Bayesian Sea Ice Detection With the ERS Scatterometer and Sea Ice Backscatter Model at C-Band

This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types

Explaining anomalies in SAR and scatterometer soil moisture retrievals from dry soils with sub-surface scattering

This paper presents the results of a laboratory investigation to explain anomalously-high soil moisture estimates observed in retrievals from SAR and scatterometer backscatter, affecting extensive areas of the world associated with arid climates. High resolution C-band tomographic profiling was applied in experiments to understand the mechanisms underlying these anomalous retrievals. The imagery captured unique high-resolution profiles of the variations in the vertical backscattering patterns though a sandy soil with moisture change. The relative strengths of the surface and sub-surface returns were dependent upon both soil moisture and soil structure, incidence-angle, and polarization. Co-polarised returns could be dominated by both surface and sub-surface returns at times, whereas cross-polarised returns were strongly associated with sub-surface features. The work confirms suspicions that anomalous moisture estimates can arise from the presence of sub-surface features. Diversity in polarization and incidence angle may provide sufficient diagnostics to flag and correct these erroneous estimates, allowing their incorporation into global soil moisture productsMorriso

Inter-comparison and evaluation of Arctic sea ice type products

oai:publications.copernicus.org:tc102910Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA – National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×106 to 0.49×106 km2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002–2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of -0.001×106 km2 in first-year ice (FYI) extent and -0.02×106 km2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007–2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscatter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.</p

Master of Science

thesisRecent accelerated mass loss offset by increased Arctic precipitation highlights the importance of a comprehensive understanding of the mechanisms controlling mass balance on the Greenland ice sheet. Knowledge of the spatiotemporal variability of snow accumulation is critical to accurately quantify mass balance, yet, considerable uncertainty remains in current snow accumulation estimates. Previous studies have shown the potential for large-scale retrievals of snow accumulation rates in regions that experience seasonal melt-refreeze metamorphosis using active microwave remote sensing. Theoretical backscatter models used in these studies to validate the hypothesis that observed decreasing freezing season backscatter signatures are linked to snow accumulation rates suggest the relationship is inverse and linear (dB). The net backscatter measurement is dominated by a Mie scattering response from the underlying ice-facie. Two-way attenuation resulting from a Raleigh scattering response within the overlying layer of snow accumulation forces a decrease in the backscatter measurement over time with increased snow accumulation rates. Backscatter measurements acquired from NASA's Ku-band SeaWinds scatterometer on the QuikSCAT satellite together with spatially calibrated snow accumulation rates acquired from the Polar MM5 mesoscale climate model are used to evaluate this relationship. Regions that experienced seasonal melt-refreeze metamorphosis and potentially formed dominant scattering layers are delineated, iv freeze-up and melt-onset dates identifying the freezing season are detected on a pixel-by-pixel basis, freezing season backscatter time series are linearly regressed, and a microwave snow accumulation metric is retrieved. A simple empirical relationship between the retrieved microwave snow accumulation metric (dB), , and spatially calibrated Polar MM5 snow accumulation rates (m w. e.), , is derived with a negative correlation coefficient of R=-.82 and a least squares linear fit equation of . Results indicate that an inverse relationship exists between decreasing freezing season backscatter decreases and snow accumulation rates; however, this technique fails to retrieve accurate snow accumulation estimates. An alternate geometric relationship is suggested between decreasing freezing season backscatter signatures, snow accumulation rates, and snowpack stratigraphy in the underlying ice-facie, which significantly influences the microwave scattering mechanism. To understand this complex relationship, additional research is required

Mapping Snowmelt Progression in the Upper Indus Basin with Synthetic Aperture Radar

The Indus River is a vital resource for food security, ecosystem services, hydropower, and economy for millions of people living in Pakistan, India, China, and Afghanistan. Glacier and snowmelt from the high altitude Himalaya, Karakoram, and Hindu Kush mountain ranges are the largest drivers of discharge in the upper Indus Basin (UIB), and contribute significantly to Indus flows. Complex climatology and topography, coupled with the challenges of field study and meteorological measurement in these rugged ranges, elicit notable uncertainties in predicting seasonal runoff as well as cryospheric response to changes in climate. Here we utilize Sentinel-1 synthetic aperture radar (SAR) imagery to track ablation season development of wet snow in the Shigar Watershed of the Karakoram Mountains in Pakistan from 2015 to 2018. We exploit opportune local image acquisition times to highlight diurnal differences in radar indications of wet snow, and examine the spatial and temporal contexts of radar diurnal differences for 2015, 2017, and 2018 ablation seasons. Radar classifications for each ablation season show spatial and temporal patterns that indicate a dry winter snowpack undergoing diurnal surface melt-refreeze cycles, transitioning to surface snow that remains wet both day and night, and finally snow free conditions following melt out. Diurnally differing SAR signals may offer insights into important snowpack energy balance processes that precede melt out, which could provide useful constraints for both glacier mass balance modeling and runoff forecasting in remote alpine watersheds

Recent glacier surface snowpack melt in Novaya Zemlya and Severnaya Zemlya derived from active and passive microwave remote sensing data

The warming rate in the Russian High Arctic (RHA) (36~158˚E, 73~82˚N) is outpacing the pan-Arctic average, and its effect on the small glaciers across this region needs further examination. The temporal variation and spatial distribution of surface melt onset date (MOD) and total melt days (TMD) throughout the Novaya Zemlya (NovZ) and Severnaya Zemlya (SevZ) archipelagoes serve as good indicators of ice mass ablation and glacier response to regional climate change in the RHA. However, due to the harsh environment, long-term glaciological observations are limited, necessitating the application of remotely sensed data to study the surface melt dynamics. The high sensitivity to liquid water and the ability to work without solar illumination and penetrate non-precipitating clouds make microwave remote sensing an ideal tool to detect melt in this region. This work extracts resolution-enhanced passive and active microwave data from different periods and retrieves a decadal melt record for NovZ and SevZ. The high correlation among passive and active data sets instills confidence in the results. The mean MOD is June 20th on SevZ and June 10th on NovZ during the period of 1992-2012. The average TMDs are 47 and 67 days on SevZ and NovZ from 1995 to 2011, respectively. NovZ had large interannual variability in the MOD, but its TMD generally increased. SevZ MOD is found to be positively correlated to local June reanalysis air temperature at 850hPa geopotential height and occurs significantly earlier (~0.73 days/year, p-value \u3c 0.01) from 1992 to 2011. SevZ also experienced a longer TMD trend (~0.75 days/year, p-value \u3c 0.05) from 1995 to 2011. Annual mean TMD on both islands are positively correlated with regional summer mean reanalysis air temperature and negatively correlated to local sea ice extent. These strong correlations might suggest that the Russian High Arctic glaciers are vulnerable to the continuously diminishing sea ice extent, the associated air temperature increase and amplifying positive ice-albedo feedback, which are all projected to continue into the future