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The lack of a readily accessible, tightly integrated data fabric connecting high-speed networking, storage, and computing services remains a critical barrier to the democratization of scientific discovery. To address this challenge, we are building National Science Data Fabric (NSDF), a holistic ecosystem to facilitate domain scientists in their daily research. NSDF comprises networking, storage, and computing services, as well as outreach initiatives. In this paper, we present a testbed integrating three services (i.e., networking, storage, and computing). We evaluate their performance. Specifically, we study the networking services and their throughput and latency with a focus on academic cloud providers; the storage services and their performance with a focus on data movement using file system mappers for both academic and commercial clouds; and computing orchestration services focusing on commercial cloud providers. We discuss NSDF's potential to increase scalability and usability as it decreases time-to-discovery across scientific domains. 

Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor and Random Forest ). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.