Science Service System

Summary of Proposal HYD2926

TitleSnow Height Estimation by Difference Phase Interferometry Technique in X-Band SAR Data
Investigator hatami, javad - Masters students, Islamic Azad University, Yazd
Team Member
Sarkaregar, Ali - Islamic Azad University, Dep. of Remote Sensing and GIS
Mr Hatami, Javad - Islamic Azad University, Yazd Branch, Yazd, Iran, Remote Sensing and GIS
Mr pakdaman, mohamad - Islamic Azad University, Yazd Branch, Yazd, Iran, Remote Sensing and GIS
Dr Almodaresi, Ali - Islamic Azad University, Yazd Branch, Yazd, Iran, Remote Sensing and GIS
SummaryConventional snow measurements, which are sporadically taken in situ, do not meet the spatial requirements for the reproduction of the seasonal behavior of snowpack in hydrological models. Satellite remote sensing data, particularly those acquired in the active microwave portion of the electromagnetic spectrum, can improve the monitoring of snow-covered surfaces over continuous space-time scales. Spaceborne synthetic aperture radar (SAR) sensors offer the possibility of visualizing snow cover over large areas (several thousands of square kilometers) at a fine spatial resolution (less than 100 m) without the influence of cloud cover or lighting conditions. Furthermore , because the radar signal is extremely sensitive to the presence of liquid water, SAR imagery permits the monitoring of snow cover during the snowmelt period. Unlike microwave radiometers, SARs can effectively distinguish between wet snow and snow-free wet ground. Due to the high absorption loss in water and the specular reflection of wet snow surfaces, wet snow produces a low radar return that contrasts well against the strong backscatter from the (rough) wet ground. Therefore, SAR sensors represent a viable tool for studying snow cover in the context of hydrological investigations. The approaches to mapping wet snow cover with x-band SARs are now tried and tested. Many models developed over the last 15 years have been validated in various environments, including mountainous terrain and glaciers , sea ice and ice sheets , agricultural fields , boreal forest , and forest-tundra ecotones . The ability of SAR to discriminate snow-free from wet or dry snow-covered surfaces has been investigated substantially more than its potential for estimating the snow mass of dry snow cover ; this is particularly true near the Arctic treeline. Recent simulations of ecological sensitivity to climate change have shown that, with the significant warming experienced at the high latitudes of the northern hemisphere, the northern and southern ecotones of the boreal forest will be the areas of greatest change in the future . Because snow is an essential component of the Arctic treeline ecosystems , it is crucial to develop the operational capability to map the seasonal and spatial distribution of snow cover and its properties in these areas. The purpose of this study was to use the images in order to estimate the level of snow cover and snow properties such as snow depth and snow water equivalent. In this study, in order to continuously monitor features such as surface snow cover and snow depth and snow depth of snow on high ground carefully any climate to try take advantage of usingRadar images . And finally, after extraction of the desired parameters makes it possible to use this technology for use in snow hydrology as the main goal will be determined The first step collect data The sensor data of terra sar x band at Time series the region is prepared and Ground data Height was measured over a time series Next Step Processing D-InSAR technique is a method where phase information of radar carrier is used to obtain the land deformation. In the repeat pass mode, if the land deformation happened during the capture of the two images, the interferometric fringes generated by these two images .snow height is calculated by interferometry techniques Next Step Analysis Conclusion The snow depth is measured from the previous stage Compare With the Ground data for Validation Then parameters like snow water equivalent And snow volume are calculated using the existing algorithms And predicts that the volume and depth of the snow better than other methods calculated

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DLR 2004-2016