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Estimation of sea surface wind vector using RADARSAT data
Remote Sensing of Environment 80 (2002) 55 – 64 www.elsevier.com/locate/rse
Estimation of sea surface wind vector using RADARSAT data Duk-jin Kima, Wooil M. Moona,b,* a
ESI3 Laboratory, School of Earth and Environmental Science, Seoul National University, Seoul 151-742, South Korea b Geophysics, University of Manitoba-Geophysics, Winnipeg, Manitoba, Canada R3T 2N2 Received 18 April 2000; received in revised form 13 June 2001; accepted 13 June 2001
Abstract Synthetic aperture radars (SAR) have higher spatial resolution than scatterometers and we can obtain more detailed wind vector information from space-borne SAR data. This type of high spatial resolution wind information can be very useful particularly in coastal regions, where the scatterometer wind information can be altered by the coastal effects because the larger footprint spatial averaging of backscattered energy is unavoidable. This paper tested RADARSAT data with CMOD_4 and CMOD_IFR2 algorithms for extracting wind information over selected coastal test areas around the Korean peninsula. Because the CMOD algorithms were originally developed for the C-band, VV-Polarized scatterometer data, we applied currently available polarization ratio models (e.g., Kirchhoff, Elfouhaily, and Thompson models) for RADARSAT data processing. Three test areas, the West Sea and East Sea on both sides of the Korea peninsula and the open sea off Jeju Island, were tested with the RADARSAT Fine, Standard, and ScanSAR beam mode data. The correlation of the wind vector results with the buoy and meteorological station data agrees well with each other but with some variations. The RMS error for the SAR-derived wind direction is somewhat great, and the wind speed RMS error using CMOD_4 algorithm is about 1.7 m/s (Kirchhoff), 1.84 m/s (Elfouhaily), and 2.4 m/s (Thompson polarization ratio model), respectively. D 2002 Elsevier Science Inc. All rights reserved. Keywords: RADARSAT; Wind vector; Polarization ratio models; RADARSAT beam modes
1. Introduction Synthetic aperture radar (SAR) data have recently been increasingly used in various earth observation applications. Since the launching of ERS-1, there have been numerous attempts to estimate wind vectors from SAR data over water-covered areas. Previously, wind vector information over the ocean were usually obtained using scatterometer data or from in-situ measurements. Scatterometers on ERS1/2 measure the wind vector with a spatial resolution of 50 50 km and accuracies of ± 2 m/s in wind speed and ± 20 in wind direction (Wismann, 1992). But because of the low spatial resolution of the scatterometer data, wind vector information in coastal regions or near coastal areas is difficult to estimate accurately using these sensors. SAR data over the ocean, however, have the potential of * Corresponding author. University of Manitoba-Geophysics, Winnipeg, Manitoba, Canada R3T 2N2. Tel.: +1-204-747-9833; fax: +1-204-4747623. E-mail addresses: [email protected] (D.-j Kim), [email protected], [email protected] (W.M. Moon).
providing wind information with finer spatial resolution when it becomes necessary. A number of investigations on estimating wind vectors over oceans have recently been reported by various groups such as Kim and Moon (2000), Korsbakken, Johannessen, and Johannessen (1998), Shuchman, Johannessen, and Rufenach (1994), Vachon, Campbell, and Dobson (1997), Vachon and Dobson (1996), and Wackerman, Rufenach, Shuchman, Johannessen, and Davidson (1996). While years of effort have been applied to tune the sea surface wind/backscatter model for scatterometer data, the current models for high resolution wind estimation using SAR data are relatively immature. In this paper, we demonstrate a technique to modify the CMOD models to be applicable to SAR data. Several polarization ratio models have been introduced in the literature for such modification. We applied and tested three polarization models to the CMOD_4 (Stoffelen & Anderson, 1993) and to the CMOD_IFR2 (IFREMER-CERSAT, 1999) models and estimate the wind speed from RADARSAT SAR data. The test areas include several high-resolution cells close to the Korean peninsula and surrounding seas.
0034-4257/01/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 0 3 4 - 4 2 5 7 ( 0 1 ) 0 0 2 6 7 - X
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In Section 2, an overview of wind retrieval is described based on the CMOD models. A technique is outlined to show how the CMOD models can be converted to the alternate polarization data of RADARSAT using simple functions of the incidence angle. Section 3 applies the models developed in Section 2 to SAR data. The results presented in this section demonstrate good wind estimation accuracy with SAR resolution capabilities. Section 4 presents the conclusions to be drawn from this study.
2. Wind vector extraction from RADARSAT data 2.1. Wind speed retrieval models There are several wind speed retrieval models available, but, in this paper, we have used two models, CMOD_4 (Stoffelen & Anderson, 1993) and CMOD_IFR2 (IFREMER-CERSAT, 1999), for estimating wind speed from RADARSAT data. The algorithm based on the CMOD_4 model was originally developed with three types of Earth observation data: the ERS-1 scatterometer data, the wind vectors from the European Centre for Medium Range Weather Forecasts (ECMWF) surface wind analysis, and the wind and wave information from the National Oceanic and Atmospheric Administration (NOAA) wind and wave buoys, respectively. The CMOD_IFR2 algorithm is used for postprocessing of ERS scatterometer data. A scatterometer wind field is usually estimated from three antenna measurements over open water. As the three possible measurements of backscattering coefficient from the fore-, mid-, and aft-beam do not have direct relationships, the backscattering coefficient is usually estimated through an indirect approach of inversion (Fig. 1). The effect of wind direction in the estimation of wind speed is important and a series of experiment was carried out. The variation of estimated wind speed as a function of
wind direction is shown in Fig. 2. In the case of HH polarized C-Band RADARSAT data, the s0 naught decreases as the incidence angle increases and the wind speed becomes more sensitive to the error in the wind direction input. 2.2. RADARSAT SAR calibration The radar cross section (s), which has units of area, characterizes the scattering strength of the target in the backscattering direction in the form of an effective area. In general, the radar cross-section of a given target is related to its shape and dielectric constant, the viewing geometry, and the wavelength (l) and polarization directions of the incident and scattered waves. The standard definition for s is in terms of the ratio of the scattered power density Irec = PtGts/(4pR2)2 measured at a distance R from the scatterer to the power density I(R) = PtGt/4pR2 of an incident wave. The Pt is the average transmitter power, and the Gt is the antenna gain. Thus (Eq. (1)), Irec s ¼ limR!1 4pR2 ð1Þ IðRÞ where the limit as R ! 1 is included to denote that the observation point is in the far-field region (Curlander & McDonough, 1991; Ulaby & Dobson, 1989). In the case of RADARSAT data, because a dynamic gain with a 4-bit ADC is used (Vachon et al., 1997), the relationship between ¯ digital number (DN) and averaged radar cross-section (s) is as follow (Eq. (2)): DN2 ¼ INT½A2 s A3
ð2Þ
where A2 (function of range) is the output scaling gain, A3 is the output scaling offset which is normally zero. The backscattering coefficient (or sigma naught) is defined as the backscattering cross section of a distributed target of horizontal area A, normalized with respect to A such that s0 = s/A. The Backscattering coefficient is usually expressed in decibel, which is given by (Eq. (3)): s0 ¼ b0 þ 10 log10 ðsinIÞ ðdBÞ
ð3Þ
where (Eq. (4)) b0 ¼ 10 log10 ½ðDN2 þ A3 Þ=A2 ðdBÞ
ð4Þ
where I is the incidence angle (Srivastava & Shepherd, 1998). 2.3. Estimation of wind direction
Fig. 1. ERS and RADARSAT beam modes. The ERS-1/2 have a fixed incidence angle and swath, but the RADARSAT SAR instrument able to adjust the incidence angle, coverage, and spatial resolution.
The precise wind direction information is necessary to estimate accurate wind speed using CMOD models. Under certain circumstances, it is possible to extract the wind direction directly form the SAR image. SAR imagery can contain kilometer-scale linear features due
D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
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Fig. 2. Estimated wind speed as a function of input wind direction.
to atmospheric roll vortices or Langmuir circulations. Roll vortices, or wind rows, are counter-rotation helical circulations in the planetary boundary layer (PBL) that are roughly aligned with the mean wind direction (Gerling, 1986; LeMone, 1973). Other linear features caused by either concentration of surfactants or Langmuir circulations are also observed in the SAR image data and these features aligned with the wind direction (Leibovich, 1983; Mastin, Harlow, Huh, & Hsu, 1985). Due to the periodicity of these features, we can estimate the wind direction utilizing a 2-D Fourier transforms. The periods are usually very long and the low-wavenumber energy of the features of interest will be very close to the origin in the wavenumber space. The peak orientation is nearly perpendicular to the measured wind direction in this case (Shuchman et al., 1994; Vachon & Dobson, 1996). But the estimated direction has a 180 ambiguity. This type of direction estimation using a 2-D Fourier transform is well known and is applied here.
are. As a preliminary way of dealing with this problem and estimating wind speeds, a relationship between the HH and VV polarization becomes necessary. Here, we present three models for ratios of the horizontal backscatter to the vertical backscatter. Recently, Thompson and Beal (1998) and Thompson, Elfouhaily, and Chapron (1998) derived an empirical expression for the polarization ratio to obtain an approx-
2.4. Polarization ratio The CMOD series algorithms were originally developed for the C-band, VV-Polarized microwave data such as ERS1/2 scatterometers. In the case of RADARSAT, the SAR system operates at C-band but with HH Polarization, and the CMOD series models cannot be directly used as they
Fig. 3. Polarization ratio (VV/HH) versus incidence angle.
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D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64 Table 2 Observed wind direction and estimated wind direction results Satellite
Fig. 4. Location map showing the study sites.
imate form for the HH polarization backscatter from RADARSAT. This hybrid expression has the form: sH 0 ¼
ð1:6 sin2 qÞ2 ð1:6 þ sin2 qÞ
sV ðU ; q; fÞ 2 0
ð5Þ
where U is the wind speed, q is the incidence angle, and f is the azimuth angle of the radar with respect to the wind direction. If the factor 1.6 is replaced by unity, this equation reduces to the Bragg scattering polarization ratio. Some theoretical polarization ratios have been summarized in the work of Elfouhaily, Thompson, Vandemark, and Chapron (1999). From this work, Vachon and Dobson (2000) deduce the C-band polarization ratio, Rp (VV/HH) (Eq. (6)).
Rp ¼
½1 þ 2tan2 ðqÞ 2 ½1 þ atan2 ðqÞ 2
½linear units
ð6Þ
Test site
RADARSAT ScanSAR East Sea #1 East Sea #2 East Sea #3 East Sea #4 East Sea #5 East Sea #6 East Sea #7 East Sea #8 Standard Inchon1 [1] Inchon2 [2] Duck-Jeok Island [3] Jeju1 [4] Jeju2 [5] Fine Jeju_A hAi Jeju_B hBi Jeju_C hCi Uljin_D hDi Uljin_E hEi Uljin_F hFi
Observed wind Estimated wind direction () direction () 264.6 198.2 333.9 328.9 32.6 13.0 303.0 68.3 340 340 282
198 227 334 58 358 48 5 88 252 236 NA
270 200 90 90 20 230 290 180
279 239 82 93 NA NA 281 171
This equation becomes same as Eq. (5) when a = 0.6 and the incidence angle is between 20 and 50 (Thompson et al., 1998) (Fig. 3). Vachon and Dobson called this relationship as Thompson model (T). They also called Kirchhoff scattering (K) when a = 1.0, and Elfouhaily scattering model (E) when following equation is used (Eq. (7)).
Rp ¼
½1 þ 2tan2 ðqÞ 2 ½1 þ 2sin2 ðqÞ 2
½linear units
ð7Þ
We will use the same name to estimate sea surface wind speed from the C-band HH polarized RADARSAT data (Fig. 3).
Table 1 RADARSAT data acquisition parameters Satellite RADARSAT
Test site
Date time (UTC)
Beam type
Config./Orient.
Product type
ScanSAR (large gray box)
East Sea #1 – #8
1997/08/18 21:20.07
SWB
DES./NORM.
SCW
Standard (black box)
Inchon 1 [1] Inchon 2 [2] Duck-Jeok Island [3] Jeju1 [4] Jeju2 [5]
1997/11/4 09:30.41
S4
ASC./NORM.
SGF
1998/02/19 21:31.58
S7
DES./NORM.
SGF
Jeju_A hAi Jeju_B hBi Jeju_C hCi Uljin_D hDi Uljin_E hEi Uljin_F hFi
1998/02/18 09:37.56
F2
ASC./NORM.
SGF
1999/11/25 09:26.22 2000/05/11 09:26.22 2000/06/04 09:26.12
F3 F3 F3
ASC./NORM. ASC./NORM. ASC./NORM.
SLC SLC SLC
Fine (diamond)
D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
3. Estimation of wind vector and discussions We have so far discussed the methods one can use for estimating wind vector from the C-band, HH polarized RADARSAT data using CMOD_4 or CMOD_IFR2. In the following, we actually calculated wind vectors for a selected study sites around the Korean peninsula and compared the results with the nearby Korean meteorological station data and/or buoy data. Each location of the study sites is shown in Fig. 4. As shown in Fig. 4, this study is focused on the coastal regions around the Korean peninsula. Fine mode data were tested on north and south shores of the Jeju Island and the shores along East Sea –Uljin area (diamond shape location symbols in Fig. 4). Standard mode data were tested on the west coast of Korean peninsula and also on the shores around Jeju Island (black box location symbols), and the ScanSAR mode data (processed by CDPF after February 1, 1999) were on the East Sea of Korean peninsula (test sites are marked with small circles with numbers). At the Jeju Island study sites, both Standard mode and Fine mode RADARSAT data were tested at same locations. At all study sites in this paper, there are nearby
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meteorological stations which one can access and obtain the necessary information at the time of the RADARSAT data acquisition, except the Duck-Jeok Island area. In the DuckJeok Island study site (solid black square 3 in Fig. 4), in-situ buoy data were available to check and validate the RADARSAT results. The RADARSAT data used for this study included all Fine, Standard, and ScanSAR modes and the data acquisition parameters are summarized in Table 1. Table 2 compares the wind estimated using RADARSAT data with that observed by meteorological stations or buoys. Examples of estimating wind directions from the wave domain information are shown in Fig. 5. The wind direction estimated from SAR data is the direction, which forms the right angle with respect to the major axes of the wavenumber domain plots, which corresponds to the direction parallel to the atmospheric roll vortices or Langmuir circulations. Because these features are appeared to km-scale, the Fourier analysis was performed over a 25.6 25.6 km region of each beam mode data. Estimation of wind directions using the 2-D Fourier transform appears to work well for most study sites except the East Sea #1, #7 and Inchon 1, 2, where the estimated wind direction deviates greatly from the measured values (Fig. 6). The discrepancy may be explained
Fig. 5. Low wavenumber estimation of wind direction at the Uljin_F hFi (a), Jeju_1 [4] (b), East Sea #1 (c), and East Sea #2 (d).
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Fig. 6. Error (difference) between the SAR-derived wind direction and the measured wind direction. Study sites are listed under each histogram. For actual location of each site, please see Fig. 4.
as follow. The wind direction estimation is based upon the orientation of the kilometer-scale sea surface features such as wind-driven Langmuir circulation or atmospheric roll vortices. It becomes therefore very difficult to extract precise wind direction if the wind speed is very low or when stratification is stable. We can thus infer that the estimation of wind directions agrees well with the observed wind directions usually when the wind speed is greater than approximately 3 – 4 m/s. The wind speed computed using CMOD_4 and CMOD_IFR2 with different polarization models and observed wind speed values, including the in-situ buoy measurements
from nearby Duck-Jeok Island, are summarized in Table 3. For the estimation of wind speed map, the radar cross section of each subimage is averaged to 1.6 1.6 km region, and the calculated wind speeds from these radar cross-section values are averaged to 12.8 12.8 km region for all beam modes. In the Duck-Jeok Island area, it was difficult to estimate correct wind direction from the SAR data, because of the topographic effects of the Duck-Jeok Island, which could certainly have affected the natural processes of the air – sea coupling. Therefore, the wind direction parameter for the CMOD models was read out in this particular case from the buoy data. The wind speed
Table 3 Table of estimated wind speed results obtained from the RADARSAT data and the observed value at nearby Korean meteorological office stations (KMOS) or buoy Estimated data [wind speed (m/s)]
Satellite RADARSAT
ScanSAR
Standard
Fine
CMOD_4
CMOD_IFR2
Test site
Observed data [wind speed (m/s)]
T
K
E
T
K
E
East Sea #1 East Sea #2 East Sea #3 East Sea #4 East Sea #5 East Sea #6 East Sea #7 East Sea #8 Inchon1 [1] Inchon2 [2] Duck-Jeok Island [3] Jeju1 [4] Jeju2 [5] Jeju_A hAi Jeju_B hBi Jeju_C hCi Uljin_D hDi Uljin_E hEi Uljin_F hFi
3.4 8.5 5.7 2.6 3.4 6.0 2.3 6.3 2.3 2.3 4.0 3.7 7.0 5.2 5.2 5.0 4.8 4.5 4.0
3.1 6.1 4.8 3.5 3.9 5.6 4.1 4.9 5.1 4.8 5.3 5.3 13.4 15.6 14.9 19.1 8.1 6.3 6.3
2.5 4.8 3.8 2.7 3.1 4.5 3.2 3.9 4.1 3.8 4.4 4.1 10.4 12.8 12.1 16.1 6.0 4.8 4.5
2.4 4.0 3.6 2.6 3.0 4.2 3.0 3.7 3.4 3.2 3.8 4.3 10.8 12.0 11.4 15.3 5.8 4.7 4.4
3.0 6.5 5.5 2.7 3.5 5.7 4.2 4.9 5.1 4.5 5.3 6.2 13.9 16.4 15.7 18.7 8.4 6.7 7.1
2.1 4.7 4.1 1.4 2.3 4.2 2.8 3.6 3.6 3.0 4.2 4.5 11.2 13.8 13.1 16.1 6.3 4.9 4.5
2.0 3.5 3.7 1.2 1.9 3.9 2.5 2.5 2.5 2.1 3.3 4.9 11.5 12.9 12.4 15.4 6.1 4.8 4.3
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results obtained applying the Thompson polarization model (T) tend to be larger than the real measured values while the results obtained using the Kirchhoff polarization model (K) and Elfouhaily polarization model (E) are very close to the measured wind speed values for both CMOD models. Vachon and Dobson (2000) also obtained abnormally high wind speed results with Thompson polarization model and similar trends from their experiment on the East Coast of Canada. Although there can be some variations depending on the incidence angle and other unknown factors, the wind speed results obtained with the CMOD_4 model and the
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Kirchhoff or Elfouhaily polarization model and the wind speed results estimated with the CMOD_IFR2 and the Kirchhoff polarization mode are very close to the observed wind speed values. The wind speed results obtained from the RADARSAT Fine beam data at Uljin study sites are very close to the measured speeds, whereas the results obtained from the Jeju test data are considerably higher than the actual values. The cause of this apparent discrepancy most likely originates from the ADC saturation problem. The RADARSAT system uses a 4-bit ADC and dynamic gain.
Fig. 7. Error (difference) between the extracted wind speed (using CMOD_4 model) and the measured wind speed. (a) Kirchhoff (b) Elfouhaily, (c) Thompson polarization ratio model. Study location code is listed under each site error histogram (see Fig. 4).
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Since the gain setting is dynamic and based on the information of the near-half swath, the extracted wind speed from RADARSAT data can cause erroneous results (Vachon et al., 1997). The wind speed estimated from the RADARSAT Standard mode data are generally higher than the actual values at nearby meteorological observation stations, regardless of the CMOD model or polarization model used (Fig. 7). Some of the discrepancies being discussed here might have been introduced by various coastal topographic effects because most of the study sites in this paper are located close to coastal areas. If however, one accepts that the wind speed estimates obtained in this study are reasonable ones,
the Thompson polarization ratio model results in considerably larger values than the observed ones, whereas Kirchhoff and Elfouhaily polarization ratio models produce more realistic and acceptable values. At the two study sites (Inchon 1 and 2) on the west coast, the wind speed values estimated using Elfouhaily polarization ratio model with the CMOD_IFR2 algorithm produced results very close to the measured values. Here, it may be useful to notice that the RADARSAT Standard mode data were acquired with incidence angles 34– 40 and the wind speed estimated was < 3 m/s (Fig. 3). However, the combination of Kirchhoff polarization ratio model and the CMOD_4 algorithm estimated the wind speed values closest to the measured
Fig. 8. Wind vector distribution estimation in East Coast of Korea estimated from RADARSAT ScanSAR data (ScanSAR data acquisition parameters: 1997/08/ 18, 21:20 Descending, SWB).
D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
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Table 4 The RMS errors between observed wind vector and estimated wind vector from RADARSAT data. The values in parenthesis (columns 5, 6, and 7) are RMS errors RMS error Wind speed
Satellite
Mode
Wind direction
Number of data points
Kirchhoff
Elfouhaily
Thompson
Number of data points
RADARSAT
ScanSAR Standard Fine All
39.9 61.5 7.7 41.9
8 4 4 16
1.84 1.86 6.20 (0.77) 3.80 (1.70)
2.13 1.84 5.66 (0.63) 3.59 (1.84)
1.28 3.44 8.37 (2.54) 5.09 (2.40)
8 5 6 (3) 19 (16)
values in Jeju Island study site. In this case, the RADARSAT Standard mode data used was S7 with incidence angles ranging 45– 49 and the wind speed was larger than 3 m/s (Fig. 3). In the case of ScanSAR data on East Sea of Korean peninsula, the extracted wind speeds are generally lower than the observed wind speeds at most sites. Especially, the extracted wind speed results using Elfouhaily polarization ratio model are somewhat under-estimated, although the ScanSAR images generated by the CDPF are radiometrically calibrated with an absolute accuracy ± 1.35 dB (Srivastava, Banik, Adamovic, & Gray, 1999). The wind vector distribution on the entire East Sea was also estimated from the ScanSAR mode data. The wind direction distribution on the entire East Sea was interpolated from east coast Korean Meteorological Observation Stations data (Fig. 4), and the wind speed at various locations were calculated from CMOD_4 model using Kirchhoff polarization ratio model. The wind vector distribution in the East Sea, computed from the RADARSAT ScanSAR data, is shown in Fig. 8, where the reddish color represents high wind speed, bluish color displays low wind speed, and the white arrows are the wind vectors. The white arrows represent the wind speed and direction at each respective location. We have so far compared the observed wind speed values at several Meteorological Observation Stations with the computed wind speeds using several different mode RADARSAT data, with several combinations of polarization ratio models and CMOD algorithms. Although the number and spatial distribution of test sites in this study are not statistically optimized, the RMS error associated with the wind vectors is estimated for the cases where the CMOD_4 algorithm is applied to various RADARSAT beam mode data (Table 4). The RMS error for the SARderived wind direction is about 42, and the wind speed RMS error using CMOD_4 algorithm is about 1.7 m/s (Kirchhoff), 1.84 m/s (Elfouhaily), and 2.4 m/s (Thompson polarization ratio model), respectively.
4. Conclusions The CMOD_4 and CMOD_IFR2 algorithms which we utilized in this study were originally developed for ERS-1 and ERS-2 scatterometers, which utilized the C-Band VV
polarization signal and require precise information on the backscattering coefficient (s0) of the SAR image data, accurate wind direction, and SAR antenna geometry. However, the RADARSAT whose data we are testing were acquired using the same C-Band signal but with HH polarization. For this reason, we needed a reliable C-Band microwave polarization model, which can establish a scientifically valid physical model between the CMOD models and the HH polarization scattering model for RADARSAT SAR data. Several polarization ratio models (e.g., Thompson, Kirchhoff, and Elfouhaily models) were first tested and applied to CMOD models for retrieval of accurate wind speed from RADARSAT (Fine, Standard, and ScanSAR modes) data. The wind vectors computed using several modes of RADARSAT data utilizing several combinations of CMOD algorithms and polarization models were compared with the Meteorological Observation Station data at each study site and they are close to the observed data, except the cases with unacceptable results. Because the RADARSAT system uses a 4-bit ADC and the dynamic gain setting is based on the information of the near-half swath, the extracted wind speed from RADARSAT data can cause erroneous results. If we use RADARSAT Standard mode data for wind speed retrieval, the wind speeds estimated using Kirchhoff or Elfouhaily model polarization ratios agree reasonably well with the observed wind speeds, especially at low wind speeds and under the 45 incidence angle situations (Inchon 1/2). CMOD_IFR2 algorithm using Elfouhaily polarization ratio model appears to work best in this case. On the other hand, CMOD_4 model and the Kirchhoff polarization ratio model appears to work better for situations with high wind speeds and incidence angles larger than 45 (Jeju Island 1/2). However, the estimation of wind speed from HH polarized RADARSAT data using Thompson polarization ratio model over-estimates the sea surface wind speed in all test sites. Wind speed extraction from RADARSAT ScanSAR mode data is generally lower than the observed wind speed, when the Elfouhaily polarization ratio model is applied. The conclusions of this study may be summarized as follow. First, the key problems encountered using RADARSAT data and CMOD algorithms for estimating the sea surface wind vector were the signal polarization difference, poorly understood sea surface condition (e.g., short fetch)
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and footprint size of each RADARSAT imaging mode. The effects of signal polarization differences between HH and VV appear to be adequately resolved by the recently proposed polarization models: Thompson, Kirchhoff and Elfouhaily polarization models. However, further study is needed to develop the sea surface scattering model(s), which can satisfactorily explain and include both the sea surface conditions for the full range of wind speeds and the effect of the various sizes of RADARSAT footprints. Secondly, the Standard and ScanSAR mode data from RADARSAT can safely be used for routine estimation of coastal wind information. However, estimation of sea surface wind vector using the RADARSAT Fine mode data requires special care. The results of this study show that the wind direction estimated using Fine mode data are generally very accurate, while the wind speed computed from Fine mode data is consistently higher than the observed values and often erratic depending of the sea state. In all cases, the topographic effects must be carefully examined when the wind vector is estimated for the sites very close to shorelines. And thirdly, there appears no one optimum combination of one CMOD algorithm and one polarization model. The users of RADARSAT data for estimating sea surface wind vector should first experiment and find the right combination for the given sea state and the types of RADARSAT data acquisition mode.
Acknowledgments This research is funded by an NSERC of Canada Operating Grant to W.M. Moon (#7400). This research is also partially funded by BK 21 program through the School of Earth and Environmental Science. Some of the RADARSAT data were provided by the Canadian Space Agency through ADRO program #493. Authors would like to thank Dr. Paris Vachon (CCRS) who provided us with timely advices during several stages of this research. We also thank Korean Meteorological Administration for the weather and wind information for this research. D.J. Kim was supported by the BK21 program through the School of Earth and Environmental Sciences (SEES), Seoul National University. Authors would like to thank the anonymous reviewers whose detailed comments greatly improved the paper.
References Curlander, J. C., & McDonough, R. N. (1991). Synthetic aperture radar systems and signal processing. New York: Wiley. Elfouhaily, T., Thompson, D. R., Vandemark, D., & Chapron, B. (1999). A
new bistatic model for electromagnetic scattering from perfectly conducting random surfaces. Waves in Random Media, 9 (3), 281 – 294. Gerling, T. W. (1986). Structure of the surface wind field from Seasat SAR. Journal of Geophysical Research, 91 (C2), 2308 – 2320. IFREMER-CERSAT. (1999). Off-line wind scatterometer ERS products: user manual. Technical Report C2-MUT-W-01-IF, Version 2.1, IFREMER-CERSAT. Kim, D. J., & Moon, W. M. (2000). Estimation of sea surface wind vector in coastal areas using RADARSAT data. IGARSS’2000, Honolulu, HI, July, 2000. Korsbakken, E., Johannessen, J. A., & Johannessen, O. M. (1998). Coastal wind field retrievals from ERS synthetic aperture radar images. Journal of Geophysical Research, 103 (C4), 7857 – 7874. Leibovich, S. (1983). The form and dynamics of Langmuir circulations. Journal of Fluid Mechanics, 15, 391 – 427. LeMone, M. A. (1973). The structure and dynamics of horizontal roll vortices in the planetary boundary layer. Journal of the Atmospheric Sciences, 30, 1077 – 1091. Mastin, G. A., Harlow, C. A., Huh, O. K., & Hsu, S. A. (1985). Methods of obtaining offshore wind direction and sea-state data from X-band aircraft SAR imagery of coastal waters. IEEE Journal of Oceanic Engineering, OE-10, 84 – 113. Shuchman, R., Johannessen, J., & Rufenach, C. (1994). Determination of wind speed wind direction and atmospheric structure using ERS-1 SAR data during NORCSEX’91. International Geoscience and Remote Sensing Symposium 1994, 537 – 539 (In. Geosci. Remote Sens. Symp.). Srivastava, S., & Shepherd, N. (1998). Extraction of beta naught and sigma naught from RADARSAT CDPF products. Rev.2-22 May. Contract No.: 9F005-6-0025/001/SN. Srivastava, S. K., Banik, B. T., Adamovic, M., & Gray, R. (1999). Maintaining image quality and calibration of RADARSAT-1 CDPF products. International Geoscience and Remote Sensing Symposium 1999, 443 – 445 (In. Geosci. Remote Sens. Symp.). Stoffelen, A., & Anderson, D. (1993). ERS-1 scatterometer data characteristics and wind retrieval skill. Proceeding of First ERS-1 Symposium, ESA SP-359. Thompson, D. R., & Beal, R. C. (1998). Mapping mesoscale and submesoscale wind fields using synthetic aperture radar. International Geoscience and Remote Sensing Symposium 1998, 1382 – 1384 (In. Geosci. Remote Sens. Symp.). Thompson, D. R., Elfouhaily, T. M., & Chapron, B. (1998). Polarization ratio for microwave backscattering from the ocean surface at low to moderate incidence angles. International Geoscience and Remote Sensing Symposium 1998, 1671 – 1673 (In. Geosci. Remote Sens. Symp.). Ulaby, F. T., & Dobson, M. C. (1989). Handbook of radar scattering statistics for terrain. Norwood, MA: Artech House Inc. Vachon, P. W., Campbell, J. W. M., & Dobson, F. W. (1997). Comparison of ERS and RADARSAT SARs for wind and wave measurement. Proc. 3rd ERS Symp., ESA SP-414 (3 vols.). Vachon, P. W., & Dobson, F. W. (1996). Validation of wind vector retrieval from ERS-1 SAR images over the ocean. Global Atmosphere and Ocean System, 5, 177 – 187. Vachon, P. W., & Dobson, F. W. (2000). Wind retrieval from RADARSAT SAR images: selection of a suitable C-band HH polarization wind retrieval model. Canadian Journal of Remote Sensing, 26 (4), 306 – 313. Wackerman, C. C., Rufenach, C. L., Shuchman, R. A., Johannessen, J. A., & Davidson, K. L. (1996). Wind vector retrieval using ERS-1 synthetic aperture radar imagery. IEEE Transactions on Geoscience and Remote Sensing, 34 (6), 1343 – 1352. Wismann, V. (1992). A C-band wind scatterometer model derived from the data obtained during the ERS-1 calibration/validation campaign. Proc. First ERS-1 Symp.
Estimation of sea surface wind vector using RADARSAT data Duk-jin Kima, Wooil M. Moona,b,* a
ESI3 Laboratory, School of Earth and Environmental Science, Seoul National University, Seoul 151-742, South Korea b Geophysics, University of Manitoba-Geophysics, Winnipeg, Manitoba, Canada R3T 2N2 Received 18 April 2000; received in revised form 13 June 2001; accepted 13 June 2001
Abstract Synthetic aperture radars (SAR) have higher spatial resolution than scatterometers and we can obtain more detailed wind vector information from space-borne SAR data. This type of high spatial resolution wind information can be very useful particularly in coastal regions, where the scatterometer wind information can be altered by the coastal effects because the larger footprint spatial averaging of backscattered energy is unavoidable. This paper tested RADARSAT data with CMOD_4 and CMOD_IFR2 algorithms for extracting wind information over selected coastal test areas around the Korean peninsula. Because the CMOD algorithms were originally developed for the C-band, VV-Polarized scatterometer data, we applied currently available polarization ratio models (e.g., Kirchhoff, Elfouhaily, and Thompson models) for RADARSAT data processing. Three test areas, the West Sea and East Sea on both sides of the Korea peninsula and the open sea off Jeju Island, were tested with the RADARSAT Fine, Standard, and ScanSAR beam mode data. The correlation of the wind vector results with the buoy and meteorological station data agrees well with each other but with some variations. The RMS error for the SAR-derived wind direction is somewhat great, and the wind speed RMS error using CMOD_4 algorithm is about 1.7 m/s (Kirchhoff), 1.84 m/s (Elfouhaily), and 2.4 m/s (Thompson polarization ratio model), respectively. D 2002 Elsevier Science Inc. All rights reserved. Keywords: RADARSAT; Wind vector; Polarization ratio models; RADARSAT beam modes
1. Introduction Synthetic aperture radar (SAR) data have recently been increasingly used in various earth observation applications. Since the launching of ERS-1, there have been numerous attempts to estimate wind vectors from SAR data over water-covered areas. Previously, wind vector information over the ocean were usually obtained using scatterometer data or from in-situ measurements. Scatterometers on ERS1/2 measure the wind vector with a spatial resolution of 50 50 km and accuracies of ± 2 m/s in wind speed and ± 20 in wind direction (Wismann, 1992). But because of the low spatial resolution of the scatterometer data, wind vector information in coastal regions or near coastal areas is difficult to estimate accurately using these sensors. SAR data over the ocean, however, have the potential of * Corresponding author. University of Manitoba-Geophysics, Winnipeg, Manitoba, Canada R3T 2N2. Tel.: +1-204-747-9833; fax: +1-204-4747623. E-mail addresses: [email protected] (D.-j Kim), [email protected], [email protected] (W.M. Moon).
providing wind information with finer spatial resolution when it becomes necessary. A number of investigations on estimating wind vectors over oceans have recently been reported by various groups such as Kim and Moon (2000), Korsbakken, Johannessen, and Johannessen (1998), Shuchman, Johannessen, and Rufenach (1994), Vachon, Campbell, and Dobson (1997), Vachon and Dobson (1996), and Wackerman, Rufenach, Shuchman, Johannessen, and Davidson (1996). While years of effort have been applied to tune the sea surface wind/backscatter model for scatterometer data, the current models for high resolution wind estimation using SAR data are relatively immature. In this paper, we demonstrate a technique to modify the CMOD models to be applicable to SAR data. Several polarization ratio models have been introduced in the literature for such modification. We applied and tested three polarization models to the CMOD_4 (Stoffelen & Anderson, 1993) and to the CMOD_IFR2 (IFREMER-CERSAT, 1999) models and estimate the wind speed from RADARSAT SAR data. The test areas include several high-resolution cells close to the Korean peninsula and surrounding seas.
0034-4257/01/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved. PII: S 0 0 3 4 - 4 2 5 7 ( 0 1 ) 0 0 2 6 7 - X
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In Section 2, an overview of wind retrieval is described based on the CMOD models. A technique is outlined to show how the CMOD models can be converted to the alternate polarization data of RADARSAT using simple functions of the incidence angle. Section 3 applies the models developed in Section 2 to SAR data. The results presented in this section demonstrate good wind estimation accuracy with SAR resolution capabilities. Section 4 presents the conclusions to be drawn from this study.
2. Wind vector extraction from RADARSAT data 2.1. Wind speed retrieval models There are several wind speed retrieval models available, but, in this paper, we have used two models, CMOD_4 (Stoffelen & Anderson, 1993) and CMOD_IFR2 (IFREMER-CERSAT, 1999), for estimating wind speed from RADARSAT data. The algorithm based on the CMOD_4 model was originally developed with three types of Earth observation data: the ERS-1 scatterometer data, the wind vectors from the European Centre for Medium Range Weather Forecasts (ECMWF) surface wind analysis, and the wind and wave information from the National Oceanic and Atmospheric Administration (NOAA) wind and wave buoys, respectively. The CMOD_IFR2 algorithm is used for postprocessing of ERS scatterometer data. A scatterometer wind field is usually estimated from three antenna measurements over open water. As the three possible measurements of backscattering coefficient from the fore-, mid-, and aft-beam do not have direct relationships, the backscattering coefficient is usually estimated through an indirect approach of inversion (Fig. 1). The effect of wind direction in the estimation of wind speed is important and a series of experiment was carried out. The variation of estimated wind speed as a function of
wind direction is shown in Fig. 2. In the case of HH polarized C-Band RADARSAT data, the s0 naught decreases as the incidence angle increases and the wind speed becomes more sensitive to the error in the wind direction input. 2.2. RADARSAT SAR calibration The radar cross section (s), which has units of area, characterizes the scattering strength of the target in the backscattering direction in the form of an effective area. In general, the radar cross-section of a given target is related to its shape and dielectric constant, the viewing geometry, and the wavelength (l) and polarization directions of the incident and scattered waves. The standard definition for s is in terms of the ratio of the scattered power density Irec = PtGts/(4pR2)2 measured at a distance R from the scatterer to the power density I(R) = PtGt/4pR2 of an incident wave. The Pt is the average transmitter power, and the Gt is the antenna gain. Thus (Eq. (1)), Irec s ¼ limR!1 4pR2 ð1Þ IðRÞ where the limit as R ! 1 is included to denote that the observation point is in the far-field region (Curlander & McDonough, 1991; Ulaby & Dobson, 1989). In the case of RADARSAT data, because a dynamic gain with a 4-bit ADC is used (Vachon et al., 1997), the relationship between ¯ digital number (DN) and averaged radar cross-section (s) is as follow (Eq. (2)): DN2 ¼ INT½A2 s A3
ð2Þ
where A2 (function of range) is the output scaling gain, A3 is the output scaling offset which is normally zero. The backscattering coefficient (or sigma naught) is defined as the backscattering cross section of a distributed target of horizontal area A, normalized with respect to A such that s0 = s/A. The Backscattering coefficient is usually expressed in decibel, which is given by (Eq. (3)): s0 ¼ b0 þ 10 log10 ðsinIÞ ðdBÞ
ð3Þ
where (Eq. (4)) b0 ¼ 10 log10 ½ðDN2 þ A3 Þ=A2 ðdBÞ
ð4Þ
where I is the incidence angle (Srivastava & Shepherd, 1998). 2.3. Estimation of wind direction
Fig. 1. ERS and RADARSAT beam modes. The ERS-1/2 have a fixed incidence angle and swath, but the RADARSAT SAR instrument able to adjust the incidence angle, coverage, and spatial resolution.
The precise wind direction information is necessary to estimate accurate wind speed using CMOD models. Under certain circumstances, it is possible to extract the wind direction directly form the SAR image. SAR imagery can contain kilometer-scale linear features due
D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
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Fig. 2. Estimated wind speed as a function of input wind direction.
to atmospheric roll vortices or Langmuir circulations. Roll vortices, or wind rows, are counter-rotation helical circulations in the planetary boundary layer (PBL) that are roughly aligned with the mean wind direction (Gerling, 1986; LeMone, 1973). Other linear features caused by either concentration of surfactants or Langmuir circulations are also observed in the SAR image data and these features aligned with the wind direction (Leibovich, 1983; Mastin, Harlow, Huh, & Hsu, 1985). Due to the periodicity of these features, we can estimate the wind direction utilizing a 2-D Fourier transforms. The periods are usually very long and the low-wavenumber energy of the features of interest will be very close to the origin in the wavenumber space. The peak orientation is nearly perpendicular to the measured wind direction in this case (Shuchman et al., 1994; Vachon & Dobson, 1996). But the estimated direction has a 180 ambiguity. This type of direction estimation using a 2-D Fourier transform is well known and is applied here.
are. As a preliminary way of dealing with this problem and estimating wind speeds, a relationship between the HH and VV polarization becomes necessary. Here, we present three models for ratios of the horizontal backscatter to the vertical backscatter. Recently, Thompson and Beal (1998) and Thompson, Elfouhaily, and Chapron (1998) derived an empirical expression for the polarization ratio to obtain an approx-
2.4. Polarization ratio The CMOD series algorithms were originally developed for the C-band, VV-Polarized microwave data such as ERS1/2 scatterometers. In the case of RADARSAT, the SAR system operates at C-band but with HH Polarization, and the CMOD series models cannot be directly used as they
Fig. 3. Polarization ratio (VV/HH) versus incidence angle.
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D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64 Table 2 Observed wind direction and estimated wind direction results Satellite
Fig. 4. Location map showing the study sites.
imate form for the HH polarization backscatter from RADARSAT. This hybrid expression has the form: sH 0 ¼
ð1:6 sin2 qÞ2 ð1:6 þ sin2 qÞ
sV ðU ; q; fÞ 2 0
ð5Þ
where U is the wind speed, q is the incidence angle, and f is the azimuth angle of the radar with respect to the wind direction. If the factor 1.6 is replaced by unity, this equation reduces to the Bragg scattering polarization ratio. Some theoretical polarization ratios have been summarized in the work of Elfouhaily, Thompson, Vandemark, and Chapron (1999). From this work, Vachon and Dobson (2000) deduce the C-band polarization ratio, Rp (VV/HH) (Eq. (6)).
Rp ¼
½1 þ 2tan2 ðqÞ 2 ½1 þ atan2 ðqÞ 2
½linear units
ð6Þ
Test site
RADARSAT ScanSAR East Sea #1 East Sea #2 East Sea #3 East Sea #4 East Sea #5 East Sea #6 East Sea #7 East Sea #8 Standard Inchon1 [1] Inchon2 [2] Duck-Jeok Island [3] Jeju1 [4] Jeju2 [5] Fine Jeju_A hAi Jeju_B hBi Jeju_C hCi Uljin_D hDi Uljin_E hEi Uljin_F hFi
Observed wind Estimated wind direction () direction () 264.6 198.2 333.9 328.9 32.6 13.0 303.0 68.3 340 340 282
198 227 334 58 358 48 5 88 252 236 NA
270 200 90 90 20 230 290 180
279 239 82 93 NA NA 281 171
This equation becomes same as Eq. (5) when a = 0.6 and the incidence angle is between 20 and 50 (Thompson et al., 1998) (Fig. 3). Vachon and Dobson called this relationship as Thompson model (T). They also called Kirchhoff scattering (K) when a = 1.0, and Elfouhaily scattering model (E) when following equation is used (Eq. (7)).
Rp ¼
½1 þ 2tan2 ðqÞ 2 ½1 þ 2sin2 ðqÞ 2
½linear units
ð7Þ
We will use the same name to estimate sea surface wind speed from the C-band HH polarized RADARSAT data (Fig. 3).
Table 1 RADARSAT data acquisition parameters Satellite RADARSAT
Test site
Date time (UTC)
Beam type
Config./Orient.
Product type
ScanSAR (large gray box)
East Sea #1 – #8
1997/08/18 21:20.07
SWB
DES./NORM.
SCW
Standard (black box)
Inchon 1 [1] Inchon 2 [2] Duck-Jeok Island [3] Jeju1 [4] Jeju2 [5]
1997/11/4 09:30.41
S4
ASC./NORM.
SGF
1998/02/19 21:31.58
S7
DES./NORM.
SGF
Jeju_A hAi Jeju_B hBi Jeju_C hCi Uljin_D hDi Uljin_E hEi Uljin_F hFi
1998/02/18 09:37.56
F2
ASC./NORM.
SGF
1999/11/25 09:26.22 2000/05/11 09:26.22 2000/06/04 09:26.12
F3 F3 F3
ASC./NORM. ASC./NORM. ASC./NORM.
SLC SLC SLC
Fine (diamond)
D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
3. Estimation of wind vector and discussions We have so far discussed the methods one can use for estimating wind vector from the C-band, HH polarized RADARSAT data using CMOD_4 or CMOD_IFR2. In the following, we actually calculated wind vectors for a selected study sites around the Korean peninsula and compared the results with the nearby Korean meteorological station data and/or buoy data. Each location of the study sites is shown in Fig. 4. As shown in Fig. 4, this study is focused on the coastal regions around the Korean peninsula. Fine mode data were tested on north and south shores of the Jeju Island and the shores along East Sea –Uljin area (diamond shape location symbols in Fig. 4). Standard mode data were tested on the west coast of Korean peninsula and also on the shores around Jeju Island (black box location symbols), and the ScanSAR mode data (processed by CDPF after February 1, 1999) were on the East Sea of Korean peninsula (test sites are marked with small circles with numbers). At the Jeju Island study sites, both Standard mode and Fine mode RADARSAT data were tested at same locations. At all study sites in this paper, there are nearby
59
meteorological stations which one can access and obtain the necessary information at the time of the RADARSAT data acquisition, except the Duck-Jeok Island area. In the DuckJeok Island study site (solid black square 3 in Fig. 4), in-situ buoy data were available to check and validate the RADARSAT results. The RADARSAT data used for this study included all Fine, Standard, and ScanSAR modes and the data acquisition parameters are summarized in Table 1. Table 2 compares the wind estimated using RADARSAT data with that observed by meteorological stations or buoys. Examples of estimating wind directions from the wave domain information are shown in Fig. 5. The wind direction estimated from SAR data is the direction, which forms the right angle with respect to the major axes of the wavenumber domain plots, which corresponds to the direction parallel to the atmospheric roll vortices or Langmuir circulations. Because these features are appeared to km-scale, the Fourier analysis was performed over a 25.6 25.6 km region of each beam mode data. Estimation of wind directions using the 2-D Fourier transform appears to work well for most study sites except the East Sea #1, #7 and Inchon 1, 2, where the estimated wind direction deviates greatly from the measured values (Fig. 6). The discrepancy may be explained
Fig. 5. Low wavenumber estimation of wind direction at the Uljin_F hFi (a), Jeju_1 [4] (b), East Sea #1 (c), and East Sea #2 (d).
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D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
Fig. 6. Error (difference) between the SAR-derived wind direction and the measured wind direction. Study sites are listed under each histogram. For actual location of each site, please see Fig. 4.
as follow. The wind direction estimation is based upon the orientation of the kilometer-scale sea surface features such as wind-driven Langmuir circulation or atmospheric roll vortices. It becomes therefore very difficult to extract precise wind direction if the wind speed is very low or when stratification is stable. We can thus infer that the estimation of wind directions agrees well with the observed wind directions usually when the wind speed is greater than approximately 3 – 4 m/s. The wind speed computed using CMOD_4 and CMOD_IFR2 with different polarization models and observed wind speed values, including the in-situ buoy measurements
from nearby Duck-Jeok Island, are summarized in Table 3. For the estimation of wind speed map, the radar cross section of each subimage is averaged to 1.6 1.6 km region, and the calculated wind speeds from these radar cross-section values are averaged to 12.8 12.8 km region for all beam modes. In the Duck-Jeok Island area, it was difficult to estimate correct wind direction from the SAR data, because of the topographic effects of the Duck-Jeok Island, which could certainly have affected the natural processes of the air – sea coupling. Therefore, the wind direction parameter for the CMOD models was read out in this particular case from the buoy data. The wind speed
Table 3 Table of estimated wind speed results obtained from the RADARSAT data and the observed value at nearby Korean meteorological office stations (KMOS) or buoy Estimated data [wind speed (m/s)]
Satellite RADARSAT
ScanSAR
Standard
Fine
CMOD_4
CMOD_IFR2
Test site
Observed data [wind speed (m/s)]
T
K
E
T
K
E
East Sea #1 East Sea #2 East Sea #3 East Sea #4 East Sea #5 East Sea #6 East Sea #7 East Sea #8 Inchon1 [1] Inchon2 [2] Duck-Jeok Island [3] Jeju1 [4] Jeju2 [5] Jeju_A hAi Jeju_B hBi Jeju_C hCi Uljin_D hDi Uljin_E hEi Uljin_F hFi
3.4 8.5 5.7 2.6 3.4 6.0 2.3 6.3 2.3 2.3 4.0 3.7 7.0 5.2 5.2 5.0 4.8 4.5 4.0
3.1 6.1 4.8 3.5 3.9 5.6 4.1 4.9 5.1 4.8 5.3 5.3 13.4 15.6 14.9 19.1 8.1 6.3 6.3
2.5 4.8 3.8 2.7 3.1 4.5 3.2 3.9 4.1 3.8 4.4 4.1 10.4 12.8 12.1 16.1 6.0 4.8 4.5
2.4 4.0 3.6 2.6 3.0 4.2 3.0 3.7 3.4 3.2 3.8 4.3 10.8 12.0 11.4 15.3 5.8 4.7 4.4
3.0 6.5 5.5 2.7 3.5 5.7 4.2 4.9 5.1 4.5 5.3 6.2 13.9 16.4 15.7 18.7 8.4 6.7 7.1
2.1 4.7 4.1 1.4 2.3 4.2 2.8 3.6 3.6 3.0 4.2 4.5 11.2 13.8 13.1 16.1 6.3 4.9 4.5
2.0 3.5 3.7 1.2 1.9 3.9 2.5 2.5 2.5 2.1 3.3 4.9 11.5 12.9 12.4 15.4 6.1 4.8 4.3
D.-j Kim, W.M. Moon / Remote Sensing of Environment 80 (2002) 55–64
results obtained applying the Thompson polarization model (T) tend to be larger than the real measured values while the results obtained using the Kirchhoff polarization model (K) and Elfouhaily polarization model (E) are very close to the measured wind speed values for both CMOD models. Vachon and Dobson (2000) also obtained abnormally high wind speed results with Thompson polarization model and similar trends from their experiment on the East Coast of Canada. Although there can be some variations depending on the incidence angle and other unknown factors, the wind speed results obtained with the CMOD_4 model and the
61
Kirchhoff or Elfouhaily polarization model and the wind speed results estimated with the CMOD_IFR2 and the Kirchhoff polarization mode are very close to the observed wind speed values. The wind speed results obtained from the RADARSAT Fine beam data at Uljin study sites are very close to the measured speeds, whereas the results obtained from the Jeju test data are considerably higher than the actual values. The cause of this apparent discrepancy most likely originates from the ADC saturation problem. The RADARSAT system uses a 4-bit ADC and dynamic gain.
Fig. 7. Error (difference) between the extracted wind speed (using CMOD_4 model) and the measured wind speed. (a) Kirchhoff (b) Elfouhaily, (c) Thompson polarization ratio model. Study location code is listed under each site error histogram (see Fig. 4).
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Since the gain setting is dynamic and based on the information of the near-half swath, the extracted wind speed from RADARSAT data can cause erroneous results (Vachon et al., 1997). The wind speed estimated from the RADARSAT Standard mode data are generally higher than the actual values at nearby meteorological observation stations, regardless of the CMOD model or polarization model used (Fig. 7). Some of the discrepancies being discussed here might have been introduced by various coastal topographic effects because most of the study sites in this paper are located close to coastal areas. If however, one accepts that the wind speed estimates obtained in this study are reasonable ones,
the Thompson polarization ratio model results in considerably larger values than the observed ones, whereas Kirchhoff and Elfouhaily polarization ratio models produce more realistic and acceptable values. At the two study sites (Inchon 1 and 2) on the west coast, the wind speed values estimated using Elfouhaily polarization ratio model with the CMOD_IFR2 algorithm produced results very close to the measured values. Here, it may be useful to notice that the RADARSAT Standard mode data were acquired with incidence angles 34– 40 and the wind speed estimated was < 3 m/s (Fig. 3). However, the combination of Kirchhoff polarization ratio model and the CMOD_4 algorithm estimated the wind speed values closest to the measured
Fig. 8. Wind vector distribution estimation in East Coast of Korea estimated from RADARSAT ScanSAR data (ScanSAR data acquisition parameters: 1997/08/ 18, 21:20 Descending, SWB).
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Table 4 The RMS errors between observed wind vector and estimated wind vector from RADARSAT data. The values in parenthesis (columns 5, 6, and 7) are RMS errors RMS error Wind speed
Satellite
Mode
Wind direction
Number of data points
Kirchhoff
Elfouhaily
Thompson
Number of data points
RADARSAT
ScanSAR Standard Fine All
39.9 61.5 7.7 41.9
8 4 4 16
1.84 1.86 6.20 (0.77) 3.80 (1.70)
2.13 1.84 5.66 (0.63) 3.59 (1.84)
1.28 3.44 8.37 (2.54) 5.09 (2.40)
8 5 6 (3) 19 (16)
values in Jeju Island study site. In this case, the RADARSAT Standard mode data used was S7 with incidence angles ranging 45– 49 and the wind speed was larger than 3 m/s (Fig. 3). In the case of ScanSAR data on East Sea of Korean peninsula, the extracted wind speeds are generally lower than the observed wind speeds at most sites. Especially, the extracted wind speed results using Elfouhaily polarization ratio model are somewhat under-estimated, although the ScanSAR images generated by the CDPF are radiometrically calibrated with an absolute accuracy ± 1.35 dB (Srivastava, Banik, Adamovic, & Gray, 1999). The wind vector distribution on the entire East Sea was also estimated from the ScanSAR mode data. The wind direction distribution on the entire East Sea was interpolated from east coast Korean Meteorological Observation Stations data (Fig. 4), and the wind speed at various locations were calculated from CMOD_4 model using Kirchhoff polarization ratio model. The wind vector distribution in the East Sea, computed from the RADARSAT ScanSAR data, is shown in Fig. 8, where the reddish color represents high wind speed, bluish color displays low wind speed, and the white arrows are the wind vectors. The white arrows represent the wind speed and direction at each respective location. We have so far compared the observed wind speed values at several Meteorological Observation Stations with the computed wind speeds using several different mode RADARSAT data, with several combinations of polarization ratio models and CMOD algorithms. Although the number and spatial distribution of test sites in this study are not statistically optimized, the RMS error associated with the wind vectors is estimated for the cases where the CMOD_4 algorithm is applied to various RADARSAT beam mode data (Table 4). The RMS error for the SARderived wind direction is about 42, and the wind speed RMS error using CMOD_4 algorithm is about 1.7 m/s (Kirchhoff), 1.84 m/s (Elfouhaily), and 2.4 m/s (Thompson polarization ratio model), respectively.
4. Conclusions The CMOD_4 and CMOD_IFR2 algorithms which we utilized in this study were originally developed for ERS-1 and ERS-2 scatterometers, which utilized the C-Band VV
polarization signal and require precise information on the backscattering coefficient (s0) of the SAR image data, accurate wind direction, and SAR antenna geometry. However, the RADARSAT whose data we are testing were acquired using the same C-Band signal but with HH polarization. For this reason, we needed a reliable C-Band microwave polarization model, which can establish a scientifically valid physical model between the CMOD models and the HH polarization scattering model for RADARSAT SAR data. Several polarization ratio models (e.g., Thompson, Kirchhoff, and Elfouhaily models) were first tested and applied to CMOD models for retrieval of accurate wind speed from RADARSAT (Fine, Standard, and ScanSAR modes) data. The wind vectors computed using several modes of RADARSAT data utilizing several combinations of CMOD algorithms and polarization models were compared with the Meteorological Observation Station data at each study site and they are close to the observed data, except the cases with unacceptable results. Because the RADARSAT system uses a 4-bit ADC and the dynamic gain setting is based on the information of the near-half swath, the extracted wind speed from RADARSAT data can cause erroneous results. If we use RADARSAT Standard mode data for wind speed retrieval, the wind speeds estimated using Kirchhoff or Elfouhaily model polarization ratios agree reasonably well with the observed wind speeds, especially at low wind speeds and under the 45 incidence angle situations (Inchon 1/2). CMOD_IFR2 algorithm using Elfouhaily polarization ratio model appears to work best in this case. On the other hand, CMOD_4 model and the Kirchhoff polarization ratio model appears to work better for situations with high wind speeds and incidence angles larger than 45 (Jeju Island 1/2). However, the estimation of wind speed from HH polarized RADARSAT data using Thompson polarization ratio model over-estimates the sea surface wind speed in all test sites. Wind speed extraction from RADARSAT ScanSAR mode data is generally lower than the observed wind speed, when the Elfouhaily polarization ratio model is applied. The conclusions of this study may be summarized as follow. First, the key problems encountered using RADARSAT data and CMOD algorithms for estimating the sea surface wind vector were the signal polarization difference, poorly understood sea surface condition (e.g., short fetch)
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and footprint size of each RADARSAT imaging mode. The effects of signal polarization differences between HH and VV appear to be adequately resolved by the recently proposed polarization models: Thompson, Kirchhoff and Elfouhaily polarization models. However, further study is needed to develop the sea surface scattering model(s), which can satisfactorily explain and include both the sea surface conditions for the full range of wind speeds and the effect of the various sizes of RADARSAT footprints. Secondly, the Standard and ScanSAR mode data from RADARSAT can safely be used for routine estimation of coastal wind information. However, estimation of sea surface wind vector using the RADARSAT Fine mode data requires special care. The results of this study show that the wind direction estimated using Fine mode data are generally very accurate, while the wind speed computed from Fine mode data is consistently higher than the observed values and often erratic depending of the sea state. In all cases, the topographic effects must be carefully examined when the wind vector is estimated for the sites very close to shorelines. And thirdly, there appears no one optimum combination of one CMOD algorithm and one polarization model. The users of RADARSAT data for estimating sea surface wind vector should first experiment and find the right combination for the given sea state and the types of RADARSAT data acquisition mode.
Acknowledgments This research is funded by an NSERC of Canada Operating Grant to W.M. Moon (#7400). This research is also partially funded by BK 21 program through the School of Earth and Environmental Science. Some of the RADARSAT data were provided by the Canadian Space Agency through ADRO program #493. Authors would like to thank Dr. Paris Vachon (CCRS) who provided us with timely advices during several stages of this research. We also thank Korean Meteorological Administration for the weather and wind information for this research. D.J. Kim was supported by the BK21 program through the School of Earth and Environmental Sciences (SEES), Seoul National University. Authors would like to thank the anonymous reviewers whose detailed comments greatly improved the paper.
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