SIMULATION OF SPACEBORNE MICROWAVE RADIOMETER MEASUREMENTS OF SNOW COVER FROM IN-SITU DATA AND EMISSION MODELS

Size: px

Start display at page:

Download "SIMULATION OF SPACEBORNE MICROWAVE RADIOMETER MEASUREMENTS OF SNOW COVER FROM IN-SITU DATA AND EMISSION MODELS"

Wesley Dalton
6 years ago
Views:

1 SIMULATION OF SPACEBORNE MICROWAVE RADIOMETER MEASUREMENTS OF SNOW COVER FROM IN-SITU DATA AND EMISSION MODELS Anna Kontu 1 and Jouni Pulliainen 1 1. Finnish Meteorological Institute, Arctic Research, Sodankylä, Finland; anna.kontu@fmi.fi ABSTRACT In this study, spaceborne brightness temperature measurements of snow-covered sparse forest are simulated from a daily data set collected at Sodankylä, Finland during winter The data set consists of measurement data of soil, snow and air. The atmospheric effects are simulated with two different methods: from measurements near the ground and from balloon-borne radio sounding profiles. The simulation results are compared to AMSR-E observations. The results show, that on low frequencies (below 20 GHz) the differences between the two methods are minimal, but the higher the frequency, the larger the differences and simulation errors are. The simulations with ground-based measurement data have lower RMS error than the ones with sounding profiles. As a conclusion, on low microwave frequencies both methods work equally well, but on higher frequencies ground-based observations and models give more accurate results than sounding profiles. INTRODUCTION Seasonal snow cover plays an important role in the hydrological and climatological processes of the boreal zone. To predict the beginning of snow melt and the amount of water coming from melting snow, continuous information on several snow parameters, such as snow water equivalent (SWE) and snow covered area (SCA), are needed, especially during springtime. Besides operational snow courses and weather stations, spaceborne microwave radiometer observations can be used to provide daily large scale information on snow cover. In order to extract snow data from spaceborne brightness temperature measurements, emission from soil, vegetation and atmosphere has to be taken into account. The properties of snow (e.g. grain size and shape, moisture content) also have an effect on its microwave emission. There are several algorithms for modelling of microwave emission from soil, vegetation canopy and snow cover. Usually empirical algorithms for estimating snow parameters are retrieved by analyzing satellite measurement data. However, the use of empirical regression coefficients reduces the regional and temporal applicability of such algorithms. On the other hand, purely theoretical emission models tend to be too complex and thus are not feasible for inversion of satellite data. Therefore the empirical and semi-empirical models used in this study, namely HUT snow emission model ( 1 ), rough bare soil reflectivity model ( 2 ) and boreal forest canopy model ( 3 ), are selected for their simplicity and generality. The validity of the chosen microwave emission models is investigated here by combining them to simulate spaceborne microwave brightness temperature measurements from an extensive in situ data set collected at Sodankylä, Finland during winter The simulation results are compared with AMSR-E instrument brightness temperature readings ( 4 ). First results of the simulations were presented in ( 5 ). This study concentrates on the differences between the brightness temperature time series simulated from ground-based weather station data and balloon-borne radio sounding data. Satellite data product validation with ground-based in-situ measurements requires accurate knowledge of the effects of atmosphere in the satellite measurement data. There is a dense network of ground-based weather stations measuring continuously around the world. Sites with frequent radio soundings are much sparser. Thus it is of interest to compare how well the ground-based measurements are able to model the effects of the

2 entire atmosphere. MATERIALS AND METHODS All the in-situ data used in this study were measured at the Arctic Research Centre of the Finnish Meteorological Institute at Sodankylä, northern Finland. The area, like the in-situ measurement site, is mostly covered with sparse pine forest. The undergrowth consists mainly of heather, lingonberry and reindeer moss. The area belongs to taiga snow class ( 6 ). Data were collected during the entire snowy season from the 7th of October 2006 to the 15th of May The permanent snow fell on the 17th of October and melted finally on the 13th of May. The measured parameters are listed and their minimum, maximum and mean values are presented in Table 1. Daily time series of all the parameters from the time of the best Aqua satellite ascending overpass were collected. Most of the data used in this study are available from Table 1: Statistics of the measured parameters. Parameter Min Max Mean Std. Dev. Unit Snow temperature ºC Snow depth cm SWE mm Snow density kg/m3 Grain size mm Soil temperature ºC Air temperature ºC For the year 2006, snow temperature was estimated as an average of measured soil (at 5 cm depth) and air (at 2 m height) temperatures. The measurement of snow temperature profile began at the turn of the year. Automatic sensors measured the temperature every ten centimeters from ground surface to 110 cm height. Snow depth was measured every minute with an acoustic sensor. Snow water equivalent and depth were measured every month from a few-kilometer long snow course. From these measurements the best fit constant snow density, kg/m 3, was defined. The daily SWE was calculated from snow depth using this constant. Snow moisture was not measured, but dry snow was estimated from satellite data using equations presented in ( 7 ). Otherwise snow was considered moist (0.5 %). Grain sizes of all the layers of snow were measured twice a week from a snow pit. Throughout the winter the measurements were conducted on the same small area. The snow layers were determined visually and the thickness of each layer was measured with a stick with 1-cm accuracy. Grain sizes were measured using the procedures suggested by ( 8 ). A small sample of snow was taken on a snow crystal screen with 1, 2 and 3-mm grids. Grain size was estimated visually by comparing the snow sample to the grids. It is not straightforward to calculate the effective grain size required by the HUT snow model from these measurements, and different methods can be found from the literature (e.g. ( 9 ),( 10 )). Here the layer thickness-weighted average of grain sizes was used. The atmospheric effects are modelled with two different methods: 1) from automatic weather station (AWS) measurements near the ground using a statistical atmospheric model ( 11 ) and 2) from balloon-borne radio sounding profiles using a physical atmospheric model ( 12 ). In both cases the measured parameters included temperature, pressure and relative humidity. The AWS system logged data every minute and a radio sonde was launched twice a day, at 11 and 23 UTC, corresponding well to the satellite overpass times. The statistical model uses standard Finnish

3 atmosphere as a starting point, and uses only air temperature as input parameter. The atmospheric model of Ulaby et al. requires air temperature, pressure and absolute humidity profiles as input parameters. The regular PTU soundings measure only relative humidity, which was converted to absolute humidity with ( 13 ). This data set was used to simulate daily time series of brightness temperatures for all the AMSR-E measurement frequencies (6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz) and both V and H polarizations. Two simulations, with the same soil and snow data but different methods for atmospheric modelling, were calculated. RESULTS The correlations, biases, RMS errors and bias-free RMS errors of the two simulations on all the AMSR-E channels are shown in Table 2. The correlations of the two simulations are almost identical and the differences in biases and RMS errors are very small especially on frequencies below 20 GHz. RMS error is lower in simulations with AWS data than in those with sonde data. On H polarization the simulation results are much poorer than on V polarization due to model inadequacies. Table 2: Comparison of the two simulated time series. Correlation Bias (K) RMS error (K) Bias-free RMSE (K) Channel (GHz) AWS Sonde AWS Sonde AWS Sonde AWS Sonde V polarization H polarization The difference between the two simulated time series on V polarization and all the AMSR-E frequencies is plotted in Figure 1. On low frequencies (below 20 GHz) the differences are very small, but the higher the frequency, the larger the differences are. Most of the time simulations with AWS data give higher brightness temperature values than those with sonde data. H polarization behaves similarly, except the difference on low frequencies is a bit higher. Figure 2 shows the comparison of simulation using AWS data and AMSR-E measurements. Like the comparison of the two simulations, the lowest frequencies have small errors. Even on the highest frequencies the average error is small but there are some very high peaks. The errors on high frequencies are explained by emission from clouds. Clouds have a very small effect on microwave radiation from the surface on low microwave frequencies, but the effect increases with

frequency. Due to different nature of the atmospheric models used with ground-based and radio sonde data, the simulation with sounding data suffers from larger errors.

$Cloud coverage fraction is measured at Sodankylä with a ceilometer, but since AMSR-E pixel size ranges from 5 to 50 km (depending on frequency), the point measurement at one location doesn't give the$

4 frequency. Due to different nature of the atmospheric models used with ground-based and radio sonde data, the simulation with sounding data suffers from larger errors. Unfortunately no hydrometeor data was available for the study. Cloud coverage fraction is measured at Sodankylä with a ceilometer, but since AMSR-E pixel size ranges from 5 to 50 km (depending on frequency), the point measurement at one location doesn't give the actual cloudiness in the entire satellite data pixel. Thus it was not possible to calculate correlation between simulation error and cloudiness. Figure 1: Differences between the two simulations (AWS - sonde) on V polarization. Figure 2: Difference between simulation with AWS data and AMSR-E measurements (simulation - measurement) on V polarization. CONCLUSIONS Daily time series of spaceborne microwave brightness temperature observations of snow-covered forest for the winter were simulated from an in-situ data set measured at Sodankylä, Finland. The simulation results were compared to AMSR-E measurements. This study

5 concentrated on the differences between the time series calculated using ground-based sensors and balloon-borne radio sounding profiles. The results show, that on the lowest frequencies the differences between the two simulations are very small, but the errors increase with frequency. Due to different nature of the atmospheric models used with the two data sets, the simulation with ground-based weather station data gives lower RMS error values than simulation using radio sounding profiles. REFERENCES 1 Pulliainen J T, J Grandell & M T Hallikainen, HUT snow emission model and its applicability to snow water equivalent retrieval. IEEE Transactions on Geoscience and Remote Sensing, 37: Wegmüller U & C Mätzler, Rough bare soil reflectivity model. IEEE Transactions on Geoscience and Remote Sensing, 37: Kruopis N, J Praks, A N Arslan, H M Alasalmi, J T Koskinen & M T Hallikainen, Passive microwave measurements of snow-covered forest areas in EMAC'95. IEEE Transactions on Geoscience and Remote Sensing, 37: Ashcroft P & F Wentz, AMSR-E/Aqua L2A global swath spatially-resampled brightness temperatures (Tb) V002, digital media, updated daily 5 Kontu A, J Pulliainen, P Heikkinen, H Suokanerva & M Takala, Validation of microwave emission models by simulating AMSR-E brightness temperature data from ground-based observations. In: Proceedings of 2007 IGARSS (IGARSS, Barcelona, Spain) 6 Sturm M & J Holmgren, A seasonal snow cover classification system for local to global applications. Journal of Climate, 8(5): Hall D, R Kell, G Riggs, A Chang & J Foster, Assessment of relative accuracy of hemisphere-scale snow cover maps. Annals of Glaciology, 34: Colbeck S, E Akitaya, R Armstrong, H Gubler, J Lafeuille, K Lied, D McClung & E Morris, The International Classification for Seasonal Snow on the Ground, (The International Commission on Snow and Ice of the International Association of Scientific Hydrology and International Glaciological Society) 9 Tedesco M & E J Kim, Intercomparison of electromagnetic models for passive microwave remote sensing of snow. IEEE Transactions on Geoscience and Remote Sensing, 44(10): Roy V, K Goïta, A Royer, A E Walker & B E Goodison, Snow water equivalent retrieval in a Canadian boreal environment from microwave measurements using the HUT snow emission model. IEEE Transactions on Geoscience and Remote Sensing, 42(9): Pulliainen J, J Kärnä & M T Hallikainen, Development of geophysical retrieval algorithms for the MIMR. IEEE Transactions on Geoscience and Remote Sensing, 31: Ulaby F, R Moore & A Fung, Microwave Remote Sensing, Active and Passive, vol. 1, chapter 5 (Addison-Wesley Publishing Company) 456 pp. 13 Hyland R W & A Wexler, Formulations for the thermodynamic properties of the saturated phases of H 2 O from K to K. ASHRAE Transactions, 89(2A):

SEA ICE MICROWAVE EMISSION MODELLING APPLICATIONS

SEA ICE MICROWAVE EMISSION MODELLING APPLICATIONS R. T. Tonboe, S. Andersen, R. S. Gill Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen Ø, Denmark Tel.:+45 39 15 73 49, e-mail: rtt@dmi.dk