Remote sensing of snow using active and passive microwave instruments

地球上的季节性积雪对世界许多国家的气候和水资源、交通和农业以及相关的社会经济活动产生重要影响。雪的存在及其对气候系统的间接影响影响气候状态，并可通过极地和山区的放大变暖影响气候动态。雪也占地球可用淡水的很大一部分。累积雪水当量（SWE）的知识告知水管理人员春季预计有多少融化。这项研究的长期目标是开发改进的积雪估计（SWE或雪深）从卫星遥感仪器用于河流预报和更好地了解积雪是如何在地球系统中变化。为实现这一目标，确定了三个主要目标。(1)开发一种新的高空间分辨率雷达遥感方法，利用最先进的地面雷达积雪测量数据，估算特别是山区和森林地区的积雪当量。这将需要在冬季雪季的关键时期（季节初期冻结、季节中期积累和季节后期融化）在不同地貌部署雷达仪器，以观察和量化雪的变化，并描述其特征，以制定估计方法。(2)利用检索科学的最新研究进展，结合联合收割机主动和被动微波遥感测量，使被动微波观测尺度的SWE估计得到改进。在增强了粗分辨率被动微波SWE数据后，该提案将探讨尺度缩小方法（贝叶斯），以更高的空间分辨率估计SWE，并生成1979年至今的时间序列。这与日本宇宙航空研究开发机构资助的卫星SWE算法开发和维护项目密切相关。(3)开发我的Web 2.0倡议，Snowtweets，利用众包数据，从已建立的网络（联邦，省和地区公用事业水平）增加现有的原位雪深数据。

The Earth's seasonal snow cover exerts an important effect on climate and on water resources, transportation and agriculture, and related socio-economic activities in many countries of the world. The presence of snow and its albedo effect on the climate system influences climate state and can affect climate dynamics through amplified warming in polar and mountain regions. Snow also accounts for a significant portion of the Earth's useable freshwater. Knowledge of accumulated snow water equivalent (SWE) informs water managers of how much melt can be expected in spring. The long term objectives of this research are to develop improved snow accumulation estimates (SWE or snow depth) from satellite remote sensing instruments for use in river forecasting and for a better understanding of how snow accumulation is changing in the Earth's system. To achieve this, three primary objectives are defined. (1) develop a new high spatial resolution radar remote sensing approach for SWE estimation, particularly in mountain terrain and forested areas, using state of the art ground-based radar measurements of snow. This will require deployment of radar instruments to different landscapes for key periods in the winter snow season (early season freeze, mid-season accumulation and late season melt) to observe and quantify changes in snow and characterize them to develop estimation methods. (2) Leverage recent research developments in retrieval science that combine active and passive microwave remote sensing measurements to enable improved SWE estimates at the passive microwave observation scale. Having enhanced the coarse resolution passive microwave SWE data, the proposal will explore downscaling methods (Bayesian) to to estimate SWE at higher spatial resolution and generate a time series from 1979- present. This links well with a satellite SWE algorithm development and maintenance project funded by the Japan Aerospace Exploration Agency. (3) develop my Web 2.0 initiative, Snowtweets, for leveraging crowdsourced data for augmenting existing in situ snow depth data from established networks (federal, provincial and regional utility levels).