首页> 外文会议>Remote sensing for agriculture, ecosystems, and hydrology XIV >FLOOD MAPPING OF YIALIAS RIVER CATCHMENT AREA IN CYPRUS USING ALOS PALSAR RADAR IMAGES
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FLOOD MAPPING OF YIALIAS RIVER CATCHMENT AREA IN CYPRUS USING ALOS PALSAR RADAR IMAGES

机译:利用ALOS PalSAR雷达图像对塞浦路斯的雅里亚斯河流域进行洪水映射。

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This study strives to highlight the potential of flood inundation monitoring and mapping in a catchment area in Cyprus (Yialias river) with the use of radar satellite images. Due to the lack of satellite data acquired during dates flood inundation events took place, the research team selected specific images acquired during dates that severe precipitation events were recorded from the rain gauge station network of Cyprus Meteorological Service in the specific study area. The relationship between soil moisture and precipitation was thoroughly studied and linear regression models were developed to predict future flood inundation events. Specifically, the application of fully polarimetric (ALOS PALSAR) and data acquired over different dates for soil moisture mapping is presented. The PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor carried by the ALOS (Advanced Land Observing Satellite) have quadruple polarizations (HH, VV, HV, VH). The amount of returned radiation (as backscatter echoes) that dictates the brightness of the image depends on factors such as the roughness, size of the target relative to the signal's wavelength, volumetric and diffused scattering. The variation in soil moisture pattern during different precipitation events is presented through soil moisture maps obtained from ALOS PALSAR and data acquired during different dates with different precipitation rates. Soil moisture variation is clearly seen through soil moisture maps and the developed regression models are used to simulate future inundation events. The results indicated the considerable potential of radar satellite images in soil moisture and flood mapping in catchments areas of Mediterranean region.
机译:这项研究力图通过雷达卫星图像突出塞浦路斯(雅里亚河)集水区洪水泛滥的监测和制图潜力。由于在洪水泛滥事件发生期间缺少卫星数据,研究小组选择了在特定研究区域从塞浦路斯气象局雨量计站网记录到严重降水事件的日期获得的特定图像。深入研究了土壤水分与降水之间的关系,并建立了线性回归模型以预测未来的洪水淹没事件。具体而言,介绍了全极化仪(ALOS PALSAR)的应用以及在不同日期获取的土壤湿度测绘数据。由ALOS(高级陆地观测卫星)携带的PALSAR(相控阵型L波段合成孔径雷达)传感器具有四极极化(HH,VV,HV,VH)。决定图像亮度的返回辐射量(作为反向散射回波)取决于多种因素,例如粗糙度,目标相对于信号波长的大小,体积和散射散射。通过从ALOS PALSAR获得的土壤水分图和在不同日期以不同降水率采集的数据,可以得出不同降水事件期间土壤水分模式的变化。通过土壤湿度图可以清楚地看到土壤湿度变化,并使用已开发的回归模型来模拟未来的淹没事件。结果表明,雷达卫星图像在地中海地区集水区的土壤湿度和洪水制图方面具有巨大潜力。

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