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.

雪和冰川融化为世界上六分之一以上的人口提供了水，但观察到的变化和对雪融化供应的模型预测表明，到2050年，加拿大和欧亚大陆几个地区未来的水需求有可能无法通过雪融化径流得到满足（Mankin等人，2015年）。雪对商业活动也很重要。例如，不列颠哥伦比亚省2012-13年度滑雪产业收入为13亿加元，占全省年度旅游活动的9%。然而，降雪也带来了巨大的经济成本。春季积雪迅速融化引发的洪水也可能是毁灭性的。例如，2013年南萨斯喀彻温省和埃尔克河流域的洪水预计将造成超过60亿美元的损失（Pomeroy等人，2015年），使其成为加拿大近代历史上损失最大的自然灾害之一。虽然积雪是一种重要的资源，但随着全球变暖，冬季雪季状况发生变化，积雪管理变得越来越具有挑战性。例如，雪季正在缩短，平均雪融化日期在季节中提前（Derksen和Brown，2012）。这些变化影响了滑雪产业、粮食生产和依赖融雪径流的地区的水力发电。雪水当量（SWE）的积累对水资源管理者来说非常重要，因为它揭示了雪中储存了多少水。这项建议的目标是增加我们的理解和使用主动和被动微波遥感系统，提高我们的估计SWE，无论是气候学和水资源的应用。提出了三个研究目标。首先，利用雷达测量的使用，以估计在积雪覆盖的地形在L，C，X和Ku频率的SWE。知识雷达响应从底层有机土壤，埋在雪中的植被和积雪覆盖的森林景观是不完整的，但重要的是，以提高估计SWE在植被（低或高的立场冠层）的存在。此外，干涉成像雷达对雪的观测已经证明了对SWE的强烈敏感性（Leinss等人，2015年），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的卫星多传感器雪产品。这将提供对现有数据之间的一致性和不一致性的见解，并将告知如何减少SWE估计中的不确定性，以创建更稳健的SWE时间序列。 第三，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以收集雪深数据，将绘制多传感器SWE数据与众包雪深数据相结合，为SWE测绘提供强大的多源方法。