Estimation of path delays, TEC and faraday rotation from SAR data

摘要

Spaceborne synthetic aperture radar (SAR) systems are used to measure\udgeo- and biophysical parameters of the Earth’s surface, e.g. for agriculture, forestry\udand land subsidence investigations. Recently launched and upcoming spaceborne SAR\udsatellites continue the trend of measuring these parameters on a global scale from space\udwith continually higher accuracies. Larger frequency modulated chirp bandwidths and increased\udspatial resolution allow for new and additional information with higher geometric\udresolution. One dares to hope that in the near future space borne radar remote sensing\udwill be the able to contribute to monitoring for earthquake precursors. The use of large\udbandwidths, however, causes signal degradation within the ionosphere. Under high solar\udactivity conditions and at low carrier signal frequency, ionosphere-induced path delays\udand Faraday rotation (FR) become significant for SAR applications. The influence of the\udtroposphere becomes relevant given geolocation accuracy requirements of less than 1 m as\udobtained e.g. with TerraSAR-X by the German Aerospace Center (DLR).\udBy means of an in-depth analysis from radar signal propagation through the atmosphere\udand within a standard SAR system model, this dissertation shows possibilities for\udmeasuring, extracting and correcting propagation effects of the two layers most relevant\udto accurate spaceborne SAR measurement: the troposphere and ionosphere. In order\udto test and crosscheck both measurements and models, data obtained from TerraSAR-X\udalong with differential GPS position measurements of corner reflectors (CR) at different\udaltitudes were processed for the tropospheric investigations. Measured data from the\udJapanese Phased Array L-band Synthetic Aperture Radar (PALSAR), onboard the Advanced\udLand Observing Satellite (ALOS), and simulated data from a potential P-band\udsystem were used to examine and simulate the ionospheric effects and to establish space\udborne methods for the extraction of ionospheric total electron content (TEC) and FR from\udSAR data. Concluding propositions evaluate possible SAR sensor and signal modifications\udto facilitate corrections.