From local CRNS network observations to meso-scale soil moisture distributions

在第一阶段的宇宙感，我们专注于土壤水分（SM）的变化，在较高的领域规模（0.1 - 1平方公里）在几个研究地点（芬特，Wüstebach，马夸特）。选择CRNS位置是为了在最大化足迹重叠和空间覆盖之间实现平衡。因此，水平SM梯度可以通过假设空间中的某种程度的连续性来识别足迹内和足迹之间，这通过大传感器足迹与高网络密度的组合来证明。然而，对于CRNS传感器的较大间距，这种假设可能是无效的。在稀疏放置的传感器之间桥接这种间隙是智能覆盖模块（SC）的主要主题。在继续第一项目阶段MC模块的实地工作的同时，我们现在将研究扩展到更大的规模。一个关键问题是：我们如何使用CRNS技术来表示SM在中尺度（~ 10 - 100 km²）上的时空分布，其中详尽的CRNS覆盖是不可行的？为此，我们将运行一个小型CRNS集群和单个CRNS探测器的“智能”组合，作为一个网络，其探测器数量与第一阶段联合实地战役中的探测器数量相似，但密度和位置经过优化。我们还将考虑一系列代理和支持测量，以告知我们在未监控位置的SM，例如。G. (1)已知影响SM的静态景观属性（2）遥感产品（RS），以反映地表SM动态和植被特性;（3）水文模型，以整合影响根区（RZ）的气象强迫;以及（4）利用地基和机载平台进行巡回观测，以量化离散时间点（RA）上沿样带的SM变异性（沿着）智能覆盖是指识别与基于CRNS的SM的关系尚未被充分约束和理解的位置属性，使得具有这种属性的新位置可以被附加的CRNS测量覆盖。智能覆盖的另一个方面是，我们将使用确定的关系，以找到最佳的位置在一个固定的CRNS网络在流域尺度，使流域尺度SM的整体估计的不确定性最小化。这些想法将与其他研究模块一起在联合野外活动（JFC）中进行探索，但也可以在具有合成SM和代理数据以及模拟CRNS观测的“虚拟”JFC中进行探索。此外，我们将探索将新的发展集成到CRNS网络的操作中，例如高灵敏度探头，定向屏蔽和局部传入校正。我们还将继续努力缩小规模。总体而言，该研究模块将结合联合收割机技术改进、方法开发、来自其他模块的中尺度数据和扩展的解释能力，以在密集和稀疏网络设置中使用CRNS获得流域尺度SM。

In the first phase of Cosmic Sense, we focused on soil moisture (SM) variability at the upper field scale (0.1 - 1 km²) at several study sites (Fendt, Wüstebach, Marquardt). CRNS locations were selected to achieve a balance between maximizing footprint overlap and spatial coverage. Thus, horizontal SM gradients could be identified within and between footprints by assuming some degree of continuity in space, justified by the large sensor footprint in combination with a high network density. For larger spacings of the CRNS sensors, this assumption may, however, not be valid.Bridging this gap between sparsely placed sensors is the main subject of the Smart Coverage module (SC). While continuing the field-scale work of the MC module of the first project phase, we now expand research to larger scales. A key question is: How can we use the CRNS technique to represent spatio-temporal distribution of SM on the meso-scale (~ 10 - 100 km²) where an exhaustive CRNS coverage is not feasible? To that end, we will operate a "smart" combination of small CRNS clusters and individual CRNS probes as a network with a similar number of probes as in the Joint Field Campaigns of the first phase, but with optimized density and locations. We will also consider a range of proxies and supporting measurements that inform us about SM at unmonitored locations, e. g. (1) static landscape attributes known to affect SM (soil texture, geomorphology, land use); (2) remote sensing products (RS) to represent surface SM dynamics and vegetation properties; (3) hydrological models to integrate the meteorological forcing that affects the root zone (RZ); as well as (4) roving observations using ground-based and airborne platforms to quantify SM variability along transects at discrete points in time (RA).Smart coverage refers to the identification of location properties for which the relationship to CRNS-based SM is not yet sufficiently constrained and understood, so that new locations with such properties can be covered by additional CRNS measurements. Another aspect of smart coverage is that we will use the identified relationships to find optimal locations within a stationary CRNS network at the catchment scale such that the overall estimation uncertainty of catchment-scale SM is minimized. These ideas will be explored, together with other research modules, in a Joint Field Campaign (JFC), but also in a "virtual” JFC with synthetic SM and proxy data and simulated CRNS observations.Furthermore, we will explore the integration of new developments into the operation of CRNS networks, such as high sensitivity probes, directional shielding, and local incoming correction. We will also continue our downscaling efforts. Overall, this research module will combine technical improvements, methodological developments, meso-scale data from other modules and extended interpretation capabilities to obtain catchment scale SM from the employment of CRNS in both dense and sparse network settings.