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Sensitivity of Ku- and X-Band Radar Observations to Seasonal Snow in Ontario, Canada
Canadian Journal of Remote Sensing ( IF 2.0 ) Pub Date : 2019-11-02 , DOI: 10.1080/07038992.2019.1704621
Aaron Thompson 1 , Richard Kelly 1 , Joshua King 2
Affiliation  

Abstract Radar scatterometer observations at 17.2 GHz and 9.6 GHz were made of the snow cover in mid-latitude agricultural fields, using the University of Waterloo scatterometer, to determine the sensitivity of the frequency-dependent radar response to snow water equivalent. Observations were made in alfalfa fields near Maryhill, Ontario during the 2013–2014 and 2014–2015 winter seasons. Additional observations in the 2014–2015 season were made at Englehart, Ontario. The natural snowpack was scanned using polarimetric observations throughout the season as snow accumulated. Conditions typical of the mid-latitude snow environment, including non-frozen ground, snow melt, freezing rain, and agricultural vegetation were observed to confound the relationship between backscatter and SWE or snow depth. With these special cases removed from the analysis, the strongest relationship is observed at 17.2 GHz where VV-polarized backscatter increased linearly by 0.35 dB/cm SWE. Further, the backscatter increases linearly by 0.10 dB/cm of snow depth. Relationships at 9.6 GHz are weaker. These findings are unique. They highlight the suitability and challenges of radar remote sensing for estimating snow accumulation in a mid-latitude environment and demonstrate the use of polarization signatures for identifying the effects of short vegetation prevalent in this environment.

中文翻译:

加拿大安大略省 Ku 和 X 波段雷达观测对季节性降雪的敏感性

摘要 17.2 GHz 和9.6 GHz 的雷达散射仪观测是在中纬度农田的积雪中使用滑铁卢大学散射仪进行的,以确定频率相关雷达响应对雪水当量的灵敏度。在 2013-2014 年和 2014-2015 年冬季期间,在安大略省玛丽希尔附近的苜蓿田中进行了观察。2014-2015 赛季的额外观察是在安大略省的恩格尔哈特进行的。随着雪的积累,整个季节使用偏振观测扫描天然积雪。观察到中纬度雪环境的典型条件,包括非冻土、雪融化、冻雨和农业植被,混淆了反向散射与 SWE 或雪深之间的关系。从分析中删除这些特殊情况后,在 17.2 GHz 处观察到最强的关系,其中 VV 极化反向散射线性增加 0.35 dB/cm SWE。此外,反向散射线性增加 0.10 dB/cm 的雪深。9.6 GHz 的关系较弱。这些发现是独一无二的。他们强调了雷达遥感在估计中纬度环境中积雪方面的适用性和挑战,并展示了使用极化特征来识别这种环境中普遍存在的短植被的影响。
更新日期:2019-11-02
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