The thickness of Arctic sea ice plays a critical role in Earth's climate and ocean circulation. An accurate measurement of this parameter on synoptic scales at regular intervals would enable characterization of this important component for the understanding of ocean circulation and the global heat balance. Presented in this paper is a low frequency VHF interferometer technique and associated radar instrument design to measure sea ice thickness based on the use of backscatter correlation functions. The sea ice medium is represented as a multi-layered medium consisting of snow, sea-ice and sea water, with the interfaces between layers characterized as rough surfaces. This technique utilizes the correlation of two radar waves of different frequencies and incident and observation angles, scattered from the sea ice medium. The correlation functions relate information about the sea ice thickness. Inversion techniques such as the genetic algorithm, gradient descent, and least square methods, are used to derive sea ice thickness from the phase information related by the correlation functions. The radar instrument is designed to be implemented on a spacecraft and the initial test-bed will be on a Twin Otter aircraft. Radar system and instrument design and development parameters as well as some measurement requirements are reviewed. The ability to obtain reliable phase information for successful ice thickness retrieval for various thickness and surface interface geometries is examined.
Global satellite remote sensing records show evidence of recent vegetation greening and an advance in the onset of the growing season at high latitudes. We apply a terrestrial net primary production (NPP) model driven by satellite observations of vegetation properties and daily surface meteorology from an atmospheric GCM to assess spatial patterns, annual variability, and recent trends in vegetation productivity across Alaska and northwest Canada. We compare these results with regional observations of the timing of growing season onset derived from satellite passive microwave remote sensing measurements from the Special Sensor Microwave Imager, SSM/I. Our results show substantial variability in annual NPP for the region that appears to be driven largely by variations in canopy photosynthetic leaf area and average summer air temperatures. Variability in maximum canopy leaf area and NPP also correspond closely to remote sensing observations of the timing of the primary seasonal thaw event in spring. Relatively early spring thawing appears to enhance NPP, while delays in seasonal thawing and growing season onset reduce annual vegetation productivity. Our results indicate that advances in seasonal thawing and spring and summer warming for the region associated with global change are promoting a general increase in NPP.
Landscape transitions between seasonally frozen and thawed conditions occur each year over roughly 50 million square kilometers of Earth's Northern Hemisphere. These realtively abrupt transitions represent the closest analog to a biospheric and hydrologic on/off switch existing in nature, affecting surface meteorological conditions, ecological trace gas dynamics, energy exchange and hydrologic activity profoundly. We utilize time series satellite-borne microwave remote sensing measurements from the Special Sensor Microwave Imager (SSM/I) to examine spatial and temporal variability in seasonal freeze/thaw cycles for the pan-Arctic basin and Alaska. Regional measurements of spring thaw timing are derived using daily brightness temperature measurements from the 19 GHz, horizontally polarized channel, spearately for overpasses with 6 AM and 6 PM equatorial crossing times. Spatial and temporal patterns in regional freeze/thaw dynamics show distinct differences between North Americ and Eurasia, and boreal forest and Arctic tundra biomes. Annual anomalies in the timing of thawing in spring also correspond closely to seasonal atmospheric CO2 concentration anomalies derived from NOAA CMDL arctic and subarctic monitoring stations. Classification differences between AM and PM overpass data average approximately 5 days for the region, through both appear to be effective surrogates for monitoring annual growing seasons at high latitudes.
Conference Committee Involvement (3)
Land Surface and Cryosphere Remote Sensing V
2 December 2024 | Kaohsiung, Taiwan
Land Surface and Cryosphere Remote Sensing IV
25 September 2018 | Honolulu, Hawaii, United States
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