Marko Mäkynen

Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice

Incidence angle dependence of three statistical parameters-the mean of the backscattering coefficient (/spl sigma//spl deg/), standard deviation, and autocorrelation coefficient of texture (/spl sigma//sub T/ and /spl rho//sub T/)-of the C-band horizontal-horizontal (HH) polarization backscattering signatures of the Baltic Sea ice are investigated using RADARSAT ScanSAR Narrow images and helicopter-borne Helsinki University of Technology Scatterometer (HUTSCAT) data. The analysis of the large amount of data shows that the relationship between the mean /spl sigma//spl deg/ in decibel scale and the incidence angle in the range from 19/spl deg/ to 46/spl deg/ is usually well described by a linear model. In general, the RADARSAT and HUTSCAT results agree with each other, and they are also supported by theoretical backscattering model calculations; the more deformed the ice, the smaller the slope between /spl sigma//spl deg/ and the incidence angle, and the higher the moisture content of snow or ice, the larger the slope. The derived /spl sigma//spl deg/ incidence angle dependencies can be used to roughly compensate the /spl sigma//spl deg/ incidence angle variation in the SAR images to help their visual and automated classification. The variability of /spl sigma//sub T/ and /spl rho//sub T/ with the increasing incidence angle is insignificant compared to the variability within each ice type. Their average changes with the incidence angle are so small that, in practice, their trends do not need to be compensated. The results of this study can be utilized when developing classification algorithms for the RADARSAT ScanSAR and ENVISAT HH-polarization Wide Swath images of the Baltic Sea ice.

Open water detection from Baltic Sea ice Radarsat-1 SAR imagery

An algorithm for open water and sea ice discrimination for Radarsat-1 ScanSAR images is presented. The algorithm is based on segmentation and local synthetic aperture radar signal intensity autocorrelation. The algorithm performance is evaluated by comparing the results to operational digitized ice charts, in which the sea ice information is based on human interpretation of multiple data sources, including remote sensing data. The algorithm locates the open water of the digitized ice charts with about 90% accuracy.

On the accuracy of thin-ice thickness retrieval using MODIS thermal imagery over Arctic first-year ice

Abstract We have studied the accuracy of ice thickness (hi) retrieval based on night-time MODIS (Moderate Resolution Imaging Spectroradiometer) ice surface temperature (Ts) images and HIRLAM (High Resolution Limited Area Model) weather forcing data from the Arctic. The study area is the Kara Sea and eastern part of the Barents Sea, and the study period spans November-April 2008–11 with 199 hi charts. For cloud masking of the MODIS data we had to use manual methods in order to improve detection of thin clouds and ice fog. The accuracy analysis of the retrieved hi was conducted with different methods, taking into account the inaccuracy of the HIRLAM weather forcing data. Maximum reliable hi under different air-temperature and wind-speed ranges was 35–50 cm under typical weather conditions (air temperature <–20cC, wind speed <5ms–1) present in the MODIS data. The accuracy is best for the 15–30 cm thickness range, ∼38%. The largest hi uncertainty comes from air temperature data. Our ice-thickness limits are more conservative than those in previous studies where numerical weather prediction model data were not used in the hi retrieval. Our study gives new detailed insight into the capability of Ts-based hi retrieval in the Arctic marginal seas during freeze-up and wintertime, and should also benefit work where MODIS hi charts are used.

On the Estimation of Melt Pond Fraction on the Arctic Sea Ice With ENVISAT WSM Images

The accuracy of microwave radiometer ice concentration (IC) retrievals in the Arctic is degraded by melt ponds on sea ice during the melting season. For the development of IC retrieval algorithms and for the quantification of their uncertainties, data sets on the area fraction of melt ponds (f_mp) are needed. f_mp retrieval with optical satellite data is limited by clouds. Thus, we have studied f_mp retrieval with ENVISAT wide swath mode (WSM) synthetic aperture radar (SAR) images which have large daily coverage over the Arctic Sea ice in 2007-2012. The WSM images used here were acquired north of the Fram Strait in June-August 2009. Data on f_mp were available from the Integrated Climate Data Centers daily Moderate Resolution Imaging Spectroradiometer (MODIS) f_mp product in a 12.5-km grid. Relationships between SAR σ° and MODIS f_mp were studied visually by comparing daily SAR mosaics andfmp charts and by analyzing f_mp and σ° time series and spatially and temporally coincident f_mp and σ° data. The correspondence between the changes of f_mp and the σ° statistics is too low to suggest f_mp estimation from the WSM images. In some cases, there was a 2-3-dB σ° increase during the ponding period. It is assumed that the variation of snow and sea ice characteristics diminishes σ° changes due to the melt ponding and drainage. Good correlation between σ° and f_mp has only been observed for smooth landfast first-year ice in previous studies. A very interesting observation was the large temporal σ° variations during the late melting season, which are likely linked to the atmospherically forced freezing-melting events. These events may also influence radiometer IC retrievals.

Measurements and modeling of vertical backscatter distribution in forest canopy

Presents the results of analysis and modeling of the airborne ranging Helsinki University of Technology Scatterometer (HUTSCAT) data obtained over an Austrian pine forest in southern France. The objective is to use high vertical resolution backscatter profiles to validate a model that is subsequently used to determine the scattering sources within a canopy and to understand the wave/tree interaction mechanisms. The backscatter coefficients derived from HUTSCAT measurements at X-band at near-normal incidence and polarizations HH, VV, and VH are analyzed. The tree crown backscatter separated from the ground backscattering shows a sensitivity of about 3 dB between 0 and 200 m/sup 3//ha. The estimation of tree height using HUTSCAT profiles gives very good results, with a mean precision of 1 m. The vertical backscatter profiles are compared with the output from the MIT/CESBIO radiative transfer (RT) model coupled with a tree growth architectural model, AMAP, which recreates tree architecture using botanical bases. An a posteriori modification to the RT model is introduced, taking into account the vertical and horizontal variability of the scattering area in order to correctly estimate the backscatter attenuation. The results show good agreement between both simulated and HUTSCAT-derived vertical backscatter distribution within the canopy. The penetration depth at near normal incidence is studied. Both simulated and experimental penetration depth are compared and appear to be of several meters, varying with the stands age.

Modelling snow and ice thickness in the coastal Kara Sea, Russian Arctic

Abstract Snow and ice thickness in the coastal Kara Sea, Russian Arctic, were investigated by applying the thermodynamic sea-ice model HIGHTSI. The external forcing was based on two numerical weather prediction (NWP) models: the High Resolution Limited Area Model (HIRLAM) and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. A number of model experiments were carried out applying different snow parameterization schemes. The modelled ice thickness was compared with in situ measurements and the modelled snow thickness was compared with the NASA Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow thickness. The HIRLAM and ECMWF model results agreed with each other on air temperature and wind. The NWP model precipitation forecasts caught up the synoptic-scale snowfall events, but the magnitude was liable to errors. The ice growth was modelled reasonably well applying HIGHTSI either with a simple parameterization for snow thickness or with the HIRLAM or ECMWF model precipitation as input. For the latter, however, an adjustment of snow accumulation in early winter was necessary to avoid excessive accumulation and consequent underestimation of ice thickness. Applying effective snow heat conductivity improved the modelled ice thickness. The HIGHTSI-modelled snow thickness had a seasonal evolution similar to that of the AMSR-E snow thickness. New field data are urgently needed to validate NWP and ice models and remote-sensing products for snow and sea ice in the Kara Sea.

Thin Ice Detection in the Barents and Kara Seas With AMSR-E and SSMIS Radiometer Data

We have studied thin ice detection using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager/Sounder radiometer data acquired over the Barents and Kara Seas during three winters (November-April) in 2008-2011. Moderate Resolution Imaging Spectroradiometer-based ice thickness charts were used as reference data. Thin ice detection was studied using polarization and spectral gradient ratios (PR and GR) calculated from the 36/37 and 89/91 GHz radiometer data. Thresholds for thin ice detection and maximum thicknesses for the detected thin ice (hT) were determined, as were error rates for misdetections. The results for different 1-D PR and GR parameters led to the conclusion that the AMSR-E PR36 and H-polarized GR8936 would be the best parameters for a 2-D classifier. We adopted the linear discrimination analysis (LDA) as a statistical tool. Thin ice areas with hT of 30 cm could be separated from thicker ice fields with approximately 20% error level. In our large data set, the estimation of thin ice thickness was not possible with reasonable accuracy due to the large scatter between ice thickness and the PR and GR signatures. This is likely due to a large data set, besides thin ice in polynyas also thin ice in the marginal ice zone and thin ice from freeze-up period. The optimal LDA parameters in the classifier and hT depended on the daily mean air temperature ((T am )). We could not yet parameterize the classifier optimally according to (T am ), but the constructed classifier worked rather robustly as indicated by the relative small error rate variation between the three analyzed winters.

Comparisons Between SAR Backscattering Coefficient and Results of a Thermodynamic Snow/Ice Model for the Baltic Sea Land-Fast Sea Ice

We have compared the time series of C-band HH-polarization backscattering coefficients (sigmadeg) of the Baltic Sea land-fast level ice with results from a 1-D high-resolution thermodynamic snow/ice model (HIGHTSI). The sigmadeg time series were obtained from ENVISAT synthetic aperture radar (SAR) images. The study period was from the middle of the winter to the early melt season, February 3-April 7, 2004. Due to the large incidence angle range of the SAR images, the sigmadeg values were divided into three subseries. In general, the HIGHTSI results greatly helped to interpret the sigmadeg behavior with changing ice and weather conditions. The modeled snow-surface temperature, cases of snow melting, and evolution of snow and ice thickness were related to the changes in sigmadeg. Equally useful information could not be obtained solely on the basis of large-scale atmospheric models. Realistic forcing data for HIGHTSI were available in the form of coastal-weather observations and model results of the European Centre of Medium-Range Weather Forecasts (ECMWF). The latter make it possible to apply HIGHTSI in the interpretation of SAR data from all ice-covered seas. There were some cases where detailed ground truth, combined with theoretical sigmadeg modeling, would have been needed for interpretation of the sigmadeg trends. A very interesting observation was the large variation of level ice sigmadeg with changing weather conditions, which complicates automatic classification of the SAR images, and thus, the algorithms must be tuned for different ice conditions. The HIGHTSI model could act as an indicator of various ice conditions for algorithm development

Passive microwave signature observations of the Baltic Sea ice

Passive microwave signatures of various Baltic Sea ice types and open water leads were measured in the spring of 1995 and in March 1997 with airborne non‐imaging microwave radiometers (MWR) operating in the frequency range from 6.8 to 36.5 GHz. The MWR datasets were assigned by video imagery into open water leads and various ice type categories. The ground data provided further classification into dry, moist and wet snow sub‐categories. The datasets were used to study the behaviour of the brightness temperature and polarization ratio as a function of frequency and the degree of ice deformation; additionally, the dimensionality of multichannel datasets, classification of surface types, and suitability of the SSM/I and AMSR‐E data and NASA Team and Bootstrap ice concentration algorithms for the mapping of the Baltic Sea ice were examined. The results indicate that open water leads can be distinguished from sea ice regardless of the snow cover wetness, using even single‐channel MWR data. Classification of ice types is possible only under dry snow condition. Determination of the ice type concentrations from the coarse‐resolution space‐borne MWR data is not feasible, because the mean signatures for various ice types are very close to each other. The results also suggest that the SSM/I and AMSR‐E data and the NASA Team and Bootstrap algorithms can be used to map total ice concentration after modifications of open water and sea ice reference signatures.

Calibration accuracy of the HUTSCAT airborne scatterometer

This communications presents a statistical evaluation of the, calibration accuracy of an airborne scatterometer system. The internal and external calibrations were conducted over a two-year time period. It is shown that the absolute calibration accuracy of the Helsinki University of Technology airborne scatterometer (HUTSCAT) is better than 0.6 dB with 90% confidence. The techniques are generally applicable to stable airborne remote-sensing radars.