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Complex ERS-2 SAR wave mode
Operational Oceanography: hnplementation at the European and Regional Scales edited by N.C. Flemming, S. Vallerga, N. Pinardi, H.W.A. Behrens, G. Manzella, D. Prandle, J.H. Stel 9 2002 Elsevier Science B.V. All rights reserved.
437
Complex ERS-2 SAR wave mode Susanne Lehner, Johannes Schulz-Stellenfleth, Birgit Sch~ittler, Helko Breit and Ilona Weinreich German Aerospace Center, Wessling, Germany
1. I N T R O D U C T I O N Flying over the ocean the synthetic aperture radar (SAR) aboard the European remote sensing satellite ERS-2 acquires SAR images of 10 km x 6 km size (imagettes) every 200 km along the orbit. Imagettes are operationally processed to image power spectra (SWA.UWA) by the European space agency (ESA). The present study is based on complex ERS-2 wave mode data, which are so far not available from ESA. Single look complex (SLC) imagettes were processed using the SAR research processor (BSAR) of the German Aerospace Center (DLR) [1]. This big effort was made in order to be prepared for the coming ENVISAT era, when SLC imagettes will be a standard ESA product. Compared to conventional SAR intensity images used so far, SLC imagettes are better suited for wind and wave measurements, because they contain a lot of additional information on sea surface motion. Recently developed multi-look techniques are applied to derive directional wave spectra [2], ice parameters [3], and wind speed [4], [5] on a global scale. Results are compared to buoy measurements [6], results of the wave model WAM, and the algorithm [7] for SAR wave spectra retrieval developed by the Max-Planck Institute for Meteorology (Hamburg). Furthermore the SLC imagettes allow to study surface features on a" global scale, e.g., statistical distribution of natural slicks, that can affect SAR and scatterometer (SCAT) derived parameters, especially wind.
2. WINDFIELDS Two methods, based on different wind wave interaction mechanism are used to derive wind speed from S AR images. 2.1. The CMOD's The first method is based on converting grey levels of SAR images into normalized calibrated radar cross-section (NCRS) using the ESA calibration algorithm [8]. Wind speed is derived using the semi empirical CMOD4 algorithm [9] and some additional information on wind direction. Usually, SAR images show distinct features like wind streaks or shadowing behind coasts from which the wind direction can be derived. CMOD was developed for the ERS scatterometer, but can also be used on SAR data as the SCAT and the SAR are both operating at C-Band [4]. Recently a retuning of the algorithm resulted in a newer version called CMOD5.
438
2.2. The Cross Correlation Algorithm (CCA) The high resolution of a SAR in flight direction is achieved by recording the Doppler history of each point scatterer. If the scatterers are not stationary during the integration time the Doppler history is disturbed and the scatterers appear misplaced and blurred in the image. The CCA wind speed algorithm is based on the fact that the random movement of the sea surface is dependent on local wind speed. This makes it possible to derive wind speeds from the image smearing in azimuth by computing the cross correlation between different looks. Making several assumptions about the underlying ocean wave spectrum and the SAR imaging process, the theoretical cross correlation function is given by the following expression [5]:
ACF(x) - e x The
wind
speed
is
calculated
by
-refitting
(1) this
model
to
the
measured
ACF.
Figure 1. top: Two imagettes from the Indian Ocean, acquired at 7.2 lat, 59.9 Ion near India (left), and at -54.3 lat, 40.1 lon near Antarctica (fight). bottom left: Respective cross correlations of 150 m and 350 m width, corresponding to wind speeds of 6 rrds (India) and 14 m/s (Antarctica). bott.o.m fight: CCA derived wind speeds measured passing the Indian Ocean from India to Antarctica.
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Figure 4. ERS-2 Imagettes showing ocean waves slicks and sea ice. Figure 1 shows two 5 km x I0 km SAR imagettes of the ocean surface in the Indian Ocean acquired on the 1.6.97. The left image shows the sea surface near India in low wind conditions of about 5 ms -1, a swell system can be observed. The fight image is taken near Antarctica in high wind conditions of about 14 ms -I. It can be seen that high wind speeds result in higher backscatter and due to a higher variance of the sea surface motion in a smearing of the image in azimuth direction. SAR wave mode yields the opportunity to cross validate the measurements on a global, continuous basis. The CCA does not need additional information on wind direction as input, but is very sensitive to surface features like slicks or long swell. Figure 4 shows imagettes of the ocean surface containing surface features and a sea ice imagette of an area that was not recognized by the scatterometer sea ice flaggino algorithm.
3. C O E F F I C I E N T O F VARIANCE As SAR image speckle is multiplicative Gaussian noise, the PDF of a single look SAR intensity image is negative exponential. This yields an expectation value of coefficient of variance (CVAR) equal to one, skewness of four and curtuosis of nine. If the image is not homogeneous, but shows surface features, the value of CVAR increases, which can be used as a texture parameter to classify surface features. Figure 3 shows the geographical distribution of CVAR. It can be observed that CVAR is close to one almost all over the ocean, but is higher in the areas of strong wind speed near Antarctica and for imagettes that show surface features. Over sea ice CVAR has a high dynamic range.
4. COMPARISON TO SCATTEROMETER WIND SPEED Figure 5 shows triple collocations of CCA derived wind speed versus scatterometer measurements. The colour-coding of the data points corresponds to the coefficient of variance. The regression line (solid) as well as the diagonal (dashed) are given. Differences between the ACC and SCAT wind speed measurements can be accommodated to the following reasons:
441
1. Usually CCA measurements are higher than SCAT derived wind speeds, especially in high wind speed conditions. Triple collocation with the coefficient of variance shows that in these cases the CVAR is high as well, due to additional structures like ocean waves. 2. Wrong sea ice flagging of SCAT data, this can happen in special sea ice conditions, an example is given in figure 1. These data points show up in figure 5 as CVAR greater 1.5, but low wind speed. 3. Sea surface slicks lead to higher CCA than SCAT measurements, which both are not in agreement with ground truth. As a conclusion it can be said that imagettes are very helpful for a more accurate analysis of scatterometer flagging and that CMOD4 SCAT measurements are in general more robust for wind speed measurements, when features contaminate the sea surface. As on the ENVISAT satellite no more scatterometer will be available, wind speed measurements will have to be derived from ASAR wave mode data. For this task more accurate methods to measure wind speed are under development [ 10].
Figure 5. Scatter plot of CCA derived wind speed versus scatterometer wind speed.
442
Figure 6. Real (left) and imaginary (fight) part of cross spectrum computed from ERS-2 imagette acquired near the west coast of Alaska (lat 42.97 lon 221.54) on June 1, 1997, 07:27 UTC together with the retrieved ocean wave spectrum.
5. CROSS SPECTRA Based on a nonlinear forward model, Hasselmann and Hasselmann developed an inversion algorithm [7], which calculates 2-d wave spectra from given spectra of SAR intensity images. One disadvantage of this algorithm is the need of a priori knowledge of the propagation direction of the different wave systems. This information has to be taken from wave models or buoys. A new idea is the use of complex SAR data to derive the wave direction by computing the cross spectrum of two looks which are separated in time by about 0.5 seconds [2]. The existing inversion algorithm converting image spectra to ocean wave spectra is extended in a consistent way to be used with cross spectra available from ENVISAT. Figure 6 shows real and imaginary part of a cross spectrum derived from a complex imagette near Alaska, together with the retrieved ocean wave spectrum. The imaginary part of the image spectrum was taken as first guess input into the inversion algorithm to derive the true ocean
443 wave spectrum. Figure 6 bottom right shows a frequency spectrum of a collocated NOAA buoy. Significant wave height and peak frequency are in very good agreement. The complex imagettes processed with BSAR are an important test data set to develop and validate ENVISAT ocean wave retrieval algorithms, which will be used by different meteorological institutes like the European Centre for Medium-Range Weather Forecast (ECMWF).
ACKNOWLEDGMENTS The authors would like to thank ESA for providing ERS-2 wave mode raw data in the framework of AO project AO3-D-192. The study was sponsored by the German Ministry of Education, Science, and Technology (BMBF) under contract 03F0165C.
REFERENCES
[1] [2]
[31 [4]
[5] [6]
[7] [8] [91
[10]
H. Breit, B. Schfittler and U. Steinbrecher, "A high precision workstation-based chirp scaling SAR processor", in Proceedings of the IGARSS97 conference, Singapore, 1997. G. Engen and H. Johnson, "SAR-ocean wave inversion using image cross spectra", IEEE Trans. Geosci. Rem. Sens., vol. 33, no. 5, pp. 1047-1056, 1995. J. Schulz-Stellenfleth and S. Lehner, "ERS SAR observations of ocean waves traveling into sea ice", submitted to J. Geophys. Res., 2000. S. Lehner, J. Horstmann, W. Koch and W. Rosenthal, "Mesoscale wind measurements using recalibrated ERS SAR images", J. Geophys. Res., vol. 103, pp. 7847-7856, 1998. V. Kerbaol, B. Chapron and P.W. Vachon, "Analysis of ERS-1/2 synthetic aperture radar wave mode imagettes", J. Geophys. Res., vol. 103, pp. 7833-7846, 1998. J. Schulz-Stellenfleth and S. Lehner, "Ocean wave imaging using an airborne single pass cross track interferometric SAR", IEEE Trans. Geosci. Rein. Sens., vol. 39, no. 1, pp. 38-44, 2001. K. Hasselmann and S. Hasselmann, "On the linear mapping of an ocean wave spectrum into a synthetic aperture radar image spectrum", J. Geophys. Res., vol. 96, pp. 10713-10729, 1991. H. Laur, P. Bally, P. Meadows, J. Sanchez, B. Schattler and E. Lopinto, "Derivation of the backscattering coefficient 2o in ESA ERS-1/2 SAR PRI data products", Technical Report, ESA, Issue 2, Rev. 1, 1996. A. Stoffelen and D. Anderson, "Characterization of ERS-1 scatterometer measurements and wind retrieval", in Proc. Second ERS-1 Symposium- Space at the Service of our Environment, Hamburg, Germany, ESA SP-361, "1993. G. Engen, K.A. Hogda and H. Johnson, "A new method for wind retrieval from SAR data", presented at the '98 CEOS Workshop, Estec, 1998.
437
Complex ERS-2 SAR wave mode Susanne Lehner, Johannes Schulz-Stellenfleth, Birgit Sch~ittler, Helko Breit and Ilona Weinreich German Aerospace Center, Wessling, Germany
1. I N T R O D U C T I O N Flying over the ocean the synthetic aperture radar (SAR) aboard the European remote sensing satellite ERS-2 acquires SAR images of 10 km x 6 km size (imagettes) every 200 km along the orbit. Imagettes are operationally processed to image power spectra (SWA.UWA) by the European space agency (ESA). The present study is based on complex ERS-2 wave mode data, which are so far not available from ESA. Single look complex (SLC) imagettes were processed using the SAR research processor (BSAR) of the German Aerospace Center (DLR) [1]. This big effort was made in order to be prepared for the coming ENVISAT era, when SLC imagettes will be a standard ESA product. Compared to conventional SAR intensity images used so far, SLC imagettes are better suited for wind and wave measurements, because they contain a lot of additional information on sea surface motion. Recently developed multi-look techniques are applied to derive directional wave spectra [2], ice parameters [3], and wind speed [4], [5] on a global scale. Results are compared to buoy measurements [6], results of the wave model WAM, and the algorithm [7] for SAR wave spectra retrieval developed by the Max-Planck Institute for Meteorology (Hamburg). Furthermore the SLC imagettes allow to study surface features on a" global scale, e.g., statistical distribution of natural slicks, that can affect SAR and scatterometer (SCAT) derived parameters, especially wind.
2. WINDFIELDS Two methods, based on different wind wave interaction mechanism are used to derive wind speed from S AR images. 2.1. The CMOD's The first method is based on converting grey levels of SAR images into normalized calibrated radar cross-section (NCRS) using the ESA calibration algorithm [8]. Wind speed is derived using the semi empirical CMOD4 algorithm [9] and some additional information on wind direction. Usually, SAR images show distinct features like wind streaks or shadowing behind coasts from which the wind direction can be derived. CMOD was developed for the ERS scatterometer, but can also be used on SAR data as the SCAT and the SAR are both operating at C-Band [4]. Recently a retuning of the algorithm resulted in a newer version called CMOD5.
438
2.2. The Cross Correlation Algorithm (CCA) The high resolution of a SAR in flight direction is achieved by recording the Doppler history of each point scatterer. If the scatterers are not stationary during the integration time the Doppler history is disturbed and the scatterers appear misplaced and blurred in the image. The CCA wind speed algorithm is based on the fact that the random movement of the sea surface is dependent on local wind speed. This makes it possible to derive wind speeds from the image smearing in azimuth by computing the cross correlation between different looks. Making several assumptions about the underlying ocean wave spectrum and the SAR imaging process, the theoretical cross correlation function is given by the following expression [5]:
ACF(x) - e x The
wind
speed
is
calculated
by
-refitting
(1) this
model
to
the
measured
ACF.
Figure 1. top: Two imagettes from the Indian Ocean, acquired at 7.2 lat, 59.9 Ion near India (left), and at -54.3 lat, 40.1 lon near Antarctica (fight). bottom left: Respective cross correlations of 150 m and 350 m width, corresponding to wind speeds of 6 rrds (India) and 14 m/s (Antarctica). bott.o.m fight: CCA derived wind speeds measured passing the Indian Ocean from India to Antarctica.
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440
Figure 4. ERS-2 Imagettes showing ocean waves slicks and sea ice. Figure 1 shows two 5 km x I0 km SAR imagettes of the ocean surface in the Indian Ocean acquired on the 1.6.97. The left image shows the sea surface near India in low wind conditions of about 5 ms -1, a swell system can be observed. The fight image is taken near Antarctica in high wind conditions of about 14 ms -I. It can be seen that high wind speeds result in higher backscatter and due to a higher variance of the sea surface motion in a smearing of the image in azimuth direction. SAR wave mode yields the opportunity to cross validate the measurements on a global, continuous basis. The CCA does not need additional information on wind direction as input, but is very sensitive to surface features like slicks or long swell. Figure 4 shows imagettes of the ocean surface containing surface features and a sea ice imagette of an area that was not recognized by the scatterometer sea ice flaggino algorithm.
3. C O E F F I C I E N T O F VARIANCE As SAR image speckle is multiplicative Gaussian noise, the PDF of a single look SAR intensity image is negative exponential. This yields an expectation value of coefficient of variance (CVAR) equal to one, skewness of four and curtuosis of nine. If the image is not homogeneous, but shows surface features, the value of CVAR increases, which can be used as a texture parameter to classify surface features. Figure 3 shows the geographical distribution of CVAR. It can be observed that CVAR is close to one almost all over the ocean, but is higher in the areas of strong wind speed near Antarctica and for imagettes that show surface features. Over sea ice CVAR has a high dynamic range.
4. COMPARISON TO SCATTEROMETER WIND SPEED Figure 5 shows triple collocations of CCA derived wind speed versus scatterometer measurements. The colour-coding of the data points corresponds to the coefficient of variance. The regression line (solid) as well as the diagonal (dashed) are given. Differences between the ACC and SCAT wind speed measurements can be accommodated to the following reasons:
441
1. Usually CCA measurements are higher than SCAT derived wind speeds, especially in high wind speed conditions. Triple collocation with the coefficient of variance shows that in these cases the CVAR is high as well, due to additional structures like ocean waves. 2. Wrong sea ice flagging of SCAT data, this can happen in special sea ice conditions, an example is given in figure 1. These data points show up in figure 5 as CVAR greater 1.5, but low wind speed. 3. Sea surface slicks lead to higher CCA than SCAT measurements, which both are not in agreement with ground truth. As a conclusion it can be said that imagettes are very helpful for a more accurate analysis of scatterometer flagging and that CMOD4 SCAT measurements are in general more robust for wind speed measurements, when features contaminate the sea surface. As on the ENVISAT satellite no more scatterometer will be available, wind speed measurements will have to be derived from ASAR wave mode data. For this task more accurate methods to measure wind speed are under development [ 10].
Figure 5. Scatter plot of CCA derived wind speed versus scatterometer wind speed.
442
Figure 6. Real (left) and imaginary (fight) part of cross spectrum computed from ERS-2 imagette acquired near the west coast of Alaska (lat 42.97 lon 221.54) on June 1, 1997, 07:27 UTC together with the retrieved ocean wave spectrum.
5. CROSS SPECTRA Based on a nonlinear forward model, Hasselmann and Hasselmann developed an inversion algorithm [7], which calculates 2-d wave spectra from given spectra of SAR intensity images. One disadvantage of this algorithm is the need of a priori knowledge of the propagation direction of the different wave systems. This information has to be taken from wave models or buoys. A new idea is the use of complex SAR data to derive the wave direction by computing the cross spectrum of two looks which are separated in time by about 0.5 seconds [2]. The existing inversion algorithm converting image spectra to ocean wave spectra is extended in a consistent way to be used with cross spectra available from ENVISAT. Figure 6 shows real and imaginary part of a cross spectrum derived from a complex imagette near Alaska, together with the retrieved ocean wave spectrum. The imaginary part of the image spectrum was taken as first guess input into the inversion algorithm to derive the true ocean
443 wave spectrum. Figure 6 bottom right shows a frequency spectrum of a collocated NOAA buoy. Significant wave height and peak frequency are in very good agreement. The complex imagettes processed with BSAR are an important test data set to develop and validate ENVISAT ocean wave retrieval algorithms, which will be used by different meteorological institutes like the European Centre for Medium-Range Weather Forecast (ECMWF).
ACKNOWLEDGMENTS The authors would like to thank ESA for providing ERS-2 wave mode raw data in the framework of AO project AO3-D-192. The study was sponsored by the German Ministry of Education, Science, and Technology (BMBF) under contract 03F0165C.
REFERENCES
[1] [2]
[31 [4]
[5] [6]
[7] [8] [91
[10]
H. Breit, B. Schfittler and U. Steinbrecher, "A high precision workstation-based chirp scaling SAR processor", in Proceedings of the IGARSS97 conference, Singapore, 1997. G. Engen and H. Johnson, "SAR-ocean wave inversion using image cross spectra", IEEE Trans. Geosci. Rem. Sens., vol. 33, no. 5, pp. 1047-1056, 1995. J. Schulz-Stellenfleth and S. Lehner, "ERS SAR observations of ocean waves traveling into sea ice", submitted to J. Geophys. Res., 2000. S. Lehner, J. Horstmann, W. Koch and W. Rosenthal, "Mesoscale wind measurements using recalibrated ERS SAR images", J. Geophys. Res., vol. 103, pp. 7847-7856, 1998. V. Kerbaol, B. Chapron and P.W. Vachon, "Analysis of ERS-1/2 synthetic aperture radar wave mode imagettes", J. Geophys. Res., vol. 103, pp. 7833-7846, 1998. J. Schulz-Stellenfleth and S. Lehner, "Ocean wave imaging using an airborne single pass cross track interferometric SAR", IEEE Trans. Geosci. Rein. Sens., vol. 39, no. 1, pp. 38-44, 2001. K. Hasselmann and S. Hasselmann, "On the linear mapping of an ocean wave spectrum into a synthetic aperture radar image spectrum", J. Geophys. Res., vol. 96, pp. 10713-10729, 1991. H. Laur, P. Bally, P. Meadows, J. Sanchez, B. Schattler and E. Lopinto, "Derivation of the backscattering coefficient 2o in ESA ERS-1/2 SAR PRI data products", Technical Report, ESA, Issue 2, Rev. 1, 1996. A. Stoffelen and D. Anderson, "Characterization of ERS-1 scatterometer measurements and wind retrieval", in Proc. Second ERS-1 Symposium- Space at the Service of our Environment, Hamburg, Germany, ESA SP-361, "1993. G. Engen, K.A. Hogda and H. Johnson, "A new method for wind retrieval from SAR data", presented at the '98 CEOS Workshop, Estec, 1998.