Mapping Firn Saturation Over Greenland Using NASA's Soil Moisture Active Passive Satellite

Mapping the spatial extent of recently identified englacial hydrological features (i.e., ice slabs and perennial firn aquifers) formed by meters-thick water-saturated firn layers over the percolation facies of the Greenland Ice Sheet using L -band microwave radiometry has recently been demonstrated. However, these initial maps are binary, and do not provide a parameter to estimate the spatial variability in the thickness and volumetric fraction of meltwater stored within the firn pore space. Here, we exploit enhanced-resolution vertical-polarization L -band brightness temperature ( $T_{V}^{B}$ ) imagery (2015&ndash;2019) generated using observations collected over Greenland by NASA&rsquo;s Soil Moisture Active Passive (SMAP) satellite and a simple two-layer L -band brightness temperature model. We map water-saturated firn layers via a &ldquo;firn saturation&rdquo; parameter, and interpret our results together with ice slab and perennial firn aquifer spatial extents, estimates of snow accumulation simulated via the Regional Atmospheric Climate Model (RACMOp2.3), and airborne radar surveys collected via NASA&rsquo;s Operation IceBridge (OIB) campaigns. We find that variable firn saturation parameter values are mapped in lower snow accumulation ice slab areas in western, northern, and northeastern Greenland, where firn is colder and water-saturated firn layers seasonally refreeze as solid-ice. Higher firn saturation parameter values are mapped in higher snow accumulation perennial firn aquifer areas in southeastern, southern, and northwestern Greenland, where firn is near the melting point, and meters-thick water-saturated firn layers exist. Our results have implications for identifying expansive englacial reservoirs that store significant volumes of meltwater in locations that are vulnerable to meltwater-induced hydrofracturing and accelerated outlet glacier flow year-round.View less &nbsp;</p

Brief communication: Mapping Greenland's perennial firn aquifers using enhanced-resolution L-band brightness temperature image time series

Enhanced-resolution L-band brightness temperature ( TB ) image time series generated from observations collected over the Greenland Ice Sheet by NASA's Soil Moisture Active Passive (SMAP) satellite are used to map Greenland's perennial firn aquifers from space. Exponentially decreasing L-band TB signatures are correlated with perennial firn aquifer areas identified via the Center for Remote Sensing of Ice Sheets (CReSIS) Multi-Channel Coherent Radar Depth Sounder (MCoRDS) that was flown by NASA's Operation IceBridge (OIB) campaign. An empirical algorithm to map extent is developed by fitting these signatures to a set of sigmoidal curves. During the spring of 2016, perennial firn aquifer areas are found to extend over &sim;66 &thinsp;000&thinsp;km 2 .</p

Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave <i>EASE-Grid 2.0</i> Brightness Temperature Earth System Data Record

Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In regions of limited sunlight and cloudy conditions, microwave measurements are particularly valuable for monitoring snow- and ice-covered ocean and land surfaces, due to microwave sensitivity to phase changes of water. Historically available at relatively low resolutions (25 km) compared to optical techniques (less than 1 km), passive-microwave sensors have provided short-timescale, large-area spatial coverage, and high temporal repeat observations for monitoring hemispheric-wide changes. However, historically available gridded passive microwave products have fallen short of modern requirements for climate data records, notably by using inconsistently-calibrated input data, including only limited periods of sensor overlaps, employing image-reconstruction methods that tuned for reduced noise rather than enhanced resolution, and using projection and grid definitions that were not easily interpreted by geolocation software. Using a recently completed Fundamental Climate Data Record of the swath format passive-microwave record that incorporated new, cross-sensor calibrations, we have produced an improved, gridded data record. Defined on the EASE-Grid 2.0 map projections and derived with numerically efficient image-reconstruction techniques, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) increases spatial resolution up to 3.125 km for the highest frequency channels, and satisfies modern Climate Data Record (CDR) requirements as defined by the National Research Council. We describe the best practices and development approaches that we used to ensure algorithmic integrity and to define and satisfy metadata, content and structural requirements for this high-quality, reliable, consistently gridded microwave radiometer climate data record

EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets

Defined in the early 1990s for use with gridded satellite passive microwave data, the Equal-Area Scalable Earth Grid (EASE-Grid) was quickly adopted and used for distribution of a variety of satellite and in situ data sets. Conceptually easy to understand, EASE-Grid suffers from limitations that make it impossible to format in the widely popular GeoTIFF convention without reprojection. Importing EASE-Grid data into standard mapping software packages is nontrivial and error-prone. This article defines a standard for an improved EASE-Grid 2.0 definition, addressing how the changes rectify issues with the original grid definition. Data distributed using the EASE-Grid 2.0 standard will be easier for users to import into standard software packages and will minimize common reprojection errors that users had encountered with the original EASE-Grid definition

The Cold Land Processes Experiment (CLPX-1): Analysis and Modelling of LSOS Data (IOP3 Period)

Microwave brightness temperatures at 18.7,36.5, and 89 GHz collected at the Local-Scale Observation Site (LSOS) of the NASA Cold-Land Processes Field Experiment in February, 2003 (third Intensive Observation Period) were simulated using a Dense Media Radiative Transfer model (DMRT), based on the Quasi Crystalline Approximation with Coherent Potential (QCA-CP). Inputs to the model were averaged from LSOS snow pit measurements, although different averages were used for the lower frequencies vs. the highest one, due to the different penetration depths and to the stratigraphy of the snowpack. Mean snow particle radius was computed as a best-fit parameter. Results show that the model was able to reproduce satisfactorily brightness temperatures measured by the University of Tokyo s Ground Based Microwave Radiometer system (CBMR-7). The values of the best-fit snow particle radii were found to fall within the range of values obtained by averaging the field-measured mean particle sizes for the three classes of Small, Medium and Large grain sizes measured at the LSOS site

Exploring scaling issues by using NASA Cold Land Processes Experiment(CLPX-1, IOP3) radiometric data

The NASA Cold-land Processes Field Experiment-1 (CLPX-1) involved several instruments in order to acquire data at different spatial resolutions. Indeed, one of the main tasks of CLPX-1 was to explore scaling issues associated with microwave remote sensing of snowpacks. To achieve this task, microwave brightness temperatures collected at 18.7, 36.5, and 89 GHz at LSOS test site by means of the University of Tokyo s Ground Based Microwave Radiometer-7 (GBMR-7) were compared with brightness temperatures recorded by the NOAA Polarimetric Scanning Radiometer (PSR/A) and by SSM/I and AMSR-E radiometers. Differences between different scales observations were observed and they may be due to the topography of the terrain and to observed footprints. In the case of satellite and airborne data, indeed, it is necessary to consider the heterogeneity of the terrain and the presence of trees inside the observed scene becomes a very important factor. Also when comparing data acquired only by the two satellites, differences were found. Different acquisition times and footprint positions, together with different calibration and validation procedures, can be responsible for the observed differences