Passive Microwave Remote Sensing of Ice Cover on Large Northern Lakes: Great Bear Lake and Great Slave Lake, Northwest Territories, Canada

Time series of brightness temperature (TB) measurement obtained at various frequencies by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) are investigated to determine ice phenology parameters and ice thickness on Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada. TB measurements from the 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset (FO)/melt-onset (MO), ice-on/ice-off dates, and ice thickness on both lakes. The sensitivity of TB measurements at 6.9, 10.7, and 18.7 GHz to ice thickness is also examined using a previously validated thermodynamic lake ice model and the most recent version of the Helsinki University of Technology (HUT) model, which accounts for the presence of a lake-ice layer under snow. This study shows that 18.7 GHz H-pol is the most suitable AMSR-E channel for detecting ice phenology events, while 18.7 GHz V-pol is preferred for estimating lake ice thickness on the two large northern lakes. These two channels therefore form the basis of new ice cover retrieval algorithms. The algorithms were applied to map monthly ice thickness products and all ice phenology parameters on GBL and GSL over seven ice seasons (2002-2009). Through application of the algorithms much was learned about the spatio-temporal dynamics of ice formation, decay and growth rate/thickness on the two lakes. Key results reveal that: 1) both FO and ice-on dates occur on average 10 days earlier on GBL than on GSL; 2) the freeze-up process or freeze duration (FO to ice-on) takes a comparable amount of time on both lakes (two to three weeks); 3) MO and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL; 4) the break-up process or melt duration (MO to ice-off) lasts for an equivalent period of time on both lakes (six to eight weeks); 5) ice cover duration is about three to four weeks longer on GBL compared to its more southern counterpart (GSL); and 6) end-of-winter ice thickness (April) on GBL tends to be on average 5-15 cm thicker than on GSL, but with both spatial variations across lakes and differences between years

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

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

Effect of snow microstructure and subnivean water bodies on microwave radiometry of seasonal snow

Remote sensing using microwave radiometry is an acknowledged method for monitoring various environmental processes in the cryosphere, atmosphere, soil, vegetation and oceans. Several decades long time series of spaceborne passive microwave observations can be used to detect trends relating to climate change, while present measurements provide information on the current state of the environment. Unlike optical wavelengths, microwaves are mostly insensitive to atmospheric and lighting conditions and are therefore suitable for monitoring seasonal snow in the Arctic. One of the major challenges in the utilization of spaceborne passive microwave observations for snow measurements is the poor spatial resolution of instruments. The interpretation of measurements over heterogeneous areas requires sophisticated microwave emission models relating the measured parameters to physical properties of snow, vegetation and the subnivean layer. Especially the high contrast in the electrical properties of soil and liquid water introduces inaccuracies in the retrieved parameters close to coastlines, lakes and wetlands, if the subnivean water bodies are not accounted for in the algorithm. The first focus point of this thesis is the modelling of brightness temperature of ice- and snow-covered water bodies and their differences from snow-covered forested and open land areas. Methods for modelling the microwave signatures of water bodies and for using that information in the retrieval of snow parameters from passive microwave measurements are presented in this thesis. The second focus point is the effect of snow microstructure on its microwave signature. Even small changes in the size of scattering particles, snow grains, modify the measured brightness temperature notably. The coupling of different modelled and measured snow microstructural parameters with a microwave snow emission model and the application of those parameters in the retrieval of snow parameters from remote sensing data are studied

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort

An Investigation into the Effects of Variable Lake Ice Properties on Passive and Active Microwave Measurements Over Tundra Lakes Near Inuvik, N.W.T.

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)