Remote sensing of snow cover with passive and active microwave sensors

Transcription

1 Hydrological Applications of Remote Sensing and Remote Data Transmission (Proceedings of the Hamburg Symposium, August 1983). IAHS Publ. no Remote sensing of snow cover with passive and active microwave sensors I PRODUCTION H, ROTT Institut fur Météorologie und Geophysik, Universitat Innsbruck, A-6020 Innsbruck, Austria K, F, KUNZI Institut fur angewandte Physik, Universitat Bern, CH-3012 Bern, Switzerland ABSTRACT Investigations using data from the Scanning Multichannel Microwave Radiometer (SMMR) on Nimbus-7 revealed the great potential of passive microwave sensors to monitor snow cover parameters, such as areal extent, water equivalent and onset of snowmelt, both on a global scale and for large drainage basins (S10 km ) virtually unaffected by cloud cover. Active microwave sensors (e.g. synthetic aperture radar, SAR) offer a much higher spatial resolution from space and aircrafts than can be achieved with passive microwave sensors. Data from an airborne SAR-experiment (X- and C-band) show, that snow extent during the runoff phase can be mapped even in rugged terrain. Télédétection de la couverture neigeuse à 1'aide de détecteurs passifs et actifs a hyper fréquences RESUME Des recherches à partir de données obtenues par le radiomètre à balayage à multi-hyperfréquences (SMMR) sur Nimbus-7, ont révélé le grand potentiel des détecteurs passifs à hyperfréquences dans la détermination des paramètres de la couche de neige, tels que l'étendue spatiale, l'équivalent en eau et l'amorçage de la fonte, sur une échelle globale et pour des bassins étendus 5 2 (S10 km ). Les mesures ne sont virtuellement pas affectées par la couverture nuageuse. Les détecteurs actifs à hyperfréquences (par exemple, radar à synthèse d'ouverture, SAR) offrent une beaucoup plus grande résolution spatiale dépuis l'espace et les avions, que celle qui sera jamais atteinte avec les détecteurs passifs. Les résultats obtenus à l'aide d'un SAR aéroporté (dans les bandes X et C) montrent qu'une étude de l'étendue de neige pendant la phase d'écoulement, peut être réalisée même en terrain accidenté. Satellite remote sensing of snow areal extent in the visible spectrum is operationally used for hydrological modelling and forecasting and for climatological applications. However, in many regions the use of visible and infrared satellite data is severely restricted due to 361

2 362 H.Rott S K.F.Kïïnzi frequent cloudiness. With sensors in the microwave region snow cover observations are possible under almost all weather conditions. In addition, microwaves are able to penetrate the snow layer and thus to provide information on snowpack properties not available from other sensors. This paper reports on ongoing research with microwave remote sensing data to monitor snow parameters on global and regional scales. Investigations with data of the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) resulted in algorithms for mapping snow parameters on a global scale, which are also useful for snow mapping in individual drainage basins. Synthetic aperture radar (SAR) systems on satellites offer significantly better areal resolution than microwave radiometers, however, more experiments are needed to determine the optimum active system for operational snow monitoring. As an example we report on a snow mapping experiment conducted in July 1981 at a test site in the Austrian Alps with an airborne X- and C-band SAR. MICROWAVE CHARACTERISTICS OF SNOW Microwave emission of snow The observed microwave emission of a surface can be expressed as brightness temperature T B, which is the product of the effective emissivity e (0 < e < 1) and absolute temperature T: T B (v) = e(v) T (K) (1) In the case of dry snow both the snow volume and the underlying ground contribute to Tg. Scattering within the snow volume reduces Tg in dependence of frequency, enabling the discrimination of snowcovered and snow-free ground. Scattering losses are correlated with the total snow volume (water equivalent) at higher microwave frequencies. Grain size and stratigraphy, however, have also some effect on scattering. Liquid water drastically changes the emission and scattering properties of snow. The emissivity of a wet snow pack is almost one, therefore wet snow cannot be discriminated from snow-free ground in many cases. Microwave backscattering from snow Contributions to radar return from snow-covered ground originate from the air-snow boundary, from scattering within the snow volume, and from the snow-ground boundary. The radar return is described by a, the scattering cross section per unit area, which is for a given target a function of frequency v, polarization p, incidence angle 9, and azimuth angle <j> : o = a (v,p,e,<j)) (2) In C-band (4-8 GHz) and X-band ( GHz) volume scattering losses in a dry snow pack are small, reducing a relatively to snow-free ground only slightly. Therefore dry snow often cannot be detected

3 Remote sensing of snow cover 363 with radar (Mâtzler & Schanda, 1983). The penetration depth of wet snow is in the order of a few centimetres. For radar return from wet snow, volume scattering of the top few centimetres is dominant, with some contribution by scattering from the rough surface. This results in low a -values for wet snow (Stiles & Ulaby, 1980). Therefore in contrast to passive microwave sensors, wet snow and snow-free ground can be clearly distinguished with active sensors. SNCW MAPPING WITH THE NIMBUS-7 SCANNING MULTICHANNEL MICROWAVE RADIOMETER (SMMR) The SMMR experiment on board Nimbus-7, launched into a s un-synchronous orbit on 24 October 1978, is scanning the earth's surface at five frequencies (6.6, 10.7, 18.0, 21.0, 37.0 GHz) in two polarizations under a constant incidence angle of -50. SMMR covers a swath width of 780 km. Due to power limitations SMMR operates only on alternate days. The Tg-values of the standard data product, as processed by NASA, are given for cells of 30x30 km 2 (37 GHz) to 156x156 km (6.6 GHz). The original antenna temperature data have 2 2 somewhat higher resolution, ranging from 28x17 km to 152x97 km. The algorithms for retrieval of snow parameters The algorithms for snow mapping with SMMR data were derived by statistical analyses and correlation with ground truth data; a detailed description is given by K'ùnzi et al. (1982). The main results are briefly summarized in the following. The brightness temperature gradient GT = [T B (v 1 ) - T B (V 2 )]/(V 1 - V 2 ) (K GHz -1 ) (3) with v 1 = 37 GHz and v = 18 GHz, and both T values in horizontal polarization, revealed the best results for discriminating snowcovered from snow-free ground and also for calculating the snow water equivalent. The errors in GT due to atmospheric extinction and emission can be neglected for snow studies under most meteorological conditions. The decision rule, which enables automatic mapping of snow areas on a global scale, is dry snow is present, if GT S D (4) The decision boundary D was determined empirically from the SMMR data of the first year as D = -0.1 K GHz -1 (Rott & Klinzi, 1983), in the case of repeated coverage of the same pixel the minimum GT value is taken. Figure 1 shows examples for frequency distributions of minimum GT in winter and summer for the European continent. The GT-distribution of the snow free continent shows little dispersion with a marked peak at +0.1 K GHz -1. The blmodal GT-distribution in winter illustrates the separation between snow-free land with positive GT values and snow-covered land on the negative side. The negative GT values are inversely correlated with snow depth or water equivalent. GT-values ~ 0.3 K GHz -1 originate from very humid soil. For calculating the water equivalent w n from SMMR measurements

4 364 H.Rott S K.F.Kunzi l.ch 3, 5, 7 July 1373 GT-2'.O ' -l'.o ' O'.O ' 1.0IK GHz'l GT FIG.l Frequency distribution of the minimum brightness temperature gradient GT (equation (3)) for Europe, normalized to maximum frequency. the following relation was found by comparison with ground truth data from Canada, Russia, and Finland in the 1978/1979 winter season (Kunzi et al., 1982): w n = A(GT - D) (mm) (5) This relation is valid for dry snow layers <50 cm, the empirical determined coefficient A varies for substantially different climatic regions, because of different snowpack scattering properties. For the investigated test areas a one sigma error of w n = 2 mm was found for equation (5) with an average coefficient A = -7.5 (mm GHz K~ ). The onset of snowmelt can be detected by observing the temporal change in GT of wet snow (GT ~ 0) and dry snow (GT < 0) due to the melt and refreeze cycle. Examples of snow mapping at three scales Automatic snow cover mapping was carried out with the use of a digital image processing system linked to a minicomputer. For one hemispheric analysis SMMR data of three alternate days (42 orbits) are used. The computer time for mapping these data in equal area projection and for classification is less than 2 h. An example is given in Fig.2, showing the northern hemispheric dry and melting snow areas derived from SMMR data on 15, 17, 19 March The analysis is based on the SMMR standard data product in 37 GHz and 18 GHz with 60x60 km cell size using the classification algorithms specified above. From the data of Fig.2 the total snow cover on land surfaces was calculated as 42.7xl0 6 km, large melting areas were found in Europe (2.2xl0 6 km 2 melting out of 5.5xl0 6 km 2 total snow cover) and in North America (3.8xl0 6 km 2 and 11.9xl0 6 km 2 respectively). A comparison of SMMR-derived total hemispheric snow areas from all seasons with NESDIS northern hemisphere weekly snow cover data derived from visible satellite sensors showed good agreement (Rott & Kunzi, 1983). In general SMMR derived hemispheric snow cover was a few percent smaller than NESDIS snow cover, because very thin snow and continuously melting areas cannot be detected

5 Remote sensing of snow cover 365 FIG.2 Hemispheric snow cover derived from SMMR data on 15, 17, 19 March Code for ice-free land surfaces: dark grey = snow free; light grey = dry snow; white = melting snow. with SMMR. Larger discrepancies due to wet snow were found only in late spring and summer. The two following examples indicate the potential of spaceborne microwave radiometers for hydrological applications. To take advantage of the full spatial resolution the SMMR antenna temperature data were used, processed by NASA on special request for a number of days. The footprints are ellipses with the dimensions 56 km x 35 km at 18 GHz, and 28 km x 17 km for the 37 GHz channel. Figure 3 shows a map of the snow extent in Central Europe derived from SMMR data on 19, 21, 23 February 1979; the snow boundaries correspond well to ground truth data. The snow covered mountain ranges of the Alps, the Carpathians, and the comparatively small Massif Central are evident. Figure 4 shows snow extent in the drainage basin of the Danube 3 2 above Vienna with a total area of 104x10 km, automatically mapped and enlarged from SMMR snow maps. Snow depth is given in two steps, the SMMR-derived snow area in the basin amounted to 61% on 21 February, to 71% on 27 February, to 28% on 13 March, and to 27% on 17 March The snow extent agrees with ground observations, snow cover first increased due to snowfall on 26 February, after 5 March the snow cover melted in lower altitudes. From the global SMMR snow maps similar analyses can be made for any drainage basin 1x10 km. For deriving w n from SMMR data for basins with various surface types, additional information on emission characteristics of the surface can improve the results (Tiuri & Sihvola, 1982).

6 366 H.Rott & K.F.Kunzi FIG.3 Snow cover in Central Europe derived from full resolution SMMR data on 19, 21, 23 February Dark grey = snow free; light grey = snow < 20 cm or broken snow cover; white = dry snow > 10 cm. FIG.4 Snow extent from SMMR data for the drainage basin of the Danube above Vienna on four days in late winter Grey level code as in Fig.3.

7 THE SAR SNCW MAPPING EXPERIMENT Remote sensing of snow cover 367 On 7 July 1981 a test site in the Otztaler Alps (Austria) was mapped during the European SAR-580 Campaign with X-band (9.3 GHz) and C-band (5.3 GHz) SAR from an aircraft flying at 6100 m a.m.s.l. The partly glacierized site covers altitudes from 2000 to 3600 m; most parts of the glaciers and some ice-free areas were covered with wet snow during the SAR overflight. Due to the high liquid water content (4% to 7% volume) the penetration depth in snow was on average only 2 cm in X-band and 4 cm in C-band, the snow surface was comparatively smooth with r.m.s. heights between 0.3 and 0.8 cm. Figure 5 shows part of the digitally processed SAR data in X-band, horizontal polarizations (HH). The full resolution of a pixel is about 3 x 3 m; in Fig.5 the data were averaged over 4x4 pixels and enhanced by a square root function, to increase the range of the low brightness values. Areas with high radar return, mainly rock covered slopes and moraines, appear bright, low grey values correspond to low return. A grey level profile is given in the look direction, covering radar look angles from 45 to 72 off normal. Topographic features are enhanced, because the backscattering intensity is dependent on the local incidence angle, given by the look angle and the surface slope angle, and because the coordinates in look direction are proportional to the distance of the surface elements to the radar antenna. Therefore slopes inclined towards the antenna appear shortened and give a strong return. No information is FIG.5 Enhanced airborne SAR image in X-band, HH polarizations, from a test site in the Austrian Alps. The grey values at the white line are plotted to the right in a relative scale. Major surface types: S = snow, R = rock, I = glacier ice, M = moraine; SH = radar shadow.

8 368 H.Rott S K.F.Kïïnzi received from the zones in the shadow of the radar beam. The snow areas show low return and can be discriminated by tone from the surrounding areas in most parts of the test site. Because of the angular dependence of the radar return, topographic information has to be included for automatic snow mapping. The differences in the backscattering can be seen in the mean values of the scattering cross section a, which were derived on a relative scale at 60 incidence angle in X-band (HH): Rock Grassland Wet snow Noise a 0 (relative) db Qualitatively similar results were obtained from C-band data, though quantitative analysis did not appear useful due to the poor signal to noise ratio related to sensor problems. Only a small area of the glaciers was snow free during the experiment; in X-band 0 of the glacier ice with its rough surface was about 5 db above a of the snow cover. Glacier ice and snow could also be separated in C-band, and similar results were obtained with Seasat SAR in L-band (1.3 GHz) (Rott, 1980). The investigations have shown the capability of radar systems to monitor the snow cover during the runoff phase. CONCLUSIONS Spaceborne microwave sensors have the potential to provide worldwide valuable data on the snow cover under all weather conditions. For applications in hydrology such as long-term and short-term runoff forecasts, a combination of passive sensors for mapping dry snow, water equivalent, and onset of snowmelt and active sensors for mapping wet snow with high areal resolution could guarantee the optimum information. The further improvement of the sensors, such as increasing surface resolution of the passive sensors, will increase the number of applications. But also with present sensor technology many users can be satisfied, however, for an operational microwave satellite system close to real time data transmission and analysis is required. ACKNOWLEDGEMENTS This work was supported in part by the Austrian Fonds zur Forderung der wissenschaftlichen Forschung and by the Swiss National Science Foundation; the SMMR data were made available by the NASA Goddard Space Flight Center. The SAR-580 Campaign was conducted by the European Space Agency and by the Joint Research Centre of the European Communities. REFERENCES Kiinzi, K.F., Patil, S. & Rott, H. (1982) Snow caver parameters retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) data. IEEE Trans. Geosci. Remote Sens. GE-20 (4), Matzler, C. & Schanda, E. (1983) Snow mapping with active microwave sensors. Int. J. Remote Sens. 4 (in press).

9 Remote sensing of snow cover 369 Rott, H. (1980) Synthetic aperture radar capabilities for glacier monitoring demonstrated with Seasat SAR data. Z. Gletscherkunde Glazialgeol. 16 (2), Rott, H. & Kunzi, K.F. (1983) Properties of the global snow cover and of snow free terrain from the Nimbus-7 SMMR first year data set. In: Proc. of the Specialist Meeting on Microwave Radiometry and Remote Sensing Applications (Rome, March 1983) (in press). Stiles, W.H. & Ulaby, F.T. (1980) The active and passive microwave response to snow parameters. 1. Wetness. J. Geophys. Res. 85 (C2), Tiuri, M. & Sihvola, A. (1982) Remote sensing of snow depth by passive microwave satellite observations. In: Proc. of IEEE Geosci. and Remote Sensing Symp (Munich, June 1982).