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

高纬度微波遥感和建模可提高对北方和北极环境净生态系统生产力的预测了解

• 批准号: RGPIN-2020-06324
• 负责人: Roy, Alexandre
• 金额: $ 2.19万
• 依托单位: Université du Québec à Trois-Rivières
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765349
• 关键词: Latitude; Microwave; Remote; Sensing; Modelling

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.

加拿大北极-北方地区（ABR）的气温正在以不成比例的高速度上升，全球变化正在迅速改变这些重要碳库地区的水、碳和能源循环。温度升高会改变 ABR 的碳平衡，并可能导致土壤中储存的碳释放到大气中，从而产生重大的气候反馈。然而，重要的控制因素包括：1) 降雪和冬季土壤热状况对冬季 CO2 排放的影响 2) 土壤湿度对初级生产总值 (GPP) 的影响以及 3) 春季冻融循环对 GPP 和春季呼吸作用的影响下降，在 ABR 中没有得到很好的量化，也没有在生态系统模型中得到很好的体现。我们对驱动这些控制的复杂相互作用的了解有限，限制了我们准确预测 ABR 区域未来如何与大气相互作用、随气候变化演变并影响人类活动的能力。 当前的星载无源微波辐射计 (PMRS) 对于监测高纬度特性特别有用，因为它们可以测量了解和监测 ABR 中碳通量所需的关键变量（土壤湿度/温度、冻融循环和雪）。然而，在开发和验证关键表面变量的星载反演算法以及开发将 PMRS 观测数据纳入生态系统模型的方法以提高对高纬度碳通量的预测性理解方面仍然存在重要挑战。该研究计划的目标是开发微波遥感方法，以改进影响 ABR 净生态系统生产力三个重要控制因素的关键地表变量的 CLASS-CTEM（加拿大生态系统模型）模拟：1) 冬季土壤热状况对冬季二氧化碳排放的影响 2 ) 土壤湿度对总初级生产力的影响，以及 3) 冻融循环对春季和秋季二氧化碳排放量的 GPP 影响。该方法充分利用了原位观测、微波遥感和生态系统模型（CLASS-CTEM）之间的互补性。特别是，拟议的计划将在加拿大的两个北极地点部署独特的空间分布式土壤呼吸监测系统和土壤湿度/温度传感器，开发新的被动微波遥感检索算法并实施同化方法，以利用遥感观测为 CLASS-CTEM 提供信息。预期成果将填补与加拿大高纬度地区重要气候变化反馈相关的科学空白，并将帮助政府、工业界和原住民社区制定气候变化适应战略。