Biochemical composition is proposed to improve process-based models of SOM degradation and climate feedbacks

A convolutional neural network (CNN) approach produced highly accurate vegetation classifications. Hyper-spectral datasets (e.g., AVIRIS) were most useful for our machine learning approaches. Accurate and high-resolution datasets generated using our approach are needed for Arctic models.

Several scaling strategies based on geomorphology were evaluated for complex polygonal landscapes.

Microtopographic variation among troughs, rims, and centers strongly affects the movement of surface water and snow and thereby affects soil water contents and active layer development.

Modeling approach improving representations of permafrost dynamics in evolving landscapes.

-

Study highlights the poor representation of Arctic photosynthesis in TBMs, and provides the critical data necessary to improve our ability to project the response of the Arctic to global environmental change.

-

Ponds and lakes affect high-latitude carbon, water, and energy budgets, however, there is no good observationally-constrained characterization of waterbodies for high-latitude systems. The Permafrost Region Pond and Lake (PeRL) database addresses this problem. PeRL includes 69 maps covering a wide range of environmental conditions.

Observationally-constrained photosynthetic traits for land models.