针对无人飞行器合成孔径雷达(UAVSAR)差分干涉测量可用于形变监测的发展潜力，介绍了美国UAVSAR的系统设计、工作原理、数据产品等，概述了国内外UAVSAR系统的发展与应用现状、存在问题和发展趋势，重点讨论了UAVSAR在火山、断裂、冰川、冻土、滑坡、地面沉降等典型地表形变监测的应用。其高空间分辨率和较强的穿透性是对星载SAR数据极好的补充，彰显了UAVSAR系统极大的应用前景，为国内机载雷达系统的研究及其在地质灾害监测方面提供参考。

针对无人飞行器合成孔径雷达(UAVSAR)差分干涉测量可用于形变监测的发展潜力，介绍了美国UAVSAR的系统设计、工作原理、数据产品等，概述了国内外UAVSAR系统的发展与应用现状、存在问题和发展趋势，重点讨论了UAVSAR在火山、断裂、冰川、冻土、滑坡、地面沉降等典型地表形变监测的应用。其高空间分辨率和较强的穿透性是对星载SAR数据极好的补充，彰显了UAVSAR系统极大的应用前景，为国内机载雷达系统的研究及其在地质灾害监测方面提供参考。

Extensive, detailed information on the spatial distribution of active layer thickness (ALT) in northern Alaska and how it evolves over time could greatly aid efforts to assess the effects of climate change on the region and also help to quantify greenhouse gas emissions generated due to permafrost thaw. For this reason, we have been developing high-resolution maps of ALT throughout northern Alaska. The maps are produced by upscaling from high-resolution swaths of estimated ALT retrieved from airborne P-band synthetic aperture radar (SAR) images collected for three different years. The upscaling was accomplished by using hundreds of thousands of randomly selected samples from the SAR-derived swaths of ALT to train a machine learning regression algorithm supported by numerous spatial data layers. In order to validate the maps, thousands of randomly selected samples of SAR-derived ALT were excluded from the training in order to serve as validation pixels; error performance calculations relative to these samples yielded root-mean-square errors (RMSEs) of 7.5-9.1 cm, with bias errors of magnitude under 0.1 cm. The maps were also compared to ALT measurements collected at a number of in situ test sites; error performance relative to the site measurements yielded RMSEs of approximately 11-12 cm and bias of 2.7-6.5 cm. These data are being used to investigate regional patterns and underlying physical controls affecting permafrost degradation in the tundra biome.