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(1)SURFACE MONITORING OVER AGRICULTURAL AREA BY USING X-BAND DUAL-POLARIZATION SAR DATA Boyeol Yoon, Younsoo Kim, Hakjung Kim Satellite Information Research Institute Korea Aerospace Research Institute, 115 Gwahangno, Youseong, Daejeon, Korea (305-333) [email protected], [email protected], [email protected]. ABSTRACT: Study area is agricultural area at Java in Indonesia. This area has been composed mainly agricultural area and tidal area near by ocean. We use for study TerraSAR-X data was captured on December, 2007. The sensitivity of TerraSAR-X radar signals to surface soil parameters has been examined over agricultural fields, using HH and VV polarization. The difference observed in the X-band, between radar signals reflected by the roughest and smoothest areas. Our results are shown the sensitivity of TerraSAR-X signal to surface roughness in agricultural area. KEY WORDS: X band, SAR, Dual-polarization, backscattering coefficient.. 1. INTRODUCTION. SAR multi-polarization and multi-frequency data is suitable to land and ocean (oil spill, so on) monitoring in this days. As TerraSAR-X, CosmoSky-med X band SAR data is distributed to user in this days, X band SAR data application of area is more and more expanded. X band SAR data application is oriented by military and national security, but now apply for disaster monitoring as well as other application area. In these days the discussion on land cover areas and their monitoring has been brought up. Korea Land Spatialization Group has been performed variety programs for land cover fields since 2006. Multi-sensor image processing technique adapted for land cover in these days. The possibility of having a global view of an land cover area with a good resolution make spaceborne remote sensing imagery a valid instrument for land cover area mapping and settlement analysis. Moreover, periodical acquisition of images over the same area allows regular and continuous monitoring of a specific area over a certain number of years. Compared to optical sensors, SAR does not suffer from limitations due to cloud cover and darkness, therefore SAR imagery can be useful in land-oriented studies of any part of the Earth. X band SAR data has been widely used for several Earth related studies (topography, volcanology, forestry etc.). Nevertheless, not much attention has been paid to agricultural area has been used for classification purposes only. In this study we focused on the meaning of backscattering coefficient of agricultural area and performed several analyses in order to understand what causes polarization effects. Concerning agricultural areas, polarization information has been shown to be a fundamental parameter for situ. measurement purposes. However, the meaning of polarization information of a agricultural area has not been analyzed in depth; moreover, it has not been completely clarified what determines the decorrelation of man-made features. Previous results show that radar signals are strongly dependent on surface roughness, when the latter has small values. Baghdadi et al. (2002) have shown that high incidence angles (N45°) are best suited to the discrimination between smooth and rough areas, under which conditions it has been shown that the backscattered signal has an exponential dependence on surface roughness (e.g. Baghdadi et al., 2008). 2. METHODS 2.1. Study Area and Data sets. This study focuses on agricultural areas located in Java area. This area contained agricultural area, forest, urban area (Lat. S 7.4, Long E 122.7 for the center).. Figure 1. Study area.

(2) For this area we had a TerraSAR-X SAR images from December, 2007. TerraSAR-X image is processed the Geocoded Ellipsoid Corrected (GEC). Imaging mode is Spot light (SL) mode with pixel spacing is 1.5m in range direction and 1.5m azimuth. Image dimension is 10km in range and 10km in azimuth direction. The GEC product is a multi look detected product. It is projected and re-sampled to the WGS84 reference ellipsoid assuming one average terrain height. Available grid formats are UTM and UPS. As the ellipsoid correction does not consider a DEM, the pixel location accuracy varies due to the terrain. The accuracy measures provided in chapter 4 are valid for flat surfaces. For other types of relief, the terrain induced SAR specific distortions will not be corrected and significant differences can appear in particular for strong relief and steep incidence angles. Resolution mode is processed spatially enhanced (SE). The spatially enhanced product is designed for the highest possible square ground resolution. Depending on imaging mode, polarization and incidence angle the larger resolution value of azimuth or ground range determines the square pixel size. The smaller resolution value is adjusted to this size and the corresponding reduction of the bandwidth is used for speckle reduction (Fritz, T., Eineder, M., 2008).. 2.3. Sigma Naught (Radiometric Calibration). Backscattering from a target is influenced by the relative orientation of the illuminated resolution cell and the sensor, as well as by the distance in range between them. The derivation of Sigma Naught thus requires a detailed knowledge of the local slope (i.e. local incidence angle), as shown in (3) and (4) (Infoterra GmbH, 2008). s 0 = b 0 × sin q LOC. (3). Where: θloc is the local incidence angle. It is derived from the Geocoded Incidence Angle Mask (GIM) that is optional for the L1B Enhanced Ellipsoid Corrected (EEC) product ordering. s 0 dB = b 0 dB + 10log10 (sin q LOC ). (4). Figure 3 show the evolution of Sigma Naught backscattering coefficients from a scene.. Figure 3. Backscattering coefficient images (HH pol.: left, VV pol.:right). 3. RESULTS AND DISCUSSION. Figure 2. Data sets (HH pol.: left, VV pol.:right) Table 1. Main characteristics of TSX image in study area Date 22/DEC/2007. 2.2. Imaging mode Spot light. Inc. angle 36.5. Orbit Descending. Pol. HH,VV. Beta Naught Computation (Radar Brightness). The radar brightness β0 is derived from the image pixel values or digital numbers (DN) applying the calibration factor KS (1). b 0 = K s × DN. 2. (1). where: KS is the calibration and processor scaling factor given by the parameter calFactor in the annotated file. DN is the pixel intensity values, Equation (2) converts β0 to dB b 0 dB = 10glog10 ( b 0 ). (2). As we apply for Equation (1) and (2), in case of HH polarization Beta nought value distributes from -49.5dB to 40.7dB and mean value is -3.5dB. In case of VV polarization Beta nought value distributes from -49.5dB to 40.5dB and mean value is -4.3dB. Beta nought value of HH and VV polarization are shown a little difference. When we compare to backscattering coefficient, HH polarization backscattering coefficient distribute from 50.02dB to 40.70dB and mean value is -3.7dB. In case of VV polarization backscattering coefficient distribute from -51dB to 40.1dB and mean value is 6.5dB. Backscattering coefficient difference of HH and VV polarization are shown more variable than Beta nought values but difference is a little change..

(3) 4. CONCLUSIONS. This study presents the use of dual-polarization X band SAR images to analyze the characteristic agricultural area information and possibility. There are many factors effect to the backscattering coefficient and many error sources. Compared to optical sensors, SAR does not suffer from limitations due to cloud cover and darkness, therefore SAR imagery can be useful in land-oriented studies of any part of the Earth and polarization information can be used to classification in land cover mapping. In this study, we choose the three polarization bands and we perform the assign the RGB color composition(R: VV, G: VV+HH, B: VV) (Figure 5). RGB color composites polarization band shows similar with a little optical image. Dark area shown terrace cropping area and urban road, white and bright color shows artificial structure and some kinds of high tree, blue color appeared coffee cultivated area. Based on chosen the three polarization information, we can analyze the surface monitoring in the future. 5. REFERENCES. Figure 4. Backscattering coefficient of agricultural area by polarization difference When we analyze into the agricultural area by class, backscattering coefficient effects by land cover surface roughness. In case of previous study, the difference between smooth and rough areas varies from 3.5 dB to 5.5 dB at high incidence angles (50°–52°), and from 2 dB to 4 dB at low incidence angles (26°–28°) (Baghdadi N., Zribi M., et al., 2008).. Baghdadi N., Zribi M., et al., 2008, Analysis of TerraSAR-X data and their sensitivity to soil surface parameters over bare agricultural fields, Remote Sensing of Environment, 112, pp. 4370–4379. Fritz, T., Eineder, M., 2008, TerraSAR-X Basic Product Specification Document, TX-GS-DD-3302, Issue 1.5. Fritz, T., 2007, TerraSAR-X Level 1b Product Format Specification, TX-GS-DD-3307, Issue 1.3. Infoterra GmbH, 2008, Radiometric calibration of TerraSAR-X Data, http://www.infoterra.de/fileadmin/ Verzeichnisordner/Dokumente/2_AboutUs/0207_Formal Docs/TSXX-ITD-TN-0049-radiometric_calculations_I1. 00.pdf.. Figure 5. Band composition image (R:VV, G:VV+HH, B:HH).

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