The Cordilleras Huayhuash and Raura are remote glacierized ranges in the Andes Mountains of Peru. A robust assessment of modern glacier change is important for understanding how regional change affects Andean communities, and for placing paleo-glaciers in a context relative to modern glaciation and climate. Snowline altitudes (SLAs) derived from satellite imagery are used as a proxy for modern (1986–2005) local climate change in a key transition zone in the Andes. Clear sky, dry season Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) satellite images from 1986–2005 were used to identify snowline positions, and their altitude ranges were extracted from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital elevation model (DEM). Based on satellite records from 31 glaciers, average snowline altitudes (SLAs), an approximation for the equilibrium line altitude (ELA), for the Cordillera Huayhuash (13 glaciers) and Cordillera Raura (18 glaciers) from 1986–2005 were 5051 m a.s.l. from 1986–2005 and 5006 m a.s.l. from 1986–2002, respectively. During the same time period, the Cordillera Huayhuash SLA experienced no significant change while the Cordillera Raura SLA rose significantly from 4947 m a.s.l. to 5044 m a.s.l.

Glacier surface melt dynamics throughout Novaya Zemlya (NovZ) and Severnaya Zemlya (SevZ) serve as a good indicator of ice mass ablation and regional climate change in the Russian High Arctic. Here we report trends of surface melt onset date (MOD) and total melt days (TMD) by combining multiple resolution-enhanced active and passive microwave satellite datasets and analyze the TMD correlations with local temperature and regional sea ice extent. The glacier surface snowpack on SevZ melted significantly earlier (−7.3 days/decade) from 1992 to 2012 and significantly longer (7.7 days/decade) from 1995 to 2011. NovZ experienced large interannual variability in MOD, but its annual mean TMD increased. The snowpack melt on NovZ is more sensitive to temperature fluctuations than SevZ in recent decades. After ruling out the regional temperature influence using partial correlation analysis, the TMD on both archipelagoes is statistically anti-correlated with regional late summer sea ice extent, linking land ice snowmelt dynamics to regional sea ice extent variations.

Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember reference library to unmix hyperspectral imagery and map vegetation coverage at the level of plant functional type (PFT), across three wetland sites in sub-arctic Alaska. This study explores the optimization and parametrization of multiple endmember spectral mixture analysis (MESMA) models to estimate coverage of PFTs across wetland classes. We use partial least squares regression (PLSR) to identify a parsimonious set of critical bands for unmixing and compare the reference and modeled coverage. Unmixing, using a full set of 110-bands and a smaller set of 4-bands, results in maps that effectively discriminate between PFTs, indicating a small investment in fieldwork results in maps mirroring the true ground cover. Both sets of spectral bands differentiate between PFTs, but the 4-band unmixing library results in more accurate predictive mapping with lower computational cost. Reducing the unmixing reference dataset by constraining the PFT endmembers to those identified in the field-site produces only a small advantage for mapping, suggesting extensive fieldwork may not be necessary for MESMA to have a high explanatory value in these remote environments.