Towards Multisensor Microwave Remote Sensing of Frost Flowers On

Towards multisensor microwave remote sensing of frost ﬂowers on sea ice

Lars Kaleschke, Georg Heygster Institute of Environmental Physics, P.O. Box 330440, D-28334 Bremen, Germany

Abstract face tension causing high salinities of up to 150 psu [Drinkwater and Crocker, 1988]. Frost flow- It has been hypothesised that frost flowers could ers are extremely effective collectors of blowing be important for tropospheric chemistry and, as snow and quickly become snow-covered. Thus, a source of sea salt aerosol, for the interpreta- layers of saline snow or slush are formed [Ulan- tion of ice core data. Furthermore, frost flow- der and others, 1995]. The frost flower layer is ers can cause severe errors in sea ice products significantly modified within several days [Per- derived from remote sensing data. Up to now ovich and Richter-Menge, 1994]. there have been only few data sets available re- There has been considerable interest in tropo- lating frost flower distribution and variability in spheric bromine chemistry due to its role in the sea ice covered regions. We present a method ozone depletion [McElroy and others, 1999]. Se- for frost flower detection using a combination vere ozone depletion events which take place of active and passive microwave sensors. The in the polar regions during springtime are asso- vertical polarised C band radar backscatter of a ciated with enhanced BrO occurrence [Wagner young sea ice region covered with frost flowers and Platt, 1998]. Furthermore, BrO was recently can be higher than the signal from multi-year proposed to be a key oxidant converting long- ice or from the wind roughened ocean. There- lived gaseous elemental mercury to total particu- fore, the classification result of Synthetic Aper- late phase mercury and/or reactive gaseous mer- ture Radar (SAR) sea ice images can be defective cury. This oxidative transformation process is re- due to the influence of frost flowers. The ambi- garded to greatly enhance the deposition flux of guity of the open water and frost flower backscat- this toxic heavy metal to the fragile polar bio- ter signal was successfully resolved using addi- sphere [Ebinghaus and others, 2002]. tional information from the 85 GHz Special Sen- Satellite observations show that BrO enriched sor Microwave/Imager (SSM/I) channels. Re- air-masses are always situated close to sea ice sults of the method were compared to aircraft [Richter and others, 1998]. The high amount of measurements and observations in the marginal bromine in the boundary layer can be explained ice zone near Svalbard. by auto-catalytic release from sea salt. A het- erogeneous mechanism leads to an exponential increase of reactive gas phase bromine, the so- called bromine explosion, since every Br atom 1 Introduction entering the liquid phase has the potential to release two Br atoms to the gas phase [Honninger¨ Frost flowers, also referred to as salt flowers, are and Platt, 2002]. Frost flowers provide a high rel- a very common sea ice feature in the Arctic and ative surface area similar to fresh snow, i.e. be- the Antarctic during the cold seasons [Perovich tween 0.2 and 2 m2g−1 [Perovich and Richter- and Richter-Menge, 1994]. Frost flowers grow Menge, 1994]. The surface area relative to the on newly-formed sea ice from a layer of super- area covered gives about a factor of 50 to 1000. saturated vapour [Martin and others, 1996]. The Recently, Rankin and others [2002] found bro- thermo-molecular pressure gradient transports mide concentrations in frost flowers around three brine from the ice interior towards the surface times higher than in sea-water. The combination whereit is then drawn onto the crystals due to sur-

1 of high bromide concentrations present in the liq- allow unique assignments to surface types. The uid phase and the large surface area presented for ambiguity of the open water and frost flower exchange highlights the frost flowers as a poten- backscatter signal can be resolved by using ad- tial source for tropospheric bromine. ditional sea ice concentration information de- Rankin and others [2002] suggested that frost rived from the 85 GHz SSM/I channels. The 19 flowers are the source for the majority of sea salt and 37 GHz channels of the SSM/I have been transported inland as aerosol during winter. So commonly used for for sea ice concentration re- far, sodium concentrations in ice cores have been trievals [Comiso and others, 1997]. The ARTIST used to deduce changes in the amount of sea salt Sea Ice (ASI) algorithm is a hybrid algorithm aerosol. The sea salt has been assumed to come combining the model of Svendsen and others from open water only. The larger sea ice extent [1987] for the 85 GHz channels with a weather should cause a decreased entrainment of sea salt filter for the other SSM/I channels [Kaleschke during glacial periods due to the larger distance and others, 2001]. The 85 GHz channels pro- from the open water. In fact the high levels of sea vide higher spatial resolution and sampling rate salt from glacial periods have been attributed to a (12.5 km versus 25 km) than the 19 and 37 more efficient zonal and meridional atmospheric GHz channels. There are about 64 measurements 2 transport [Petit and others, 1999]. This interpre- from the 85 GHz channels (15×13 km resolution) and only 4 independent measurements from tation of high sodium concentrations being due 2 to increased cyclonic activity may be incorrect, the 19 GHz channels (69×43 km resolution) 2 it may rather be an indicator of increased sea ice for each 100×100 km SAR frame. In this pa- and frost flower production. per we present a feature-level data-fusion sea ice Frost flowers change the electromagnetic prop- classification method for ERS-SAR and SSM/I. erties of the newly-formed sea ice surface in The method was evaluated using satellite under- the microwave as well as in the optical region flights during the Arctic Radiation and Turbu- of the electromagnetic spectrum. As the flow- lence Interaction STudy (ARTIST), which was ers protude a few centimetres above the surface conducted in the environment of the Svalbard into the boundary layer, the infrared tempera- archipelago in March and April 1998 [Augstein, ture measured from the satellite is about 4-6◦ K 2000]. colder than that of the same ice cleared of flowers [Martin and others, 1996]. At C-band (λ ≈ 5.6 cm) the brine enriched slush layer under the 2 Method frost flowers dominates the backscatter. The surface of the frost flower crystals (surface rough- At first, the incidence angle dependence ness σ ≈ 0.5 cm) appears rough at Ku-band 0 B = ∂σ (θ)/∂θ (1) (λ ≈ 2.2cm) yielding predominant surface scattering [Drinkwater and Crocker, 1988]. Ulan- of the radar backscatter is estimated. The der and others [1995] observed an increase of 5- backscatter (in dB) can be described approx- 10 dB for ERS-SAR C-band backscatter, with the imately by the linear relation [Ezraty and largest increases occurring for snow-infiltrated Cavaine, 1999], frost flowers. Two different nilas backscatter 0 classes at -17 dB and at -10 dB were found us- σ (θ) = A + Bθ. (2) ing combined ERS-SAR and Landsat data, with the latter class believed to be affected by frost Within the overlap of the ascending and descend- flowers [Steffen and Heinrichs, 1994]. Zabel and ing branch of the orbit each SAR pixel is mea- others [1996] suggested to use the frost flowers sured at two incidence angles. Thus, two co- signal to classify ice of roughly 5-20 cm thick- registered complementary SAR images can be ness. used for the estimation of the slope B and linear It is difficult to analyse the SAR images recorded incidence angle normalisation [Kaleschke and with the European Remote Sensing Satellite Kern, 2002] (ERS-2) since the ambiguities in the mean nor- Then, statistical, textural, and spectral features malised radar cross section σ0 in general do not are derived from ERS-2 SAR images using a sliding calculation window. Fast computational

2 methods have been used for the calculation of Polar and Marine Research (AWI) while operat- the Grey-Level Co-occurrence Matrix (GLCM) ing microwave radiometers, line scanners, radi- which contains the conditional-joint probabili- ation thermometer, and a laser altimeter, as well ties of all pairwise combinations of grey levels as several sensors for measuring meteorological [Clausi and Zhao, 2002]. The normalised exten- parameters. sion difference (NED) derived from the spectral The flight pattern of the Polar 4 aircraft on 12th moments can be used to distinguish between the of March 1998 is shown in Figure 1 by the ice-free and sea ice covered ocean [Schmidt and dashed white line. This data has been plotted Heygster, 1997]. The above procedure requires over the ERS-2 SAR image and the ASI ice con- the presence of swell in the image. However, ice centration as calculated from SSM/I data. The concentrations derived from 85-GHz SSM/I data aircraft measurements and observations (600 to corresponding to the SAR pixels is independent 1600 m flight level) were co-located to the ERS- of the sea state. The gridding and interpolation 2 SAR image pixels using aircraft position data of the ASI sea ice concentration CASI data to the (GPS, tilt, heading) and assuming uniform ice ERS-SAR image-grid has been performed using motion. The minimum air temperature on 11th continuous curvature splines in tension [Smith March 1998 was -40◦C according to the ARTIST ◦ ◦ and Wessel, 1990]. The features fi are scaled to buoy 9372 at 81,8 N, 10,9 E. Such cold tem- the range [0,1] by using the hyperbolic tangent peratures promoted the growth of frost flowers. function Backscatter values as high as those from the ◦ − wind-roughened ocean sea surface (at 80,2 N, 1 fi(x) µfi ◦ ◦ ni = (tanh( ) + 1) (3) 11 E) caused by the frost flowers (at 80,7 N, ◦ 2 σfi 8 E) can also be seen in Figure 1.

10˚ 20˚ with mean µfi and standard deviation σfi com- 0˚ puted only once for all of the data used in order to preserve the class-dependent biases. The result-

90 82˚ ing features ni reside in a N-dimensional unit- 90 ERS−SAR less space. For the results of this paper we used 90 only N = 2 dimensions, the mean backscat- 0 ter σ and the ice concentration CASI . More 90 features calculated from the SAR-image or from 90 81˚ other sensors can be added easily using the same 90 90 formalism. 90 Frost Flowers 90 Kohonen’s Learning Vector Quantization (LVQ), a supervised neural network, was used for clas- 90 SSM/I 80˚

sification [Kohonen, 1997]. LVQ approximates 90 60 Ice Concentration 60 the Bayesian decision boundaries by a set of op- 30 30 timally placed reference vectors without a-priori assumptions about the probability density func- Open Water SVALBARD tions. The network is trained by learning from examples. Therefore representative examples of all Figure 1: ASI ice concentration isolines, ERS- occurring classes have to be selected. The classi- SAR images and flight pattern of the Polar 4 on fication result can be evaluated by using test data 12th March 1998. which was not used for the training.

3 Data set 4 Results

During ARTIST several satellite under-ﬂights An average incidence angle dependence of B = were carried out with the research aircraft Po- −0.3 0.1 dB/deg was estimated for sea ice in lar 2 and 4 of the Alfred Wegener Institute for a compacted marginal ice zone [Kaleschke and

3 Kern, 2002]. The value B = 0.3 was used for a covered with frost flowers was about -7 to -2 dB. linear incidence angle normalisation. The frost flower covered area was about 15×15 2 The SAR image was annotated along the flight km with a mean backscatter of -5 dB. track with the surveillance logs. Thus, the visual interpretation of the SAR image has been greatly eased. About 50 homogeneous areas of five 5 Conclusion different surface types were manually selected in the SAR image. Open water (OW), first- The backscatter value of newly-formed ice can (FY) and multi-year ice (MY), newly-formed be as high as that of wind roughened open water ice (NI), as well as newly-formed ice with frost due to the influence of frost flowers. It is pos- flowers (FR) were discriminated. The latter sible to separate the frost flower and open water newly-formed ice types have been described as signature by the simultaneous analysis of SSM/I dark/light nilas and grey ice in the surveillance and SAR data. However, there are large remain- logs. ing errors due to the confusion of frost flowers 0 The mean backscatter value σ was calculated with other ice types. Therefore to resolve these using a sliding calculation window of 31×31 errors other features have to be considered for pixel size. The sea ice concentration was cal- an advanced classification. Such features will be culated using the ASI algorithm [Kaleschke and evaluated in an ongoing project. others, 2001]. The selected areas were used for training and testing of the neural network, respectively. The number of reference vectors for the LVQ algorithm was 500. Acknowledgements The overall classification accuracy of the train- The authors would like to thank the two review- ing and the independent test data was 95,7% and ers for making useful suggestions. This research 81,5%, respectively. The confusion matrix is was funded by the German Research Founda- given in Table 1. 2717 randomly selected OW tion (DFG) under contract HE-1746/5-2 and HE- test data were classified correctly as OW (2662) 1746/11-1. The ARTIST project was funded and incorrectly as NI (51) and FY(34). The re- by the Commission of the European Commu- sulting classification error for OW is about 3%. nity under contract Nr. ENV4-CT97-0497-0487 The classification errors of NI and FY are about (DG12-ESCY). 20% to 30%. The worst result was achieved for FR which was confused with MY (about 50% to 100% error). However, there would have been larger errors without the use of ice concentra- References tion data. Without such data there would have been a confusion between FR and OW as well. Augstein, E. Final Report On The Arctic This example shows, that the combined analysis Radiation and Turbulence Interaction Study of SSM/I and SAR data can improve the classi- (ARTIST) Contract Nr. ENV4-CT97-0497- fication accuracy considerably. The normalised 0487 (DG12-ESCY). Alfred-Wegener-Institut fur¨ Polar- und Meeresforschung (AWI) Bre- merhaven, 2000. Table 1: Confusion matrix of 10000 test data. Clausi, D. A. and Y. Zhao. Rapid extraction of OW NI FY MY FR image texture by co-occurrence using a hy- OW: 2632 51 34 0 0 brid data structure. Computers and Geosci., NI: 72 2443 428 158 31 28(6):763–774, 2002. FY: 0 54 1475 457 28 MY: 0 0 9 1067 531 Comiso, J. C., D. J. Cavalieri, C. L. Parkinson, FR: 2 0 0 0 528 and P. Gloersen. Passive microwave algo- rithms for sea ice concentration : a comparison of two techniques. Remote Sensing Environ., 0 radar cross section σ of the newly-formed ice 60(3):357–384, 1997.

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