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首页> 外文学位 >The impact of snow cover variability on snow water equivalent estimates derived from passive microwave brightness temperatures over a prairie environment.
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The impact of snow cover variability on snow water equivalent estimates derived from passive microwave brightness temperatures over a prairie environment.

机译：积雪变化对大草原环境中被动微波亮度温度得出的雪水当量估算值的影响。

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摘要

Considerable seasonal and inter-annual variation in the physical properties and extent of snow cover pose problems for obtaining reliable estimates of quantities and characteristics of snow cover from both conventional and satellite measurements (Goodison and Walker, 1994; Goita et al., 2003). In spite of these challenges, the Climate Research Branch of the Meteorological Service of Canada (MSC) has developed a suite of algorithms to derive snow water equivalent (SWE) estimates from remotely sensed passive microwave imagery (Goodison and Walker, 1994; Derksen et al., 2002a; Goita et al., 2003). The MSC algorithms work particularly well over open prairie environments under the assumption of large areas of consistent snow cover (Derksen et al., 2002a). While studies have documented underestimation in passive microwave estimates of snow extent in marginal areas when compared to optical satellite data (Derksen et al., 2003b), the accuracy in SWE retrievals under variable and patchy snow conditions is not well understood.;This research verifies that the continuous snow cover assumption embedded in the MSC passive microwave SWE algorithm does not produce acceptable results over a patchy snow cover. Several in-situ observations that appear to play an important role in affecting the satellite passive microwave data over a variable snow cover include the presence or absence of an ice lens, the fractional snow covered area, snow depth and the ground temperature. In an attempt to mitigate the impact of fractional snow cover on snow water equivalent estimates, a weighted algorithm is proposed that applies the percentage of snow cover over a remotely sensed footprint to the SWE estimate derived by the MSC algorithm.;In an effort to better understand how a variable and patchy snow cover impacts remotely sensed SWE retrievals, field-based experiments were conducted over patchy snow covered areas in February 2005 and March 2008. A systematic sampling strategy was developed over a 1600 square kilometre (km2 ) area in southern Saskatchewan near a calibration/validation flight line used for algorithm development in the 1980s (Goodison and Walker, 1994). Land covers found at the sampling sites included fallow and stubble fields, pastures and shelter belts. A large number of sampling sites contained snow pack layers that included one or more ice lenses.
机译：积雪的物理性质和程度的季节性和年际变化都很大，这给从常规和卫星测量中获得可靠的积雪数量和特征估计提供了问题（Goodison和Walker，1994； Goita等，2003）。尽管存在这些挑战，加拿大气象局（MSC）的气候研究部门仍开发了一套算法，可从遥感无源微波图像中得出雪水当量（SWE）估算值（Goodison和Walker，1994年； Derksen等人，1994年）。 （2002a； Goita等，2003）。在大面积一致积雪的假设下，MSC算法在开阔的草原环境下特别有效（Derksen等，2002a）。尽管有研究表明与光学卫星数据相比，微波对边缘地区降雪程度的被动微波估计低估了（Derksen et al。，2003b），但在多雪和零散雪况下SWE检索的准确性尚不十分清楚。嵌入在MSC无源微波SWE算法中的连续积雪假设在片状积雪上无法产生可接受的结果。在影响可变雪盖上的卫星无源微波数据的过程中，一些原地观测似乎起着重要作用，包括是否存在冰透镜，积雪的分数面积，积雪深度和地面温度。为了减轻部分积雪对雪水当量估算值的影响，提出了一种加权算法，该算法将遥感足迹上积雪的百分比应用于MSC算法得出的SWE估算值。为了了解可变的零散的积雪如何影响遥感的SWE取回，2005年2月和2008年3月在零散的积雪地区进行了野外实验。在萨斯喀彻温省南部1600平方公里（km2）的区域开发了系统的采样策略在1980年代用于算法开发的校准/验证飞行路线附近（Goodison and Walker，1994）。在采样点发现的土地覆盖物包括休耕地，茬地，牧场和防护林带。大量采样点的积雪层包括一个或多个冰晶。

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  • 作者

    Turchenek, Kim Richard.;

  • 作者单位

    The University of Regina (Canada).;

  • 授予单位 The University of Regina (Canada).;
  • 学科 Physical Geography.;Remote Sensing.;Hydrology.
  • 学位 M.Sc.
  • 年度 2010
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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