Climate change is causing widespread thawing and degradation of permafrost, which has associated impacts on infrastructure, ecosystems, and the global carbon cycle. Data are needed to observe and monitor permafrost and for input into models that project permafrost change. Permafrost is a challenge to study because it is a subsurface condition of the ground, largely found in remote locations, and vastly distributed. An ad hoc committee of experts, under the auspices of the National Research Council, organized a workshop to explore opportunities for harnessing remote sensing technologies to advance our understanding of permafrost status and trends and the impacts of permafrost change.
Many workshop discussions focused on using remote sensing technologies to measure various permafrost properties and processes and other ecological characteristics that can be used to achieve better understanding of permafrost and its dynamics. Measurements of permafrost-related ecological variables provide some crucial information about changes in the relevant ecological characteristics and are used to extract information about permafrost conditions and processes. One innovative example of a permafrost-related ecological variable is the measurement of changes in seasonal micro-topography to estimate ice content in permafrost. Permafrost properties are those characteristics that are inherent to permafrost. Examples include ice content, maximum depth of seasonal thaw (depth to the surface of permafrost), and permafrost temperature. Currently, there are considerably more permafrost-related ecological properties that can be observed with remote sensing methods than permafrost processes and properties. Of the more than 60 permafrost and permafrost-related ecological properties and processes that were discussed during the workshop, the following emerged as having the most impact in advancing the current state of knowledge of permafrost landscapes, if they could be measured through remote sensing:
• Active layer thickness
• Ground ice (volume and morphology)
• Snow characteristics (extent, water equivalent, depth, density, conductivity)
• Surface topography (static, macro-, and micro-)
• Longer-term surface subsidence
• Thermokarst distribution
• Surface water bodies (including dynamics, redistribution)
• Surficial geology-terrain units (including lithology, bedrock)
• Soil organic layer (thickness, moisture, conductivity)
• Land cover (including spectral vegetation indices)
• Vegetation structure and composition
• Methane (flux or concentration)
• Water vapor flux
• Carbon dioxide (flux or concentration)
• Land surface temperature
• Subsurface soil temperature
• Seasonal heave/subsidence
• Soil moisture
• Biomass (above ground)
It was clear from the workshop discussions that innovative, multiscale, multisensor approaches, using ground-based, aircraft, and spaceborne instruments, would substantially advance the current state of knowledge of permafrost landscapes and, in the process, would provide critically needed information on subsurface properties that determine the vulnerability of permafrost systems to warming. The participants discussed the utility of remote sensing observations, both from existing sensors and those expected in the near future, and that it could be advanced through synergistic approaches that would permit derivation of data products characterizing critical permafrost properties across spatial scales. Advancement of techniques and algorithms was emphasized as a means to integrate field measurement with remote sensing observations, allowing improved direct and indirect retrieval of permafrost properties and thereby establishing a baseline against which change and longer-term trends can be assessed. The algorithms, data sets, and derived products would ultimately allow better model-data assimilation, initialization, and parameterization, and thus the advancement of more realistic permafrost models than would otherwise be possible. Taken together, field measurements, remote sensing–derived maps of properties, and improved models would advance our understanding and prediction of the state of permafrost landscapes and associated feedbacks to the climate system.
