Editorial for special issue: "Remote sensing of environmental changes in cold regions"

Cold regions, characterized by the presence of permafrost and extensive snow and ice cover, are significantly affected by changing climate. Of great importance is the ability to track abrupt and longer term changes to ice, snow, hydrology and terrestrial ecosystems that are occurring within these regions. Remote sensing allows for measurement of environmental variables at multiple spatial and temporal scales, providing key support for monitoring and interpreting the environmental changes occurring in cold regions. The recent advances in the application of remote sensing for the analysis of environmental changes in cold regions are documented in this Special Issue

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0

This report documents the GEOS-5 global atmospheric model and data assimilation system (DAS), including the versions 5.0.1, 5.1.0, and 5.2.0, which have been implemented in products distributed for use by various NASA instrument team algorithms and ultimately for the Modem Era Retrospective analysis for Research and Applications (MERRA). The DAS is the integration of the GEOS-5 atmospheric model with the Gridpoint Statistical Interpolation (GSI) Analysis, a joint analysis system developed by the NOAA/National Centers for Environmental Prediction and the NASA/Global Modeling and Assimilation Office. The primary performance drivers for the GEOS DAS are temperature and moisture fields suitable for the EOS instrument teams, wind fields for the transport studies of the stratospheric and tropospheric chemistry communities, and climate-quality analyses to support studies of the hydrological cycle through MERRA. The GEOS-5 atmospheric model has been approved for open source release and is available from: http://opensource.gsfc.nasa.gov/projects/GEOS-5/GEOS-5.php

Tundra Snow Cover Properties from \u3cem\u3eIn-Situ\u3c/em\u3e Observation and Multi-Scale Passive Microwave Remote Sensing

Tundra snow cover is important to monitor as it influences local, regional, and global scale surface water balance, energy fluxes, and ecosystem and permafrost dynamics. Moreover, recent global circulation models (GCM) predict a pronounced shift in high latitude winter precipitation and mean annual air temperature due to the feedback between air temperature and snow extent. At regional and hemispheric scales, the estimation of snow extent, snow depth and, snow water equivalent (SWE) is important because high latitude snow cover both forces and reacts to atmospheric circulation patterns. Moreover, snow cover has implications on soil moisture dynamics, the depth, formation and growth of the permafrost active layer, the vegetation seasonality, and the respiration of C02. In Canada, daily snow depth observations are available from 1955 to present for most meteorological stations. Moreover, despite the abundance and dominance of a northern snow cover, most, if not all, long term snow monitoring sites are located south of 550N. Stations in high latitudes are extremely sparse and coastally biased. In Arctic regions, it can be logistically difficult and very expensive to acquire both spatially and temporally extensive in-situ snow data. Thus, the possibility of using satellite remote sensing to estimate snow cover properties is appealing for research in remote northern regions. Remote sensing techniques have been employed to monitor the snow since the 1960s when the visible light channels were used to map snow extent. Since then, satellite remote sensing has expanded to provide information on snow extent, depth, wetness, and SWE. However, the utility of satellite sensors to provide useful, operational tundra snow cover data depends on sensor parameters and data resolution. Passive microwave data are the only currently operational sources for providing estimates of dry snow extent, SWE and snow depth. Currently, no operational passive microwave algorithms exist for the spatially expansive tundra and high Arctic regions. The heterogeneity of sub-satellite grid tundra snow and terrain are the main limiting factors in using conventional SWE retrieval algorithm techniques. Moreover, there is a lack of in-situ data for algorithm development and testing. The overall objective of this research is to improve operational capabilities for estimating end of winter, pre-melt tundra SWE in a representative tundra study area using satellite passive microwave data. The study area for the project is located in the Daring-Exeter-Yamba portion of the Upper-Coppermine River Basin in the Northwest Territories. The size, orientation and boundaries of the study area were defined based on the satellite EASE grid (25 x 25 km) centroid located closest to the Tundra Ecosystem Research Station operated by the Government of the Northwest Territories. Data were collected during intensive late winter field campaigns in 2004, 2005, 2006, 2007, 2008, and 2009. During each field campaign, snow depth, density and stratigraphy were recorded at sites throughout the study area. During the 2005 and 2008 seasons, multi-scale airborne passive microwave radiometer data were also acquired. During the 2007 season, ground based passive microwave radiometer data were acquired. For each year, temporally coincident AMSR-E satellite Tb were obtained. The spatial distribution of snow depth, density and SWE in the study area is controlled by the interaction of blowing snow with terrain and land cover. Despite the spatial heterogeneity of snow cover, several inter-annual consistencies were identified. Tundra snow density is consistent when considered on a site-by-site basis and among different terrain types. A regional average density of 0.294 g/cm3 was derived from the six years of measurements. When applied to site snow depths, there is little difference in SWE derived from either the site or the regional average density. SWE is more variable from site to site and year to year than density which requires the use of a terrain based Classification to better quantify regional SWE. The variability in SWE was least on lakes and flat tundra, while greater on slopes and plateaus. Despite the variability, the interannual ratios of SWE among different terrain types does not change that much. The variability (CV) in among terrain categories was quite similar. The overall weighted mean CV for the study area was 0.40, which is a useful regional generalization. The terrain and landscape based classification scheme was used to generalize and extrapolate tundra SWE. Deriving a weighted mean SWE based on the spatial proportion of landscape and terrain features was shown as a method for generalizing the regional distribution of tundra SWE. The SWE data from each year were compared to AMSR-E satellite Tb. Within each season and among each of the seasons, there was little difference in 19 GHz Tb. However, there was always a large decrease in 37 GHz Tb from early November through April. The change in ΔTb37-19 throughout each season showed that the Tb at 37 GHz is sensitive to parameters which evolve over a winter season. A principal component analysis (PCA) showed that there are differences in ΔTb37-19 among different EASE grids and that land cover may have an influence on regional Tb. However, the PCA showed little relationship between end of season ΔTb37-19 and lake fraction. A good relationship was found between ΔTb37-19 and in-situ SWE. A quadratic function was fitted to explain 89 percent of the variance in SWE from the ΔTb37-19. The quadratic relationship provides a good fit between the data; however, the nature of the relationship is opposite to the expected linear relationship between ΔTb37-19 and SWE. Airborne Tb data were used to examine how different snow, land cover and terrain properties influence microwave emission. In flat tundra, there was a significant relationship between SWE and high resolution ΔTb37-19. On lakes and slopes, no strong relationships were found between SWE and high resolution ΔTb37-19. Due to the complexity of snow and terrain in high resolution footprints, it was a challenge to isolate a relationship between SWE and Tb. However, as the airborne footprint size increased the amplitude of variability in Tb decrease considerably to the point that Tb in large footprints is not sensitive to local scale variability in SWE. As such, most of the variability evident in the high and mid resolution airborne data will not persist at the EASE grid scale. Despite the many challenges, algorithm development should be possible at the satellite scale. The AMSR-E ΔTb37-19 changes from year to year in response to differences in snow cover properties. However, the multiple years of in-situ snow data remain the most important contribution in linking Tb with SWE

GLEAM v3 : satellite-based land evaporation and root-zone soil moisture

The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised, aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 36-year data set spanning 1980-2015, referred to as v3a (based on satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multi-source precipitation product), and two satellite-based data sets. The latter share most of their forcing, except for the vegetation optical depth and soil moisture, which are based on observations from different passive and active C-and L-band microwave sensors (European Space Agency Climate Change Initiative, ESA CCI) for the v3b data set (spanning 2003-2015) and observations from the Soil Moisture and Ocean Salinity (SMOS) satellite in the v3c data set (spanning 2011-2015). Here, these three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in the case of the v3a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the v3b and c data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar to the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements ranging between 0.78 and 0.81 for the different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at www.GLEAM.eu and may be used for large-scale hydrological applications, climate studies, or research on land-atmosphere feedbacks

The role of snow in soil thermal dynamics of the arctic terrestrial ecosystems

The vast area of permanent or seasonal snow cover is an essential component of terrestrial ecosystems in northern mid-to-high latitudes (45-90°N), which has insulation effects on the soil layer beneath it. The affected soil thermal regimes will impact soil carbon dynamics. Recent observations indicate that there are substantial changes in both snow cover extent and duration due to climate change in the area. It is important to understand the insulation effect historically so as to better quantify its role in affecting ecosystem carbon dynamics under changing climate in the future. This study incorporates the snow insulation effect by introducing a snow model into an existing soil thermal model in a biogeochemistry modeling framework, the Terrestrial Ecosystem Model (TEM). The coupled model is used to evaluate the effects of snow dynamics on thermal regimes in the pan-Arctic for the period 2003-2010. Available satellite snow-cover data and site-level data are used to calibrate and evaluate the modeling system for the historical period. The study demonstrates that the revised model reproduces the top-soil layers’ thermal regime and freeze/thaw status reasonably well for the region. The study finds that the insulation effect of snow can alter soil thermal regime. The soil temperature estimations at 5cm and 20cm depths using the satellite snow data are in general 5℃ warmer in winters compared to those using the previous version of the model. There is a lag of soil cooling rate in early winter and a lag of soil warming rate in late spring. The study also finds that the insulation effect of snow can influence ground freeze/thaw status. The frozen line estimated by the revised model moves slightly southward in late spring and slightly northward in early winter. This study suggests that future analysis of soil thermal and carbon dynamics should take snow dynamics into account for the region

Analysis of information systems for hydropower operations

The operations of hydropower systems were analyzed with emphasis on water resource management, to determine how aerospace derived information system technologies can increase energy output. Better utilization of water resources was sought through improved reservoir inflow forecasting based on use of hydrometeorologic information systems with new or improved sensors, satellite data relay systems, and use of advanced scheduling techniques for water release. Specific mechanisms for increased energy output were determined, principally the use of more timely and accurate short term (0-7 days) inflow information to reduce spillage caused by unanticipated dynamic high inflow events. The hydrometeorologic models used in predicting inflows were examined to determine the sensitivity of inflow prediction accuracy to the many variables employed in the models, and the results used to establish information system requirements. Sensor and data handling system capabilities were reviewed and compared to the requirements, and an improved information system concept outlined

The influence of winter time boreal forest tree transmissivity on tree emission and passive microwave snow observations

Forest cover significantly attenuates natural upwelling ground microwave emission from seasonal terrestrial snow. This presents a major challenge for the accurate retrieval of snow from airborne or spaceborne passive microwave (PM) observations. Forest transmissivity is a key parameter describing tree emission because not only does it influence the proportion of sub-canopy upwelling microwave emission penetrating through the forest canopy, it also controls the forest thermal emission. Hence, it is a very important parameter for correcting the influence of forests on spaceborne or airborne observations of the Earth’s land surface. Under sub-zero temperatures, vegetation water content can be frozen influencing the microwave transmissivity of trees. Yet this phenomenon has not been verified through experimentation leaving significant uncertainty in tree emission modelling and spaceborne microwave observations. Therefore, a season-long experiment was designed to study this phenomenon. Ground-based radiometer observations of tree emission, spaceborne observations of forest emission, and model simulations of canopy emission were conducted during this experiment. Based on this experiment, the influence of physical temperature on tree transmissivity was verified, and a model developed to quantitatively describe this temperature-transmissivity relationship. An evaluation of this temperature-transmissivity relationship was conducted showing that both ground-based and spaceborne observations of tree emission are significantly influenced by this phenomenon. Furthermore, passive microwave spaceborne snow retrievals in forested regions are influenced by this phenomenon. Finally, an approach to reduce the influence of the temperature-transmissivity relationship on passive microwave spaceborne snow retrievals is demonstrated