CROSS SEA DETECTION BASED ON SYNTHETIC APERTURE RADAR (SAR) DATA AND NUMERICAL WAVE MODEL (WAM)

Xiao-Ming LI(1)(2), Susanne Lehner(1) , Ming-Xia HE(2)

Johannes Schulz-Stellenfleth(1)

(1)German Aerospace Centre (DLR), Oberpfaffenhofen, 82234 Wessling, Germany, Email: Xiao.Li@dlr.de (2)Ocean Remote Sensing Institute, Ocean University of China, Qingdao, 266003, China

ABSTRACT

The present paper is about the detection of cross seas based on ERS-2/SAR wave mode data and comparison to WAM model. A case of cross seas observed by ERS-2/SAR was analyzed for its generation and development by wave mode data together with the WAM model two-dimensional (2-D) spectra. The sea surface elevation is estimated from these wave mode data with cross sea features using a CMOD type tilt algorithm. An effective method to remove the speckle noise from SAR wave mode images is introduced, too, in order to get better results of sea surface elevation. 1. Introduction

It is well known that Synthetic Aperture Radar (SAR) provides directional ocean wave and surface wind information on a continuous and global scale [1]. Due to the high resolution of SAR data, it is possible to analyze the structure of the ocean wave field, such as wave groups and individual wave behavior. In the framework of the project WAVEALTAS, ESA provided a two-year’s ERS-2/SAR wave mode raw data set, which was reprocessed to single-look-complex SAR images at DLR using the BSAR processor [1]. The processed data set contains typically between 1300 and 1500 images of 10km by 5km size daily. 1.1 SAR ocean wave spectra algorithm Two types of ocean waves usually characterize the sea surface, namely wind sea and swell. The first refers to waves influenced by the local wind, the latter to waves that have propagated out of the generating area and are thus no longer affected by the local wind. A sea state

that is characterized by a wind sea and one or more swell systems is called a mixed sea or confused sea. If their directions differ, the sea state is called a cross sea. A cross sea case which occurred in the South-East Pacific was captured clearly by consecutive ERS-2 SAR wave mode images acquired on August 10, 2000 at 17:30 UTC. One of the consecutive imagettes is shown in Fig. 1. The case is studied using 2-D spectra, including the WAM model [2] spectra, cross spectra of SAR complex data [3] and the non-linear retrieved PARSA spectra [4]. By a simple travel kinematic model, generation and development of cross seas is demonstrated.

Figure 1. ERS-2/SAR wave mode data imaging a cross

sea, acquired on August 10, 2000 at 17:30 UTC Traditionally, SAR ocean wave measurements are carried out in the spectral domain to estimate the two-dimensional spectrum. One approach is realized in the MPI (Max-Planck Institute) scheme [5]. Later by making using of the SAR complex image, the cross spectra method was derived to retrieve ocean wave propagation directions without ambiguity [3]. This is the standard algorithm for ASAR wave mode data [6]. However, all these applications do not make use of full

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Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

information on the two-dimensional sea surface elevation field provided by SAR. 1.2 Sea Surface Elevation

Sea surface elevation is estimated from SAR wave mode imagettes, e.g. as done by the LISE algorithm developed by DLR [7]. The objective of this paper is to introduce a technique to estimate the sea surface elevation field from SAR wave mode by a CMOD type tilt algorithm (Wolfgang.Koch@gkss.de). It is well known that the geophysical model function CMOD describes the relationship between wind speed, wind direction, antenna look direction and incidence angle [8]. The incidence angle chosen for SAR wave mode images is set around 23◦. This does not take account into the tilt caused by the sea surface elevation. Although in steep wave situations, the change of tilt is significant. Given the wind speed U10 and wind direction and by using CMOD, thus the tilt angle of the individual ocean wave is estimated. This is the main idea of the approach introduced in the paper. 1.3 Speckle noise removing in SAR images

SAR images are generated by coherent processing of the scattered signals and they are highly susceptible to specking effects [9]. The presence of speckle in SAR image reduces the ability of information extraction, especially when the ratio of signal to noise is low over the ocean. It is observed that the energy due to the imaged ocean wave field is concentrated in narrow angular sectors of the image spectrum. Thus it is possible to choose a proper threshold to remove the speckle noise in the image by a Fourier Fast Transform (FFT) type of filter. The paper is structured as follows: in section 2, the data set used in this research is introduced. Section 3 is about the generation and development of the cross seas case. The technique of removing speckle noise and estimation of sea surface elevation on SAR wave mode data is introduced in the fourth section.

2. Data Set Description

For this study ERS-2 SAR images and ERA-40 model data are used. 2.1 ERS-2 SAR wave mode data

ERS SAR wave mode data is acquired over the ocean every 200 km along the satellite track with the coverage of 5km x 10km, when image mode data is not requested. A two-year wave mode dataset from ERS-2 SAR acquired during 1999 and 2000, which has been reprocessed to single-look-complex data at DLR [1]. 2.2 Model data

After the success of ERA-15, European Centre for Medium-Range Weather Forecasts (ECMWF) is performing their second reanalysis called ERA-40 [10], which covers 45 years, from 1957 until 2002. Starting 1991, wave height data obtained from the altimeters on board of ERS-1 and ERS-2 are assimilated into ERA-40 wave data. The Numerical wave model used for the research is the well-known WAM [2]. It is the so called the third generation model (cycle 4), in which the wave spectrum is computed by integration of the energy balance equation. The model resolution is chosen as 1◦ by 1◦ and forced by the ERA-40 high resolution wind field (1◦ by 1◦). 3. Observation of Cross Seas

The case of cross seas imaged by SAR occurred in the southeastern Pacific on Aug.10, 2000. An impressive pattern of crossing ocean wave systems is observed on at least 8 consecutive images, i.e. on a distance of more than 1000 km. The most distinct peaks can be observed on the image which is situated at 23.06S and 111.6W degrees as shown in Fig. 1. Three consecutive imagettes through the cross sea area are shown in Fig. 2. The hindcast WAM model spectra (upper row), observed cross spectra (middle row with wave traveling direction ambiguity removed by using the imaginary part of the cross spectrum) and retrieved PARSA spectra (lower row) are shown as well. It is

clear from these contour plots that the cross sea contains two distinct swell systems travelling to northeast and northwest. These are denoted as Sne and Snw hereby respectively. The two swell systems are most dominant in the last imagette, which is shown in Fig. 2. The PARSA retrieved Sne swell system peak wave length is about 400m, which agrees well with the observed cross spectral peak, but the WAM model hindcast result shows only about 300m. Gonzalez et al., [11] introduced a simple kinematical wave model which has shown that swell generally obeys linear wave theory of propagation and does not seem to be affected by propagating through zones of steady wind. With the simple kinematical model and swell group speed calculated from the WAM model result. It is estimated that Swell Sne was generated by a storm about 4000km away near to the Antarctic Continent and after travelled 96 hours it arrived at the observation point. Swell Sne was generated by an anti-clockwise low pressure about 2200km away and travelled about 54 hours to the observation point. In Fig. 4 swell travelling and cross sea generation is shown on a simplified map. The black dotted line shows the swell great circle route from the generation area (green triangle) to the observation at the imagette location (Black Square, 23.06S and 111.6W). The yellow lines indicate swell traveling distance during one day. The imaginary part of the SAR cross spectrum shows a strong peak of the swell system Snw as shown in Fig.3. This peak is underestimated in the WAM model. This is due to the fact that the input wind field from ERA-40 at the generation area of the swell was weak. This can be concluded from the comparison to the Quikscat wind field in Fig. 5. The maximum wind speed measured by Quikscat is about 30m/s larger than ERA-40 model result about 20m/s. For the Sne swell system, it can be observed that the energy contained on the system is largest in the imagette (the rightmost one Fig. 2) closest to the generation area and decreases northward along the orbit due to the swell dissipation.

As expected the PARSA inversion algorithm turns the peak of swell Sne towards the azimuth direction. In a next step the dissipation scheme of the WAM model will be compared to the inverted SAR spectra. 4. Estimation of Sea Surface Elevation

4.1 Technique of speckle noise removing Radar signals returned from the scatterers within the SAR resolution cell are added up coherently during the process, which makes images susceptible to speckle. Basically the speckle is signal-dependent and acts like multiplicative noise. Therefore, in the SAR image analysis, the multiplicative noise model is used [12]. Iσ = I *S (1)

In which, Iσ is SAR intensity, I is the cross section information and S is speckle noise. In the SAR one-dimension directional spectrum, the energy is concentrated in very small angular sectors. This property gives the possibility to remove the speckle and extract the useful information by setting a proper threshold. Before doing the (FFT), the logarithm is calculated. Thus the multiplicative speckle noise in SAR image is transformed into additive noise i.e. log(Iσ) = log(I)+log(S) (2) After the FFT, the speckle is still additive, i.e. F(log(Iσ)) = F( log(I))+ F(log(S)) (3) Using the proper thresholds, the speckle can be removed and the intensity image can be retrieved by inverse FFT. This method is denoted as LOG-FFT method. Fig. 6 shows the speckle reduced result of the second imagette shown in Fig.2. The filtered result shows that the speckle noise is reduced significantly. Two different swell systems can be observed clearly. 4.2 Technique of sea surface elevation estimation As mentioned above, the elevation of sea surface will cause radar cross section changes. Different tilt of the sea surface generates different local incidence angles in every pixel of the SAR image. The procedure to estimate the sea surface elevation is demonstrated in the following.

First, the normalized radar cross section (NRCS) σ0sim

of the imagette is simulated by using the collocated ERA-40 wind field data (wind speed U10 and wind direction ψ), the antenna look direction φ and the incidence angle θ is 23◦ in the CMOD5 function. The calibration constant applied for SAR wave mode images is taken to be -44.96dB [13]. Second, from the intensity value of pixels in imagettes, the observed σ0

obs measured by SAR is computed by using the calibration constant. The cost function J is defined as Eq.4

( ) ( ) ( )( 2

100100 ,,,,,, θϕψσαθϕψσα UUJ simobs −+= ) (4)

J(α) optimizes the angle α, the difference between local incidence angle due to tilt and 23◦. The minimum of the cost function corresponds to the best fit of local incidence angle. In a further step, the slope in every pixel from the local incidence angle is estimated. The sea surface elevation of SAR wave mode imagettes is obtained by integrating the slope in every pixel along the range direction. Fig. 6 shows the intensity value of pixels in the selected purple rectangular area of the filtered imagette shown in Fig. 5. Fig. 7 is the corresponding sea surface elevation result using the CMOD tilt algorithm. In a next step, this first result will be validated against buoy data and model results. Summary

A case of cross seas captured clearly by SAR is analyzed for swells generation and dissipation based on SAR wave mode data and WAM model. The comparison among WAM model spectra, cross spectra and PARSA non-linear inverted spectra is demonstrated. SAR measurements of sea state could be used to validate the quality of the wind field driving the WAM model. A CMOD type technique for sea surface elevation estimation is introduced. A LOG-FFT speckle noise reduction method used on ERS-2 SAR wave mode data is applied. It is effective for noise removal and extraction of information in

individual ocean waves properties (e.g. crest height and length). Acknowledgment The ERS-2/SAR wave mode raw data were kindly supplied by ESA in the framework of AO WAVEATLAS. We thank for the ECMWF make the ERA-40 data set available freely. Reference

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WAM spectra

Cross-spectra Module

PARSA-spectra

Figure 2. SAR Imagettes, WAM Model spectra (m4), cross spectra (m2), PARSA retrieved spectra (m4)

Figure 3. Cross spectra imaginary part of rightmost imagette shown in Fig. 2

Figure 4. Sketch map of cross sea system generation

Figure. 5 Wind fields of ERA-40 and Quikscat at

generation area of Swell Snw

Figure 5. Filtered result by LOG-FFT method of third imagette shown in Fig.2

Figure 6. Intensity value of pixels in Fig. 5 purple

rectangular area

Figure 7. Corresponding Sea surface elevation