PRELIMINARY STUDY ON DEVELOPING AN L-BAND WIND RETRIEVAL MODEL FUNCTION USING ALOS/PALSAR Osamu Isoguchi, Masanobu Shimada Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA) 2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505, Japan Email:isoguchi.osamu@jaxa.jp ABSTRACT The relationship between ocean wind vectors and L- band normalized radar cross sections (NRCS) is examined using the Phased-Array L-band Synthetic Aperture Radar (PALSAR). We used PALSAR ScanSAR images with a wide range of incidence angles from 17º to 43º. More than 6,000 match-ups, each consisting of the NRCS, incidence angles, wind speeds and wind directions, were collected. The NRCS exhibits a power-law relationship with respect to the wind speed. The coefficients of the power law can be derived as a function of the incidence angles. Based on this relation, the wind speeds are then inversely estimated from the NRCS and the incidence angles. A comparison with the truth winds reveals -0.68m/s bias and 2.99m/s rms error, demonstrating the feasibility of L-band scatterometry. Collecting more match-ups and further considering wind-direction dependence, which was not included in this study, would lead to the derivation of a robust L-band model function. 1. INTRODUCTION Wind fields are very important to human activity. They are modified drastically in coastal areas, where sharp variations in heat, moisture, and momentum transfers as well as elevation occur between land and sea. Although the behavior of coastal wind fields has an immediate effect on social activities such as marine traffic and leisure, our understanding of these phenomena is inadequate due to the difficulty of conducting in-situ observations over the coastal seas with sufficient temporal and spatial resolution. Synthetic Aperture Radar (SAR) can provide highspatial-resolution images of ocean-surface roughness. It can be used to estimate wind speed even in coastal areas. Wind stress variations on the sea surface modulate the roughness, changing the backscattered radar power, usually expressed as normalized radar cross sections (NRCS). An empirical relation between NRCS and wind speeds known as the geophysical model function (GMF) has been established using scatterometermeasured NRCS and buoy or model winds [1] [2] [3]. SAR images can thus be used to resolve high-resolution wind fields by applying the model functions. Recently, SAR-measured NRCS have been directly compared with buoy and scatterometer-estimated winds to establish an L-band model function [4]. However, it does not take account of incidence angle dependency due to constraints on JERS-1 operation. In January 2006, the Japan Aerospace Exploration Agency (JAXA) launched the Advanced Land Observing Satellite (ALOS), which carries the Phased-Array L-band Synthetic Aperture Radar (PALSAR). PALSAR can observe the Earth with various off-nadir angles and has a ScanSAR function that enabled us to investigate incidence angle dependency and to develop an L-band model function. In this study, we investigate the relationship between NRCS from PALSAR and co-located wind data. 2. DATA AND METHOD We used PALSAR data observed with the ScanSAR HH mode from April to December 2006. The strip slantrange images produced by the Σ-SAR processor [5] cover a 350km swath with incidence angles ranging from 17º to 43º. The digital numbers (DN) of the PALSAR amplitude images are converted to NRCS by applying a conversion factor (CF) of -83 db. o 2 σ = 10 log 10 DN + CF (1) For truth winds, we used two types of products, depending on the time period: QSCAT/NCEP Blended Ocean Winds from Colorado Research Associates for April-June 2006, and the Center for Ocean-Atmospheric Prediction Studies (COAPS) QuikSCAT Global Daily Pseudo-Stress Vectors for July-November 2006. The QSCAT/NCEP are ocean-surface wind data derived by spatial blending of high-resolution satellite data (from the Seawinds instrument on the QuickSCAT satellite) and global weather center analyses (NCEP). The analyses have global coverage with high temporal and spatial resolutions (6-hourly, and 0.5 x 0.5 degree) [6]. The COAPS winds are produced using a variational approach (direct minimization) with tuning parameters determined using Generalized Cross-Validation (GCV) [7]. These products are on a 1 1 grid. We made a match-up data set consisting of the PALSAR NRCS, incidence angles, wind speeds, and relative wind directions from the PALSAR and co-located wind data. The relative wind direction represents the difference between the wind direction and the PALSAR beam angle. The wind speed and direction are linearly interpolated using the 6-hourly data pair acquired before and after the PALSAR observation. Data having Proc. Envisat Symposium 2007, Montreux, Switzerland 23 27 April 2007 (ESA SP-636, July 2007)
changes in wind speeds > 2m/s or in wind directions > 20 o during the 6-hour period are eliminated. Fig. 1(a) is a PALSAR ScanSAR image acquired around Hokkaido, Japan, at 1247 UTC 8 June 2006, upon which the QSCAT/NCEP winds at 1200 UTC have been superimposed. The latitude-longitude coordinate in Fig. 1(a) is converted into a satellite ground track (azimuth-range) coordinate (Fig. 1(b)). In this coordinate system, incidence angles are a function of range addresses. The NRCS are calculated for a region of about 5 5km centered at the wind observation points. Data taken within 50km of the coast were eliminated to avoid land contamination. In total, 6,015 match-ups were collected. Fig. 2 presents histograms of the wind speeds and directions. Currently, we cannot collect sufficient data to establish a robust GMF, especially in strong wind ranges (> 10m/s) and in crosswind directions (90º and 270º). Figure 2. Histograms of match-up data with respect to (a) wind-speed bins and (b) wind-direction bins. 3. RESULTS Fiure 1. (a) PALSAR ScanSAR geo-coded image acquired around Hokkaido, Japan, at 1247 UTC 8 June 2006, with QSCAT/NCEP winds at 1200 UTC superimposed. (b) Same as (a) except for a satellite ground track (azimuth-range) coordinate. The GMF of NRCS over the ocean is generally expressed as σº = A 0 (1+A 1 cosφ+a 2 cos2φ), (2) where φ is the wind direction and A n s are functions of the wind speed and incidence angle. In order to investigate the dependency of NRCS on wind speed, wind direction, and incidence angle, the matchups were classified into bins of 1m/s wind speed, 11.25º wind direction, and 5.2º incidence angle. Any data whose deviation exceeded 3σ was excluded. The mean and standard deviation were then calculated for each bin whose final count was larger than 10. The A 0 coefficient in Eq. 2 generally indicates a power law of A 0 =kw a, where W is the wind speed. Assuming A 1 =0, we calculated a modified NRCS (σº/(1+a 2 cos2φ)), normalized with respect to the wind directions and their average, for each wind-speed and incidence-angle bin. We then derived wind-speed coefficients (a) and backscattered power coefficients (b=10log 10 k), using the least-square method, for various A 2 s ranging from 0 to 0.2 at 0.05 intervals. The errors reached a minimum when A 2 ranged from 0 to 0.05, which is generally consistent with past results for L-band airborne SAR (A 2 =0.05±0.05) [8]. It seems that the wind-direction dependence of the L-band is not as significant as that of the c-band or the Ku-band. In the following sections, we
focus on a simple power law relationship (σº= kw a ) in which wind-direction dependence is not considered (A 1, A 2 =0). Only the incidence-angle dependency on the wind-speed (a) and backscattered power coefficients (b) was investigated. Figure 3. Scatter plots of the NRCS with respect to the incidence angle. The data are colored according to the wind-speed bin. First, the NRCS variation within a bin (5º width) due to incidence-angle dependency is modified by approximating the value at a central incidence angle. Fig. 3 presents scatter plots of the NRCS with respect to the incidence angle, colored depending on the wind speed. The NRCS in each wind-speed bin decreases in a roughly linear fashion with the incidence angle. In addition, they increase stepwise with the wind speed. However, those in the lower wind range (< 3m/s) are scattered off the mode toward the low-nrcs direction. Here, we modify the NRCS by assuming a constant slope for all wind-speed and incidence-angle ranges. Linear approximation formulas for each wind range are calculated. Because their slopes converge at about -0.62 for wind speeds of 4 to 14m/s, the NRCSs are modified based on their mean (A=-0.628): σº =σº +A(θ-θ o ), (3) where σº is the modified NRCS, θ is the incidence angle, and θ o is the central incidence angle at each bin. The mean and standard deviation of the modified NRCS were calculated for each incidence-angle and windspeed bin and are plotted with respect to wind speeds in Fig. 4. The x-axis for wind speed is logarithmic. The means increase linearly with the logarithm of wind speed, revealing a roughly power-law relationship, although the anomalies in the relations and the standard deviations are large for wind speeds lower than 3m/s. Also indicated is their step-like decrease with respect to the incidence angle. Linear approximation formulas were derived from the data for wind speeds greater than 3m/s and are superimposed on the plots. Figure 4. Plots of the mean NRCS with respect to wind speed, calculated for each incidence angle and windspeed bin. The x-axis for the wind speed is logarithmic. Linear approximations for each incidence-angle bin are superimposed. Figure 5. (a) Wind-speed and (b) back-scattered power coefficients with respect to the incidence angle. Coefficients for the c-band and Ku-band are superimposed.
result was derived based on assumptions of no winddirection dependency and no wind-speed dependency for the wind-speed coefficient (a). However, a close look at Fig. 4 reveals the wind-speed dependence of the power law: the slopes (i.e. the wind speed coefficients (a)) seem to change around 8m/s. In addition, a relatively clear wind-direction dependence was confirmed for large incidence angles and high windspeed ranges (not shown). Currently, there are not enough match-ups, especially for high winds and crosswinds, to take these dependencies into account. We plan to conduct detailed derivations involving these effects after collecting more match-ups. 4. SUMMARY Figure 6. Histograms of scattering diagram relating the truth and SAR-estimated wind speeds. The wind-speed and backscattered power coefficients are derived from the formulas for each incidence-angle bin (Fig. 5). Those for the c- and Ku-bands, based on existing GMFs [2][3], are overlaid. The backscattered power coefficients decrease with the incidence angle. The decrease is smaller than those in the c- and Kubands, resulting in a smaller total dynamic range (from - 24 to -12dB). The wind-speed coefficients also decrease with the incidence angle, which is opposite of the results from the c- and Ku-band. The value of 0.9 at a 20º incidence angle is larger than that derived from the airborne SAR (0.5±0.1) [8]. Assuming no wind-speed dependency, the wind-speed (a) and backscattered power (b) coefficients were approximated by a third-degree equation involving incidence angles. Based on this derived relationship, we estimated the approximate wind speed from the NRCS and incidence angle as: W sar =10^((NRCS-b(θ))/(10 a(θ))), (4) where W sar is the SAR-derived speed and θ is the incidence angle. Fig. 6 presents histograms of the scattering diagram relating the truth and SAR-derived wind speeds. The comparison indicates -0.68m/s bias and 2.99m/s root mean square (rms) error. The result certainly suggests the feasibility of L-band scatterometry. However, the plot still exhibits relatively large deviations, with those derived by SAR tending to be underestimated in the low-speed range (< 5m/s) and overestimated in the high-speed range (> 10m/s). This We investigated the relationship between wind fields and L-band NRCS, derived from the ALOS/PALSAR ScanSAR data, over the ocean. By co-registering the QuikSCAT-based winds onto the PALSAR images, more than 6,000 match-ups consisting of the NRCS, incidence angles, wind speeds, and wind directions were collected. This marks the first time that this kind of comprehensive investigation of L-band NRCS for ocean wind vectors has been conducted. Because no significant wind-direction dependency was found within the current match-ups, only the dependencies of the NRCS on wind speed and incidence angle were examined in this study. The plots with respect to wind speed roughly follow a power-law relationship, depending on the incidence angle. We derived the wind-speed and backscattered power coefficients as a function of the incidence angle. Wind speeds were inversely estimated from the NRCS and incidence angles of the match-ups based on this derived relationship. A comparison of the estimations with the truth winds indicates a bias of -0.68m/s and an rms error of 2.99m/s. This accuracy does not achieve the quality of real-world usage in the current manner. Further consideration regarding wind-direction and wind-speed dependency would improve the accuracy. The result indicates the feasibility of wind-field detection by an L-band SAR. It should be noted, however, that maintaining radiometric accuracy seems to be more critical for an L-band SAR than for a c-band SAR due to its narrower dynamic range for wind-speed variations. RERERENCES 1. Stoffelen, A. & Anderson, D. (1997). Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4. J. Geophys. Res. 102, 5767-5780.
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