Influence of Land-Cover Category on Brightness ... - IEEE Xplore

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 35, NO. 2, MARCH 1997

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Influence of Land-Cover Category on Brightness Temperature of Snow Lauri Kurvonen and Martti Hallikainen, Fellow, IEEE

Abstract— A helicopter-borne multifrequency radiometer (24, 34, 48 and 94 GHz vertical polarization) was used to investigate the behavior of the brightness temperature of snow in Sodankylä (latitude: 67.41 N, longitude: 26.58 E), Northern Finland. The measurements were carried out during dry snow, wet snow, and snow-free conditions. The angle of incidence was 45 in all measurements. The measurements and the main results are presented. The analysis is focused on the effect of vegetation and land type on the brightness temperature of snow. The main topics of this paper are: a) the general behavior of the brightness temperature of snow for different land types, b) the effect of forest vegetation on the brightness temperature of snow, and c) the capability of the radiometer system to monitor snow extent in forests during the melting period. Index Terms—Brightness temperature of snow, microwave radiometer, remote sensing of snow.

I. INTRODUCTION

A

T HIGH latitudes days are short and the weather is often cloudy in winter. The main advantages of applying microwave radiometry in remote sensing of snow are its independence of lighting and weather conditions and its capability to measure the water equivalent of dry snow. Nevertheless, the independence of weather conditions is reduced at high frequencies (e.g. 90 GHz). Satellite radiometers provide global data, thus the snow situation can be estimated for large areas simultaneously. The water equivalent of snow, the extent of the snow cover, and the on-set of melt are the parameters that can be retrieved by satellite radiometry [1]–[3], [5], [7]. On the global scale, the accuracy has been so far moderate. This is due to three factors. First, each footprint of a satellite microwave radiometer may include several land and vegetation categories. The measured brightness temperature depends on the characteristics and fraction of each category [1], [2]. Second, the measured brightness temperature is a combination of contributions from the ground, snow and vegetation [4]. Third, the brightness temperature is very sensitive, depending on frequency, to the snow grain size and to snow wetness [6]–[8]. The effect of these factors and their interaction must be known if snow parameters are retrieved from measured brightness temperatures. One of the main problems in the application of satellite microwave radiometry for snow measurements has been poor spatial resolution. New satellite radiometers (DMSP SSM/I Manuscript received February 23, 1995; revised April 30, 1996. The authors are with the Helsinki University of Technology, Laboratory of Space Technology, Otakaari 5A, 02150 Espoo, Finland. Publisher Item Identifier S 0196-2892(97)00972-8.

and the near-future ESA MIMR) provide 85/89 GHz vertically and horizontally polarized channels. MIMR has a proposed spatial resolution of 5 km at 89 GHz, while at lower frequencies the spatial resolution is tens of kilometers [9]. The 85/89 GHz channel significantly improves the spatial resolution of spaceborne radiometers. Airborne measurements are helpful when the benefits and limits of this new frequency channel are investigated. From 1991 to 1994, the Laboratory of Space Technology in Helsinki University of Technology conducted an investigation on microwave remote sensing of snow. ERS-1 SAR images, airborne microwave radiometer and scatterometer data were used in the study [10]. In this paper, the airborne radiometer measurements of this project and their analysis are presented. The analysis focuses on the effect of vegetation and land type on the brightness temperature of snow. The aim of the radiometer measurements was to define: a) the general behavior of the brightness temperature of snow for different land types, b) the effect of forest canopies on the brightness temperature of snow, c) the capability of the radiometer system to monitor the snow cover extent. II. INSTRUMENTATION In 1991, a helicopter-borne multifrequency microwave radiometer was constructed in the Laboratory of Space Technology. It was designed for airborne measurements of snow, ice and forest. The radiometer operates at 24, 34, 48, and 94 GHz and its measurement polarization is vertical. The 24, 34, and 48 GHz receivers operate in the Dicke mode, while the 94 GHz receiver operates in the total power mode. The main technical characteristics of the system are presented in Table I. The radiometer system is controlled by a microcomputer which also stores the data. The four receivers and a video camera are placed on a support structure below a helicopter. The angle of incidence can be set from 0 to 45 off nadir. The radiometer is calibrated by measuring a blackbody at two well known temperatures. The blackbody is realized by two absorbers; one of them is cooled to 77 K by liquid nitrogen and the other is at ambient temperature. The targets are measured several times before and after each measurement flight. The relation between the brightness temperature and the output of each receiver is calculated from the mean values of the absorber measurements. A mechanical calibrator is also applied to the calibration of the total power receiver (94 GHz channel). It is an absorber at ambient temperature, which is mechanically moved in front of the antenna beam. The brightness temperature of the mechan-

0196–2892/97$10.00  1997 IEEE

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MAIN CHARACTERISTICS

TABLE I HUT RADIOMETER SYSTEM

OF THE

Fig. 1. Test site Sodankylä and the test lines.

ical calibrator is measured before and after each measurement period during a measurement flight. The additional calibration of the total power receiver (94 GHz) is needed to achieve an accuracy similar to that of the Dicke receivers.

volume. As shown by Table II, some of the land cover types do not occur on the test lines. This is due to small percentage or scattered location of those land cover types. IV. MEASUREMENTS

III. TEST SITE Our test site is located in Sodankylä, Finnish Lapland (latitude: 67.41N, longitude: 26.58E; see Fig. 1). In Sodankylä the monthly mean temperature is below 0 C from October to April; thus, accumulation of permanent snow cover usually starts in October. During winter all precipitation is in the form of snowfall in Sodankylä. Snow covers the ground about 200 days a year in open areas and from 210 to 220 days in forests. Typically, snow cover reaches its maximum depth in midApril and the snow water equivalent (SWE) is then about 170 mm. Snow starts to melt at the end of April and the ground is typically free of snow by the end of May. A total of 19 test lines representing the most common local land type classes were selected within the 40 km 40 km test site for the airborne measurements (see Table II and Fig. 1). The applied classification is based on the areal classification produced by Finnish National Board of Survey. The properties of forest canopies along the test lines were measured in the fall of 1991, including tree species, the mean height, and stem

Six measurement campaigns were conducted during 1991–1993. Information on the measurement campaigns is presented in Table III. The test lines were measured twice with the radiometer system in each measurement campaign, except in the spring of 1993 when three measurements were completed. In all measurements the angle of incidence was 45 Extensive ground truth measurements were carried out in winter and spring campaigns. A. Ground Truth The Finnish Hydrological Office conducted the field measurements in Sodankylä. They measured the snow parameters on the test lines, including the depth, density, wetness, temperature, and permittivity. The weather and snow conditions during the campaigns are summarized below. 1) Snow-Free Conditions: October 22–24, 1991: The first measurement campaign was carried out on Oct. 22–24, 1991. The ground was frozen, but it was free of snow. The radiometer data for snow-free

KURVONEN AND HALLIKAINEN: INFLUENCE OF LAND-COVER CATEGORY ON BRIGHTNESS TEMPERATURE OF SNOW

DISTRIBUTION

OF

TABLE II LAND COVER TYPES IN

ground is used as a reference data set for studies about the effect of snow on the brightness temperature. September 16–19, 1992: The fall campaign of 1992 was carried out earlier than the one in 1991 in order to measure the test lines under wet ground conditions. The air temperature was 9–20 C higher than in October, 1991. 2) Dry Snow Conditions: The winter campaigns were conducted under dry snow conditions on March 3–4, 1992 and on January 20–21, 1993. Unfortunately, the snow water equivalent (SWE), the grain size, and the snow layer structure in the two winter campaigns were practically the same. March 3–4, 1992: SWE varied from 57 to 164 mm along the test lines and the overall average on the test lines was 130 mm. On March 3 the highest day-time air temperature was 2 C on Test Line 1, but the night between March 3 and 4 was cold, recording a 24 C minimum. On March 4, the day-time temperature was 10 C. The snow was dry and its temperature was below 0 C during the whole campaign. The

THE

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SODANKYLA¨ TEST SITE

snow layer had a normal temperature profile: at the top, snow followed the air temperature, but this dependency decreased with increasing snow depth. At the bottom of the snow layer the temperature of the snow was constant and close to 0 C. January 19–21, 1993: SWE varied from 53 to 182 mm along the test lines; the smallest value of the water equivalent was measured on the ice of Lake Orajärvi (Test Line 10). The overall average on the test lines was 140 mm. On Test Line 1 the air temperature was 25 C on January 19 and 10 C on January 20; thus, the snow layer was dry during the campaign. 3) Wet Snow Conditions: The spring campaigns were carried out under wet snow conditions on April 30–May 1, 1992 and May 5–14, 1993. In the spring campaign of 1992 melting had just started and all the test lines were covered by wet snow. The spring campaign of 1993 was carried out later in spring because the aim was to cover the entire melting period. April 30–May 1, 1992: The melting period was about to start during this measurement campaign. SWE along the test

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OVERVIEW

OF THE

TABLE III AIRBORNE CAMPAIGNS IN

lines varied between 50 and 178 mm. The overall average was 130 mm, which was equal to the average in the winter campaign of 1992. The average snow wetness by volume was 3.0%. The average snow density was 0.274 g/cm3 which was 16% higher than in March, 1992. The air temperature was above 0 C during the whole campaign. May 5–14, 1993: The melting had begun and the mean SWE was 78 mm on May 5, when the campaign started. Snow existed in few places at the end of the campaign on May 14.

THE

SODANKYLA¨ TEST SITE

The day-time air temperature was above 0 C during the whole campaign, but the air temperature was below 0 C during a few nights. The night of May 13 was the coldest; then the temperature reached 7 C on Test Line 3. B. Radiometer Measurements In the fall and spring the emissivities of forest canopy, frozen ground, and wet snow were relatively close to each other; therefore, the mean brightness temperatures of the test


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Fig. 2. Mean brightness temperatures for the Sodankylä test lines at 34 GHz in fall (October 22, 1991), winter (March 4, 1992) and spring (May 1, 1992) conditions. See Table II for an explanation of the test lines.

other test lines in the fall and spring measurements. In the fall the river (Test line 5) was still free of ice while some ice floes already existed on the lake (Test line 10). In the spring the river was partially free of ice while the lake was still covered by ice and very wet snow (slush). The brightness temperature differences between the used frequencies were small in fall and spring measurements (see Tables IV and VI), excluding the morning measurement on May 13, 1993 (discussed in Section V-C). The results confirm that at the polarization and frequencies used, wet snow cannot be distinguished from bare ground in a straightforward manner. In the winter the mean brightness temperatures are significantly lower and standard deviations higher that those in the fall and spring, which is caused by dry snow (see Fig. 2 and Tables IV–VI). The brightness temperature of dry snow is relatively low while that of vegetation remains high. In addition, the variations in the snow layer also increase the deviation of the brightness temperature of snow. V. DATA ANALYSIS

Fig. 3. Observed effect of the snow situation on the mean brightness temperature for different land types at 24 GHz. Fall: snow-free and frozen ground, air temperature 3 C. Mid-winter: dry snow, SWE 122–145 mm, air temperature 7 C. Spring: wet snow, SWE 137–169 mm, air temperature 6 C. Results from snow-free conditions (October 22, 1991) are used as a reference.

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lines showed similar variations. As an example, Fig. 2 shows the mean brightness temperatures for the test lines at 34 GHz in the fall, winter and spring (see Table II for the description of the test lines). Only Test Lines 5 and 10 stand out from the

Satellite microwave radiometry of snow is applied to large areas which often consist of various land and vegetation types. Thus, the measured brightness temperature is a combination of brightness temperature contributions from the ground, snow cover, and vegetation. The effect of these factors and their interaction has to be known when geophysical parameters are retrieved from the measured brightness temperatures. The main parameters of interest are the snow water equivalent, the extent of snow cover layer and the on-set of melt [1]–[3], [6]–[8]. A. Brightness Temperature for Different Land Types Tables IV–VI present the mean brightness temperatures and standard deviations with SWE for different land types in Sodankylä. In mid-winter the snow situation was relatively similar along the test lines, SWE was from 120 to 160 mm, excluding Test Lines 5 and 10. In the mid-winter measurements, SWE of the test lines mainly varied due to areal differences in

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TABLE IV OBSERVED MEAN BRIGHTNESS TEMPERATURE AND STANDARD DEVIATION (VERTICAL POLARIZATION AND 45 ANGLE OF INCIDENCE) FOR DIFFERENT LAND TYPES IN 3 C. SEE TABLE II FOR EXPLANATION OF LAND COVER TYPES THE FALL OF 1991. THE GROUND WAS FROZEN AND FREE OF SNOW, AIR TEMPERATURE WAS

0

TABLE V OBSERVED MEAN BRIGHTNESS TEMPERATURE, ITS STANDARD DEVIATION (VERTICAL POLARIZATION AND 45 ANGLE OF INCIDENCE) AND SWE FOR DIFFERENT LAND TYPES IN THE MID-WINTER OF 1992. THE GROUND WAS COVERED BY DRY SNOW AND THE AIR TEMPERATURE WAS 7 C.

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snow fall, not due to vegetation. In most cases there are several test lines for each land type from different parts of the test site; thus, the effect SWE variations is reduced when land types are studied instead of test lines. Figs. 3 and 4 show the change of the mean brightness temperature for various land types with

the three snow situations (no snow, dry snow with SWE 122 to 145 mm and wet snow with SWE 137 to 169 mm) at 24 and 94 GHz, respectively. The relative values are calculated from Tables IV–VI. The reference situation in Figs. 3 and 4 is the fall of 1991 when the ground was frozen and free of snow. In


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TABLE VI OBSERVED MEAN BRIGHTNESS TEMPERATURE, ITS STANDARD DEVIATION (VERTICAL POLARIZATION AND 45 ANGLE OF INCIDENCE) AND SWE FOR DIFFERENT LAND TYPES IN THE SPRING OF 1992. THE GROUND WAS COVERED BY WET SNOW AND THE AIR TEMPERATURE WAS 6 C

mid-winter, surface type and SWE have more influence on the brightness temperature at the low frequency (24 and 34 GHz) than at the high frequency (94 GHz). At 24 GHz the mean brightness temperature of open areas (gravel, open bog, clearcut and forested bog) decreases with increasing SWE, while at other frequencies such a behavior is not found. It is apparent that at higher frequencies emission from the ground cannot penetrate the thick snow layer. At the higher frequencies the changes in the brightness temperature are mainly caused by the vegetation. Based on the magnitude of the change, three categories can be discriminated at 94 GHz: the change for forests is 20 K, that for sparsely forested areas is from 45 K to 60 K and that for open areas is over 80 K, respectively. The results indicate that even relatively sparse forest canopies (stem volume from 100 to 150 m3 /ha) may mask from 35% to 75% of the decrease of the brightness temperature caused by dry snow, depending on frequency. This suggests that accurate retrieval of the snow water equivalent from satellite data is not possible without detailed information on land-cover categories. Figs. 3 and 4 also show that the differences in the brightness temperature for various categories are relatively small in the spring when snow is wet. B. Effect of Forest Vegetation on the Brightness Temperature The brightness temperature for dense forest vegetation is close to its physical temperature while the brightness temperature for dry snow is relatively low in the frequency range used. Thus, the forest vegetation increases the total brightness temperature in dry snow situations. The transmissivity of forest vegetation decreases with increasing frequency; therefore, the effect of vegetation increases with increasing frequency [11].

Fig. 5, which is based on the data acquired on March 4, 1992, shows the change brightness temperature as a function of forest canopy density. The clear-cut area (test lines 1a, 17 and 18a) has no trees and SWE is 141 mm. The Sapling (Test Lines 1b, 17, 18b) has a stem volume of 0–50 m3 /ha and SWE is 141 mm, the Pine I (test lines 3b, 7b, 12, 15) 50–100 m3 /ha and 134 mm and the Pine II (test lines 1c, 19) 100–150 m3 /ha and 134 mm, respectively. Fig. 5 shows a 24 K increase due to forest canopies at 24 GHz, 48 K at 34 GHz, 45 K at 48 GHz and 66 K at 94 GHz, respectively. The brightness temperature increases with increasing frequency and stem volume until it saturates. The total brightness temperature of a forest area with snow-covered ground can be presented as (1) where is the emitted brightness temperature of the snow covered ground, is that of the forest vegetation, is the attenuation of forest, is the downward emitted brightness temperature of the atmosphere and is the reflectivity of the snow-covered ground. The reflection of the atmosphere from the ground is attenuated in both directions by forest The attenuation of forest vegetation is modeled by a homogeneous attenuator, which is located between the snow covered ground and the open air. This simplification is accepted because mean brightness temperatures of relatively large areas (clear-cut, sapling, Pine I and Pine II) are used in calculations. The emitted brightness temperature from the snow covered ground can be presented as (2)

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Table V). The term was estimated by the mean brightness temperature of Sapling, Pine I and Pine II (see Table V). In the calculations To is estimated by [13] (6) where is the physical temperature of ground and is the physical temperature of air. On March 4, 1992, was 2 C and was 7 C. In general, Fig. 6 shows that the total attenuation increases with increasing frequency. However, the highest attenuation values are obtained for Pine I (50–100 m3 /ha instead of Pine II (100–150 m3 /ha. The peculiar behavior of the total attenuation for Pine I and Pine II is explained by the fact that the crowns of trees are dominant attenuators and radiators in the frequency range used. The older and bigger pines (Pine II) have sparser crowns than the younger pines (Pine I), hence the attenuation and emission of Pine I is larger than that of Pine II in spite of the fact that the stem volume of Pine II is greater than that of Pine I. These results confirm that the brightness temperature is very sensitive to vegetation in snow measurements, especially at the higher frequencies. Thus, detailed vegetation information is needed for accurate radiometry measurements of snow. C. Detection of Refrozen Snow

Fig. 4. Observed effect of the snow situation on the mean brightness temperature for different land types at 94 GHz. Fall: snow-free and frozen ground, air temperature 3 C. Mid-winter: dry snow, SWE 122–145 mm, air temperature 7 C. Spring: wet snow, SWE 137–169 mm, air temperature 6 C. Results from snow-free conditions (October 22, 1991) are used as reference.

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where is the effective physical temperature of the snow covered ground and is the effective emissivity of the snow covered ground. Since the forest vegetation is considered as an uniform attenuator, the brightness temperature of the forest can be presented as [12]

(3) Equation (1) can be rewritten by using (2) and (3) (4) According to (4) the total attenuation of the forest

is (5)

Fig. 6 shows the total attenuation for different pine forests in the Sodankylä test site on March 4, 1992. The total attenuation was calculated for each vegetation type (Sapling, Pine I and Pine II) by (5). The term was estimated by the measured mean brightness temperature of Clear-cut (see

The on-set of melting is characterized by the daily changes from high brightness temperature values (wet snow in the daytime) to low values (refrozen dry snow at night). When snow refreezes at night, the brightness temperature decreases rapidly with increasing frequency. Thus, refrozen snow can be distinguished from bare ground and wet snow. This concept can be used for estimating the extent of the snow cover with radiometry during the melting period, if forest canopies do not mask the phenomenon totally. The detection of refrozen snow in forested areas was tested on May 13, 1993 when most of the snow had melted and the ground was only partially covered by snow. Snow patches existed in forests while open areas were free of snow. The thickness of the snow patches varied from 10 to 20 cm. The night between 12 and 13 May was cold, 7 C at the lowest, thus the existing snow patches froze completely. The total length of the test lines with snow patches was 1200 m and that without snow 950 m, respectively. The first measurement was made early in the morning when the air temperature was 2 C and snow patches were still frozen from the top to the bottom. The second measurement was made in the afternoon when the snow patches were wet and melting. The afternoon was sunny and the air temperature was 10 C. Fig. 7 shows the mean brightness temperature and 90% deviation for the forest test lines with snow patches (12, 16, 18b, and 19). On these test lines 40–60% of the ground was covered by snow. The solid markers represent the mean brightness temperatures and the empty markers represent the 90% deviation (90% of the results are between the empty markers). Fig. 8 shows the results for the forest test lines without snow patches (1b,1c, 3, and 7b), respectively. In Fig. 7, the deviation at the higher frequencies is larger and


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Fig. 5. Observed change of mean brightness temperature versus forest vegetation on March 4, 1992. Clear-cut: Stem volume 0 m3 /ha and SWE 141 mm, Sapling: stem volume 0–50 m3 /ha and SWE 141 mm, Pine I: stem volume 50–100 m3 /ha and SWE 134 mm and Pine II: stem volume 100–150 m3 /ha and SWE 134 mm.

Fig. 6. Calculated total attenuation of pine canopies on March 4, 1992. Sapling: stem volume of 0–50 m3 /ha Pine I: stem volume of 50–100 m3 /ha and Pine II: stem volume of 100–150 m3 /ha.

less symmetric in the morning than that in the afternoon, while in Fig. 8 the general behavior of both measurement results is very similar to each other. Fig. 9 shows the difference between the mean values from the morning and the afternoon measurements shown in Figs. 7 and 8. In the forest test lines without snow patches, the observed brightness temperature change is almost independent of frequency: it is close to the difference in the physical temperature between the morning and the afternoon. In the forest test lines with snow patches, the difference between the morning and the afternoon results increases with increasing frequency, because volume scattering in refrozen snow decreases with increasing frequency. Moreover, this effect was more dominant than masking of the forest vegetation (see also Section V-B). Thus, the best discrimination of refrozen snow was achieved at 94 GHz. The results suggest that a 94 GHz

radiometer is useful for estimating the extent of the snow cover during the melting period by comparing results for wet and refrozen conditions. VI. CONCLUSIONS Airborne microwave radiometer measurements of snowcovered and snow-free terrain were carried out in 1991–1993 in Sodankylä, Northern Finland. The goal of the project was to define: a) the general behavior of the brightness temperature of snow for different land types, b) the effect of forest canopies on the brightness temperature of snow, and c) the capability of the radiometer system to discriminate refrozen snow in forested areas during the melting period. Based on the measurements during dry snow conditions (high SWE values), the influence of the surface type and the SWE on the brightness temperature was higher at low

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Fig. 7. Observed mean brightness temperature and 90% deviation for forest test lines with snow patches on May 13, 1993.

Fig. 8. Observed mean brightness temperature and 90% deviation for forest test lines without snow patches on May 13, 1993.

Fig. 9. Observed change of mean brightness temperature between morning and afternoon measurements (see Figs. 7 and 8) on May 13.


frequencies (24 and 34 GHz) than at high frequencies (48 and 94 GHz). These differences were relatively small due to the rather large snow depths in the measurements. At the higher frequencies, the changes in the brightness temperatures were mainly caused by forest canopies. The effect of vegetation density on the brightness temperature was investigated by using the attenuation approach [12]. In general, the attenuation of the forest vegetation was observed to increase with increasing frequency and increasing stem volume. The total attenuation varied from 0.5 to 2.5 dB depending on the frequency and on the volume of forest. Nevertheless, the maximum attenuation values were observed for Pine I instead of Pine II, in spite of the fact that the stem volume of Pine II (100–150 m3 /ha) was greater than that of Pine I (50–100 m3 /ha). The experimental results on May 13, 1993 confirm that a high-frequency radiometer (94 GHz) is the most useful for estimating the extent of the snow cover in forested areas during the melting period. The capability of microwave radiometry for discrimination of refrozen snow increases with increasing frequency. The best discrimination of refrozen snow versus bare ground was achieved at 94 GHz. In general, the results of this study confirm that the landcover type has a strong influence on the brightness temperature of dry snow covered terrain; therefore, the land-cover type has to be considered in the retrieval of snow parameters from satellite data. The application of a digital land-type map with information on the emission behavior of different land types in dry snow conditions in the relevant frequency range, will increase the accuracy of the inversion methods for the snow parameters. REFERENCES [1] M. Hallikainen, “Retrieval of snow water equivalent from Nimbus-7 SMMR data: Effect of land-cover categories and weather conditions,” IEEE J. Oceanic Eng., vol. OE-9, pp. 372–376, 1984. [2] M. Hallikainen and P. Jolma, “Comparison of algorithms for retrieval of snow water equivalent from Nimbus-7 SMMR data in Finland,” IEEE Trans. Geosci. Remote Sensing, vol. 30, Jan. 1992. [3] M. Sturm, T. Grenfell, and D. Perovich, “Passive microwave measurements of tundra and taiga snow covers in Alaska, U.S.A.,” Ann. Glaciology, vol 17. pp. 125–130, 1993. [4] M. Hallikainen, M. Dobson, and S. Moezzi, “Influence of surface type on the brightness temperature of snow-covered terrain,” in Proc. URSI Commission F Symp. Wave Propagat. Remote Sensing, pp. 273–279, Louvain-la-Neuve, Belgium, 9–15 June, 1983. [5] A. Walker and B. Goodison, “Discrimination of a wet snowcover using passive microwave satellite data,” Ann. Glaciology, vol 17. pp. 307–311, 1993. [6] F. Ulaby and H. Stiles, “The active and passive microwave response to snow parameters, Part II: Water Equivalent of Dry Snow,” J. Geophys. Res., vol. 85, pp. 1045–1049, 1980. [7] E. Schanda, C. Mätzler, and K. Künzi, “Microwave remote sensing of snow cover,” Int. J. Remote Sensing, vol. 4, pp. 149–158, 1983. [8] R. Armstrong, A. Chang, A. Rango, and E. Josberger, “Snow depths and grain-size relationships with relevance for passive microwave studies,” Ann. Glaciology, vol 17. pp. 171–176, 1993. [9] C. Barron and B. Battrick, Eds., “The multifrequency imaging microwave radiometer (MIMR)–Instrument panel report,” European Space Agency, 1990. [10] M. Hallikainen, V. Jäa¨ skeläinen, T. Häme, and J. Perälä, “Application of ERS-1 Active Microwave Instrumentation data to remote sensing

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of snow,” Helsinki University of Technology, Laboratory of Space Technology, Report 6, 1992. [11] C. Mätzler, “Microwave transmissivity of a forest canopy: Experiments made with a beech,” Remote Sensing Environ., vol. 48, pp. 172–180, 1994. [12] F. Ulaby, R. Moore, and A. Fung, Microwave Remote Sensing: Active and Passive, Vol.II. Dedham, MA: Artech House, 1981, p. 889. [13] J. Pulliainen, M. Hallikainen, E. Somersalo, J-P. Kärnä, V. Jäa¨ skeläinen, J. Hyyppä, J. Talvela, and J-P. Luntama, “Study of microwave interaction with the earth’s surface, Volume I,” ESA Rep., ESTEC no. 8447/89/NL/PB(SC), 1990.

Lauri Kurvonen was born October 14, 1964, in Orimattila, Finland. He received the Master’s degree and the Licentiate’s degree in electrical engineering from Helsinki University of Technology (HUT), Espoo, in 1991 and 1994, respectively. Since 1989 he has been with the Laboratory of Space Technology/HUT in Espoo, Finland. His research interests include active and passive microwave remote sensing of ice, snow, and forest. Presently, he is a Research Fellow at the Space Applications Institute of Joint Research Centre of European Commission, Ispra, Italy.

Martii Hallikainen (M’83–SM’85–F’93) received the M.Sc. and the Doctor of Technology degrees from Helsinki University of Technology (HUT), Faculty of Electrical Engineering, in 1971 and 1980, respectively. From 1981 to 1983 he was a Postdoctoral Fellow at the University of Kansas Remote Sensing Laboratory, Lawrence, where his research involved microwave sensing of snow and soil. Since 1987 he has been a Professor of space technology at HUT, where his research interests include active and passive microwave remote sensing and microsatellite technology. He is Director of the Laboratory of Space Tehcnology at HUT. He was a Visiting Scientist from 1993 to 1994 at European Union’s Joint Research Centre, Institute for Remote Sensing Applications, Italy. Since 1989 he has been Principal Investigator of several ESA-funded international projects concerning construction of microwave sensors and microwave sensing of land, vegetation, sea ice, and snow. Dr. Hallikainen has been Vice President of the IEEE Geoscience and Remote Sensing Society from 1994 to 1995 and the GRSS President in 1996. Since 1988 he has been a member of the GRSS Administrative Committee. He was General Chairman of the IGARSS’91 Symposium and Guest Editor of the Special IGARSS’91 Issue of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. Since 1992 he has been an Associate Editor of this transactions and a Corresponding Member of the IEEE New Technology Directions Committee. He was Secretary General of the European Association of Remote Sensing Laboratories (EARSeL) from 1989 to 1993 and Chairman of the Organizing Committee for the EARSeL 1989 General Assembly and Symposium. He has been a member of the EARSeL Council since 1985 and Member of the Editorial Board of the EARSeL Advances in Remote Sensing since 1992. He is an Official Member of URSI Commission F (Wave Propagation and Remote Sensing), Member of the ESA Earth Observation Data Operations Scientific and Technical Advisory Group (DOSTAG), and Member of the ESA MIMR Scientific Advisory Group. He is a Member of the Advisory Committee for the European Microwave Signature Laboratory of the European Union’s Joint Reseach Centre, Institute for Remote Sensing Applications. He received the 1984 Editorial Board Prize of Journal Electricity in Finland. He and his research team received the 1989 National Research Project of the Year Award from Tekniikka and Talous (Technology and Management Magazine). In 1990 he received the HUT Foundation Award for excellence in research. His is the winner of the Microwave Prize for best paper in the 1992 European Microwave Conference. He is the recipient of the IEEE GRSS 1994 Outstanding Service Award.