CMG: Multi-Scale Data Assimilation of Soil Moisture Under Heterogeneous Soil Hydraulics

本项目利用不同来源的数据，如原位和遥感平台，对浅层地下高分辨率土壤水分进行预测，这些数据具有不同的支持尺度和精度。土壤湿度受土壤类型、地形、植被和气候等因素的控制。正在进行/计划进行的全球尺度卫星平台地面任务，以及其他基于原位和飞机的土壤湿度测量活动，为研究跨区域的多尺度土壤水文过程和控制的演变提供了独特的机会。土壤非均质性通过质地、有机质含量、孔隙度、大孔隙度和结构的变化影响土壤水分的分布。近年来的研究表明，在自然条件下，近地表土壤的水力特性随时间的动态变化，主要受地表附近可能存在的颗粒迁移、孔隙空间演化和其他生物活动的影响。本研究的目的是利用不同分辨率的地面传感器（原位）和机载传感器（如极化扫描辐射计（PSR） 50m × 50m和电子扫描薄阵列辐射计（ESTAR） 800m × 800m）获得的数据，准确预测1997年南方大平原（SGP97）实验区遥感足迹中1 ~ 2m的土壤水分动态。在本文中，我们提出了一种新的应用伴随方法在粗尺度上估计土壤水力导电性。动态过程与升/降尺度技术相结合，以提供不同空间分辨率下的土壤水力特性。最后对匹配分辨率下的土壤水分进行预测。近年来，了解和量化全球水、能源和碳循环已成为维持地球健康的优先研究。土壤湿度是全球水循环的一个关键变量。例如，土壤湿度条件在决定地下水补给量方面很重要，而不是河流流量。此外，陆地-大气相互作用严重依赖于土壤湿度的状态。准确评估土壤水分的时空变化有利于许多应用和回答各种研究问题。土壤水分的测量对于研究农业、环境、生态、水资源、气候动力学、土壤强度、土壤可通行性和土壤侵蚀具有重要意义。例如，在气候动力学中，土壤水分储存的长期变化已被确定为气候变化的一个指标，土壤水分信息可以校准和验证气候预测。考虑到土壤湿度在大多数陆地表面过程中所起的关键作用，需要以高时空分辨率和精度监测或估计土壤湿度。利用新的航空遥感和先进的数学模型，结合现有的土壤性质数据，我们建议开发一个新的框架来估计在选定地区的非常高分辨率的土壤水分分布。

This project is concerned with the prediction of high resolution soil moisture in shallow subsurface using data from different sources, such as in-situ and remote sensing platforms, which have varying scales of support and precision. Soil moisture is controlled by the factors such as soil type, topography, vegetation, and climate. The ongoing/planned global-scale land surface mission of satellite platforms, and other in-situ, and aircraft-based soil moisture measurement campaigns present a unique opportunity to study the evolution of multiscale soil hydrologic processes and controls across the regions at a range of spatial and temporal scales. Soil heterogeneity affects the distribution of soil moisture through variations in texture, organic matter content, porosity, macroporosity, and structure. Recent studies showed the dynamics of near surface soil hydraulic properties across time under natural conditions due to possible particle migration, pore space evolution, and other biological activities near the earth surface. The objective of this proposal is to use the data obtained from sources, such as ground-based sensors (in-situ) and air-borne sensors at different resolutions (e.g., Polarimetric Scanning Radiometer (PSR) 50m x 50m and Electronically Scanned Thinned Array Radiometer (ESTAR) 800m x 800 m), to accurately predict the dynamics of soil moisture at the finer scales (1-2m) in selected remote sensing footprints of Southern Great Plains 1997 (SGP97) experimental region. In the proposal, we use a novel application of adjoint method for estimation of soil hydraulic conductivity on a coarse scale. The dynamic procedure is coupled with upscaling/downscaling techniques to provide soil hydraulic properties at different spatial resolutions. Finally the soil moisture at matching resolutions will be predicted.In recent years, understanding and quantifying the global water, energy, and carbon cycles has become a priority research to sustain the health of our planet. Soil moisture is a key variable in the global water cycle. For example, soil moisture conditions are important in determining the amount of ground water recharge as opposed to stream flow. In addition, land-atmosphere interactions critically depend on the state of soil moisture. Accurate assessment of the spatial and temporal variation of soil moisture is advantageous for numerous applications and for answering diverse research questions. Measurement of soil moisture is important for the study agriculture, environment, ecology, water resources, climate dynamics, soil strength, trafficability, and soil erosion. For example, in climate dynamics, long-term changes in soil moisture stores have been identified as an indicator of climate change, and soil moisture information can calibrate and validate climate predictions. Given the critical role that soil moisture plays in most land-surface processes, it is desirable that soil moisture be monitored or estimated at high spatio-temporal resolution and accuracy. Using new airborne remote sensing and advanced mathematical modeling in conjunction with available soil properties data, we propose to develop a new framework to estimate very high resolution soil moisture distribution at selected regions.