will address current inaccuracies that hinder monitoring efforts, which are crucial for various aspects of Canadian life. The project aims to create a hyperspectral algorithm for accurate atmospheric correction by collecting in-situ data and using advanced modeling techniques. AI will be leveraged to improve computational efficiency and enable more precise utilization of remote sensing data. This will ultimately advance the field of lake monitoring and benefit multiple stakeholders.
University of British Columbia (Two awards)
Project Title: Canada-Wide Mapping of Forest Fuel Attributes Using Space-borne LiDAR, Structural Simulations and Time Series Satellite Data
This project aims to develop open-access tools integrating LiDAR-derived forest fuel assessments with satellite data to extrapolate critical forest fuel attributes across Canada’s forested areas; this information is crucial for effective forest management and fire decision-making. By leveraging innovative modeling techniques and open data distribution, the project seeks to advance forest fuel assessment methods, potentially reaching an application readiness level of 6, offering valuable resources for stakeholders and enhancing wildfire risk management.
Project Title: FIRECAN: Enhancing Wildfire Plume Modelling in Canada through Sentinel Satellite Data Integration and Experimental Measurements
This project aims to use satellite measurements and targeted experiments to evaluate and improve the current understanding of chemical emissions from Canadian forest fires, which is crucial for enhancing modeling of smoke plumes and assessing their impacts on human health and the environment. By focusing on obtaining glyoxal (ethanedial) and formaldehyde column densities and refining emission factors and branching ratios, the research seeks to advance forest fire smoke modeling, potentially mitigating the societal challenges posed by the increasing prevalence of forest fires.
University of Quebec in Rimouski
Project Title: Advancing our Understanding of Ocean Biological Carbon Pumps of the Arctic and Sub-Arctic Seas using Hyperspectral Observations of Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission
This project, which involves remote sensing specialists and oceanographers from universities and government departments, aims to leverage NASA’s PACE mission to improve the accuracy of ocean colour observations, particularly in eastern Canada, to better understand the ocean’s capacity to uptake carbon and its changes over time. By enhancing phytoplankton biomass estimation and extracting additional information on phytoplankton properties, the research aligns with objectives to address climate change, foster partnerships, and enhance ocean monitoring efforts critical for ecosystem health and carbon management.
University of Victoria (British Columbia)
Project Title: Satellite-Based Kelp Mapping (SKeMa): A Software Framework for First Nations
This project, conducted in collaboration with First Nations groups and other organizations, aims to develop a framework utilizing satellite imagery to monitor canopy-forming kelp forests in British Columbia, crucial for maintaining marine ecosystems and supporting culturally significant species. By creating software and providing training courses for First Nations communities, the project seeks to give local stakeholders the means to monitor and manage their marine territories more effectively, aligning with Canada’s marine protection targets and strategies.
York University (Ontario)
Project Title: Accurate Forest Carbon Quantification from SEO Data to Drive Nature-Based Climate Solutions
This project aims to leverage satellite EO data to advance forest carbon quantification, addressing the urgent need for accurate information on carbon sequestration and fostering expertise in climate change mitigation. By improving understanding of carbon dynamics, developing AI methods for carbon estimation, and assessing forest management impacts, the project aims to elevate the application readiness level of developed methods and contribute to informed decision-making, benefiting both the environment and Canadian communities while raising awareness of EO and climate science through outreach activities.
