scholarly journals Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval

2017 ◽  
Vol 11 (5) ◽  
pp. 2059-2073 ◽  
Author(s):  
Kevin Guerreiro ◽  
Sara Fleury ◽  
Elena Zakharova ◽  
Alexei Kouraev ◽  
Frédérique Rémy ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Flight Period ◽  
Data Sets ◽  
Data Set ◽  
Arctic Sea ◽  
The Common ◽  
Ice Floes ◽  
Along Track

Abstract. Over the past decade, sea-ice freeboard has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. While recent studies have demonstrated the capacity of the CryoSat-2 mission (2010–present) to provide accurate freeboard measurements, the current estimates obtained with the Envisat mission (2002–2012) still require some large improvements. In this study, we first estimate Envisat and CryoSat-2 radar freeboard by using the exact same processing algorithms. We then analyse the freeboard difference between the two estimates over the common winter periods (November 2010–April 2011 and November 2011–March 2012). The analysis of along-track data and gridded radar freeboard in conjunction with Envisat pulse-peakiness (PP) maps suggests that the discrepancy between the two sensors is related to the surface properties of sea-ice floes and to the use of a threshold retracker. Based on the relation between the Envisat pulse peakiness and the radar freeboard difference between Envisat and CryoSat-2, we produce a monthly CryoSat-2-like version of Envisat freeboard. The improved Envisat data set freeboard displays a similar spatial distribution to CryoSat-2 (RMSD  =  1.5 cm) during the two ice growth seasons and for all months of the period of study. The comparison of the altimetric data sets with in situ ice draught measurements during the common flight period shows that the improved Envisat data set (RMSE  =  12–28 cm) is as accurate as CryoSat-2 (RMSE  =  15–21 cm) and much more accurate than the uncorrected Envisat data set (RMSE  =  178–179 cm). The comparison of the improved Envisat radar freeboard data set is then extended to the rest of the Envisat mission to demonstrate the validity of PP correction from the calibration period. The good agreement between the improved Envisat data set and the in situ ice draught data set (RMSE  =  13–32 cm) demonstrates the potential of the PP correction to produce accurate freeboard estimates over the entire Envisat mission lifetime.

In-situ measured primary productivity of ice algae in Arctic sea ice floes using a new incubation method

2016 ◽  
Vol 51 (3) ◽  
pp. 387-396 ◽  
Author(s):  
Ho Jung Song ◽  
Jae Hyung Lee ◽  
Gawn Woo Kim ◽  
So Hyun Ahn ◽  
Houng-Min Joo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Primary Productivity ◽  
Arctic Sea Ice ◽  
Ice Algae ◽  
Arctic Sea ◽  
Ice Floes ◽  
Incubation Method

scholarly journals Comparison of CryoSat-2 and ENVISAT freeboard height over Arctic sea ice: Toward an improved Envisat freeboard height retrieval

2017 ◽  
Author(s):  
Kevin Guerreiro ◽  
Sara Fleury ◽  
Elena Zakharova ◽  
Alexei Kouraev ◽  
Frédérique Rémy ◽  
...  
Keyword(s):  
Sea Ice ◽  
Growth Cycle ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Flight Period ◽  
Height Difference ◽  
Ice Roughness ◽  
The Common ◽  
Thickness Variations

Abstract. During the past decade, sea ice freeboard height has been monitored with various satellite altimetric missions with the aim of producing long-term time series of ice thickness. To achieve this goal, it is essential to analyze potential inter-mission biases and to produce freeboard height datasets as free of instrumental error as possible. In the present study, we compare Envisat and CryoSat-2 freeboard height during the common flight period (2010–2012). Our results show that Envisat freeboard height is always thinner (−14 cm in average) when compared to CryoSat-2 (3 cm in average). In addition, Envisat freeboard height displays an unrealistive negative growth from November to April (−2.4 to −3.7 cm) while CryoSat-2 dispalys a positive and coherent winter growth (2.4 to 2.7 cm). The discrepancy between the two datasets is found to be related to a dissimilar impact of ice roughness and snow volume scattering on SAR (CryoSat-2) and pulse-limited (Envisat) altimetry. Following this result, we show that the freeboard height difference between the two datasets can be expressed as a function of the waveform pulse-peakiness. Based on the relation between the Envisat pulse-peakiness and the freeboard height difference, we produce a monthly CryoSat-2-like version of Envisat freeboard height that reduces the average RMSD with CryoSat-2 from ~ 16 cm to ~ 2 cm and improves the freeboard height growth cycle (2–3 cm). The comparison of the altimetric datasets with in situ ice draft measurements during the common flight period shows that the corrected Envisat dataset (RMSE = 16–29 cm) is as accurate as CryoSat-2 (RMSE = 13–25 cm) and highly more accurate than the uncorrected Envisat dataset (RMSE = 108–132 cm). The comparison of the improved Envisat freeboard height dataset is then extended to the rest of the Envisat mission to demonstrate the validity of the improved Envisat dataset out of the calibration period. As a result, we find a good agreement between the Envisat and the in situ ice draft datasets (RMSE = 14–30 cm), which demonstrates the potential of the pulse-peakiness-correction to produce accurate freeboard height estimates over the entire Envisat mission. Finally, we show the averaged-circumpolar ice thickness variations from 2002 to 2015 by combining CryoSat-2 and Envisat datasets.

Arctic sea ice albedo: A comparison of two satellite-derived data sets

1993 ◽  
Vol 20 (1) ◽  
pp. 41-44 ◽  
Author(s):  
Axel J. Schweiger ◽  
Mark C. Serreze ◽  
Jeffrey R. Key
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Data Sets ◽  
Arctic Sea ◽  
Derived Data

scholarly journals Characterizing Arctic sea ice topography using high-resolution IceBridge data

2016 ◽  
Vol 10 (3) ◽  
pp. 1161-1179 ◽  
Author(s):  
Alek A. Petty ◽  
Michel C. Tsamados ◽  
Nathan T. Kurtz ◽  
Sinead L. Farrell ◽  
Thomas Newman ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Feature ◽  
Ice Thickness ◽  
First Year ◽  
Data Set ◽  
Surface Features ◽  
Developed Surface ◽  
Arctic Sea

Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes. The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.

Dissolved Neodymium Isotopes Trace Origin and Spatiotemporal Evolution of Modern Arctic Sea Ice

2020 ◽  
Author(s):  
Georgi Laukert ◽  
Dorothea Bauch ◽  
Ilka Peeken ◽  
Thomas Krumpen ◽  
Kirstin Werner ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Surface Waters ◽  
Ice Cores ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Spatiotemporal Evolution ◽  
Arctic Sea ◽  
The Arctic Ocean ◽  
Ice Floes

<p>The lifetime and thickness of Arctic sea ice have markedly decreased in the recent past. This affects Arctic marine ecosystems and the biological pump, given that sea ice acts as platform and transport medium of marine and atmospheric nutrients. At the same time sea ice reduces light penetration to the Arctic Ocean and restricts ocean/atmosphere exchange. In order to understand the ongoing changes and their implications, reconstructions of source regions and drift trajectories of Arctic sea ice are imperative. Automated ice tracking approaches based on satellite-derived sea-ice motion products (e.g. ICETrack) currently perform well in dense ice fields, but provide limited information at the ice edge or in poorly ice-covered areas. Radiogenic neodymium (Nd) isotopes (ε<sub>Nd</sub>) have the potential to serve as a chemical tracer of sea-ice provenance and thus may provide information beyond what can be expected from satellite-based assessments. This potential results from pronounced ε<sub>Nd</sub> differences between the distinct marine and riverine sources, which feed the surface waters of the different sea-ice formation regions. We present the first dissolved (< 0.45 µm) Nd isotope and concentration data obtained from optically clean Arctic first- and multi-year sea ice (ice cores) collected from different ice floes across the Fram Strait during the RV POLARSTERN cruise PS85 in 2014. Our data confirm the preservation of the seawater ε<sub>Nd</sub>signatures in sea ice despite low Nd concentrations (on average ~ 6 pmol/kg) resulting from efficient brine rejection. The large range in ε<sub>Nd</sub> signatures (~ -10 to -30) mirrors that of surface waters in various parts of the Arctic Ocean, indicating that differences between ice floes but also between various sections in an individual ice core reflect the origin and evolution of the sea ice over time. Most ice cores have ε<sub>Nd</sub> signatures of around -10, suggesting that the sea ice was formed in well-mixed waters in the central Arctic Ocean and transported directly to the Fram Strait via the Transpolar Drift. Some ice cores, however, also revealed highly unradiogenic signatures (ε<sub>Nd</sub> < ~ -15) in their youngest (bottom) sections, which we attribute to incorporation of meltwater from Greenland into newly grown sea ice layers. Our new approach facilitates the reconstruction of the origin and spatiotemporal evolution of isolated sea-ice floes in the future Arctic.</p>

scholarly journals Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm

2018 ◽  
Author(s):  
Nils Hutter ◽  
Lorenzo Zampieri ◽  
Martin Losch
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Tracking Algorithm ◽  
The Arctic ◽  
Power Law Distribution ◽  
Data Set ◽  
Extensive Evaluation ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift

Abstract. Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This paper introduces two methods that detect and track LKFs in sea ice deformation data and establish an LKF data set for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). Both algorithms are available as open-source code and applicable to any gridded sea-ice drift and deformation data. The LKF detection algorithm classifies pixels with higher deformation rates compared to the immediate environment as LKF pixels, divides the binary LKF map into small segments, and re-connects multiple segments into individual LKFs based their distance and orientation relative to each other. The tracking algorithm uses sea-ice drift information to estimate a first guess of LKF distribution and identifies tracked features by the degree of overlap between detected features and the first guess. An optimization of the parameters of both algorithms is presented, as well as an extensive evaluation of both algorithms against hand-picked features in a reference data set. An LKF data set is derived from RGPS deformation data for the years from 1996 to 2008 that enables a comprehensive description of LKFs. LKF densities and LKF intersection angles derived from this data set agree well with previous estimates. Further, a power-law distribution of LKF length, an exponential distribution of LKF lifetimes, and a strong link to atmospheric drivers, here Arctic cyclones, is derived from the data set. Both algorithms are applied to output of a numerical sea-ice model to compare the LKF intersection angles in a high-resolution Arctic sea-ice simulation with the LKF data set.

scholarly journals A linear model to derive melt pond depth from hyperspectral data

2019 ◽  
Author(s):  
Marcel König ◽  
Natascha Oppelt
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Large Scale ◽  
Hyperspectral Data ◽  
Arctic Sea Ice ◽  
Optical Data ◽  
Melt Pond ◽  
Arctic Sea ◽  
Melt Water

Abstract. Melt ponds are key elements in the energy balance of Arctic sea ice. Observing their temporal evolution is crucial for understanding melt processes and predicting sea ice evolution. Remote sensing is the only technique that enables large-scale observations of Arctic sea ice. However, monitoring vertical melt pond evolution in this way is challenging because most of the optical signal reflected by a pond is defined by the scattering characteristics of the underlying ice. Without knowing the influence of melt water on the reflected signal, the water depth cannot be determined. To solve the problem, we simulated the way melt water changes the reflected spectra of bare ice. We developed a model based on the slope of the log-scaled remote sensing reflectance at 710 nm. We validated the model using 49 in situ melt pond spectra and corresponding depths from ponds on dark and bright ice. Retrieved pond depths are precise (RMSE = 2.81 cm) and highly correlated with in situ measurements (r = 0.89; p = 4.34e−17). The model further explains a large portion of the variation in pond depth (R2 = 0.74). Our results indicate that pond depth is retrievable from optical data under clear sky conditions. This technique is potentially transferrable to hyperspectral remote sensors on UAVs, aircraft and satellites.

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