Improving passive microwave soil moisture retrievals using RADARSAT constellation mission data

使用 RADARSAT 星座任务数据改进被动微波土壤湿度反演

• 批准号: RGPIN-2020-04705
• 负责人: Berg, Aaron
• 金额: $ 3.13万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716421
• 关键词: Improving; passive; microwave; soil; moisture

Accurate soil moisture products would have important benefits for improved predictions related to agriculture, weather and streamflow. Currently, global estimates of soil moisture are available from several satellite missions including NASA's soil moisture active passive mission (SMAP). Validation of these products over Canada has resulted in variable success. Over agricultural regions the satellite derived soil moisture product from SMAP is considered accurate, however results for northern regions were less promising. The proposed research will examine opportunities to improve these soil moisture products. Areas we will focus on for improvement of these retrievals include: separating the impact of vegetation emission from soil emission to further isolate the soil moisture response and reducing the impact of inundated surfaces on the soil moisture retrievals. For each of these applications we will use data from the recently launched RADARSAT-Constellation mission (RCM) for high resolution observations of vegetation properties and saturated features. Finally we will investigate ways to integrate RCM and SMAP data to produce a high resolution soil moisture map over Canada. The proposed research will form several research projects suitable for the training of new scientists with interest in using satellite data for environmental observations and modelling.

准确的土壤湿度产品对于改进与农业、天气和水流相关的预测具有重要意义。 目前，全球土壤湿度估计可通过多个卫星任务获得，包括美国宇航局的土壤湿度主动被动任务（SMAP）。这些产品在加拿大的验证取得了不同程度的成功。在农业地区，卫星从 SMAP 获得的土壤湿度产品被认为是准确的，但北部地区的结果不太乐观。 拟议的研究将探讨改善这些土壤湿度产品的机会。 我们将重点改进这些反演的领域包括：将植被排放的影响与土壤排放分开，以进一步隔离土壤水分响应，并减少淹没表面对土壤水分反演的影响。对于这些应用中的每一个，我们都将使用最近启动的 RADARSAT-Constellation 任务 (RCM) 的数据来对植被特性和饱和特征进行高分辨率观测。最后，我们将研究如何整合 RCM 和 SMAP 数据来生成加拿大的高分辨率土壤湿度图。拟议的研究将形成几个研究项目，适合培训有兴趣使用卫星数据进行环境观测和建模的新科学家。