Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5-9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11-12 cm and bias of 2.7-6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover' extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001-2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD <= 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability.

Knowledge of the spatial and temporal distribution of active layer thickness (ALT) throughout northern Alaska would help to understand the effects of climate change in the region, as well as to quantify how much the permafrost degradation manifestly in progress there is contributing to the accumulation of greenhouse gases in the atmosphere. For this reason, we are developing extensive high-resolution maps of ALT in northern Alaska. We use machine learning along with an extensive set of spatial data layers to upscale ALT from thousands of training pixels taken from high resolution swaths of estimated ALT derived from airborne polarimetric P-band synthetic aperture radar (SAR). The resulting maps of up-scaled ALT have been compared to thousands of validation samples set aside from the PolSAR-derived swaths and to in situ ALT measurements. The maps have achieved root-mean-square errors (RMSEs) of 5-7 cm relative to validation samples, and RMSEs of approximately 10-12 cm relative to in situ ALT measurements.

Rapid climate warming has widely been considered as the main driver of recent increases in Arctic tundra productivity. Field observations and remote sensing both show that tundra greening has been widespread, but heterogeneity in regional and landscape-scale trends suggest that additional controls are mediating the response of tundra vegetation to warming. In this study, we examined the relationship between changes in vegetation productivity in the western Canadian Arctic and biophysical variables by analyzing trends in the Enhanced Vegetation Index (EVI) obtained from nonparametric regression of annual Landsat surface reflectance composites. We used Random Forests classification and regression tree modelling to predict the trajectory and magnitude of greening from 1984 to 2016 and identify biophysical controls. More than two-thirds of our study area showed statistically significant increases in vegetation productivity, but observed changes were heterogeneous, occurring most rapidly within areas of the Southern Arctic that were: (1) dominated by dwarf and upright shrub cover types, (2) moderately sloping, and (3) located at lower elevation. These findings suggest that the response of tundra vegetation to warming is mediated by regional- and landscape-scale variation in microclimate, topography and soil moisture, and physiological differences among plant functional groups. Our work highlights the potential of the joint analysis of annual remotely sensed vegetation indices and broad-scale biophysical data to understand spatial variation in tundra vegetation change.