High Latitude Microwave Remote Sensing and Modelling for Improved Predictive Understanding of Net Ecosystem Productivity in Boreal and Arctic Environment

加拿大北极-北方地区（ABR）的气温正以不成比例的高速度上升，全球变化正在迅速改变这些地区的水、碳和能源循环，而这些地区是重要的碳库。不断上升的气温改变了ABR的碳平衡，并可能导致储存在土壤中的碳释放到大气中，从而产生主要的气候反馈。然而，重要的控制因素，如1)积雪和冬季土壤热状态对冬季CO2排放的影响，2)土壤湿度对总初级生产（GPP）的影响，3)春季冻融循环对GPP和秋季呼吸的影响，在ABR中没有很好地量化，在生态系统模型中也没有很好地表示。我们对驱动这些控制的复杂相互作用的有限理解限制了我们准确预测ABR区域未来如何与大气相互作用，如何随着气候变化而演变并影响人类活动的能力。

Temperatures are rising at disproportionally high rates across Canada's Arctic-Boreal Region (ABR) and global changes are rapidly altering the water, carbon and energy cycles in these regions that are important carbon pools. Rising temperatures alter the ABR's carbon balance and potentially lead to the release of the carbon stored in the soil to the atmosphere, creating major climatic feedbacks. However, important controls such as 1) the impact of snow and winter soil thermal regime on winter CO2 emission 2) the impact of soil moisture on gross primary production (GPP) and 3) the freeze/thaw cycle on GPP in spring and respiration in fall, are not well quantified in ABR and not well represented in ecosystem models. Our limited understanding of the complex interactions driving these controls limits our ability to accurately predict how ABR regions will interact with the atmosphere in the future, evolve with climate change and affect human activities. Current spaceborne passive microwave radiometers (PMRS) are particularly useful for monitoring high latitude characteristics, because they can measure key variables (soil moisture/temperature, freeze/thaw cycles and snow) needed to understand and monitor carbon fluxes in ABR. However, important challenges remain in developing and validating spaceborne retrieval algorithms of key surface variables and developing methodologies to ingest PMRS observations into ecosystem models in order to improve predictive understanding of carbon fluxes in high latitude. The goal of this research program is to develop microwave remote sensing approaches to improve CLASS-CTEM (Canadian ecosystem model) simulations of key surface variables affecting three important controls of ABR net ecosystem productivity: 1) winter soil thermal regime impact on winter CO2 emission 2) soil moisture impact on gross primary productivity and 3) freeze/thaw cycles impact on GPP in spring and fall CO2 emission. The methodological approaches take full advantage of the complementarity between in-situ observations, microwave remote sensing, and the ecosystem model (CLASS-CTEM). In particular, the proposed program will deploy a unique spatially distributed soil respiration monitoring system and soil moisture/temperature sensors at two arctic sites in Canada, develop new passive microwave remote sensing retrieval algorithms and implement assimilation approach to inform CLASS-CTEM using remote sensing observations. The expected outcomes will fill the scientific gaps related to important climate change feedbacks in Canada's high latitude regions and will help government, industry and Aboriginal communities to develop adaptation strategies to climate change.