Remote sensing of snow using active and passive microwave systems

使用主动和被动微波系统遥感雪

• 批准号: RGPIN-2017-04385
• 负责人: Kelly, Richard
• 金额: $ 1.6万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678861
• 关键词: Remote; sensing; snow; using; active

Snow and glacier melt provide water for more than one sixth of the world's population yet observed changes and model predictions of snowmelt supply suggest that by 2050 future water demands in several parts of Canada and Eurasia are at risk of not being met by snow melt runoff (Mankin et al., 2015). Snow is also important for business activities. For example, the British Columbia ski industry revenue for 2012-13 was $1.3 billion which represents 9% of all annual provincial tourism activity. Yet snow also attracts significant economic costs. Hazards created by flooding from rapid spring snowmelt can also be devastating. For example, the recent 2013 flood in the South Saskatchewan and Elk river basins have been projected to cost in excess of $6 billion (Pomeroy et al. 2015), making it one of the costliest natural disasters in recent Canadian history. While snow accumulation is an important resource, its management is becoming increasingly challenging as the winter snow season regime changes with global warming. For example, the snow season is shortening with the average snow melt date shifting earlier in the season (Derksen and Brown, 2012). Changes impact the ski industry, food production and the hydroelectric power generation in regions that rely on snowmelt runoff.******Snow water equivalent (SWE) accumulation is of keen importance for water resource managers because it reveals how much water is stored in snow. The goal of this proposal is to increase our understanding and use of active and passive microwave remote sensing systems improve our estimation of SWE, both for climatology and water resources applications. Three research objectives are proposed. First, to exploit the use of radar measurements to estimate SWE in snow covered terrain at L, C, X and Ku frequencies. Knowledge about the radar response from underlying organic soils, buried vegetation within snow and from snow covered forest landscapes is incomplete yet important to improve estimates of SWE in the presence of vegetation (low or tall stand canopies). In addition, interferometric imaging radar observations of snow have demonstrated strong sensitivity to SWE (Leinss et al. 2015) and this proposal will confirm these findings over a wider range of snow environments. Second, the proposal will focus on combining different satellite multi-sensor snow products. This will provide insights into agreements and disagreements between existing data and will inform how the uncertainties in the SWE estimates might be reduced to create a more robust time series of SWE. Third, the research will use techniques and methods deployed in the first two objectives to develop and explore new science applications. For example, my Snowtweets research project, which seeks to connect with school programmes to collect snow depth data, will map multi-sensor SWE data in combination with crowdsourced snow depth data to provide a powerful multi-source approach to SWE mapping.

积雪和冰川融化为世界六分之一以上的人口提供了水，但观察到的变化和融雪供应的模型预测表明，到 2050 年，加拿大和欧亚大陆的一些地区未来的用水需求将面临无法通过融雪径流满足的风险。曼金等人，2015）。雪对于商业活动也很重要。例如，不列颠哥伦比亚省滑雪业 2012-13 年收入为 13 亿加元，占全省年度旅游活动的 9%。然而，雪也会带来巨大的经济成本。春季快速融雪引发的洪水造成的危害也可能是毁灭性的。例如，最近发生在南萨斯喀彻温省和埃尔克河流域的 2013 年洪水预计造成的损失将超过 60 亿加元（Pomeroy 等，2015），使其成为加拿大近代历史上损失最惨重的自然灾害之一。虽然积雪是一种重要资源，但随着冬季雪季状况随着全球变暖而变化，积雪的管理变得越来越具有挑战性。例如，雪季正在缩短，平均融雪日期提前（Derksen 和 Brown，2012 年）。这些变化影响着依赖融雪径流的地区的滑雪业、粮食生产和水力发电。********雪水当量 (SWE) 积累对于水资源管理者来说非常重要，因为它揭示了储存的水量在雪中。该提案的目标是增加我们对主动和被动微波遥感系统的理解和使用，提高我们对气候学和水资源应用的 SWE 的估计。提出了三个研究目标。首先，利用雷达测量来估计积雪地形中 L、C、X 和 Ku 频率的 SWE。关于底层有机土壤、雪中埋藏植被和积雪覆盖的森林景观的雷达响应的知识并不完整，但对于改善植被（低或高林冠）存在的 SWE 估计很重要。此外，干涉成像雷达对雪的观测表明对 SWE 具有很强的敏感性（Leinss 等人，2015），该提案将在更广泛的雪环境中证实这些发现。其次，该提案将侧重于结合不同卫星多传感器雪产品。这将深入了解现有数据之间的一致和分歧，并告知如何减少 SWE 估计中的不确定性，以创建更稳健的 SWE 时间序列。 第三，该研究将使用前两个目标中部署的技术和方法来开发和探索新的科学应用。例如，我的 Snowtweets 研究项目旨在与学校项目联系起来收集雪深数据，该项目将结合众包雪深数据绘制多传感器 SWE 数据，为 SWE 绘制提供强大的多源方法。