Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R near-surface soil moisture retrievals using data from NASA's Cyclone GNSS (CYGNSS) mission. In this article, we describe the input datasets, preprocessing methods, model architecture, development methods, and detail the soil moisture products generated from these retrievals. The performance of this product is quantified against in situ measurements and compared to both the target dataset (retrievals from the Soil Moisture Active-Passive (SMAP) satellite) and the v1.0 soil moisture product from the CYGNSS mission. The Muon Space product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm$^3$ cm$^{-3}$ for in situ soil moisture observations from SMAP core validation sites is shown, though performance is lower than SMAP's when comparing in forests and/or mountainous terrain. The Muon Space product outperforms the v1.0 CYGNSS soil moisture product in almost all aspects. This initial release serves as the foundation of our operational soil moisture product, which soon will additionally include data from Muon Space satellites.

翻译：Muon Space（Muon）正在建设一个小型卫星星座，其中多颗卫星将搭载全球导航卫星系统反射测量（GNSS-R）接收机。为迎接该星座的发射，我们开发了一套通用深度学习反演流程，目前该流程已利用NASA飓风GNSS（CYGNSS）任务的数据，实现了业务化的GNSS-R近地表土壤湿度反演。本文阐述了输入数据集、预处理方法、模型架构及开发流程，并详细说明了基于这些反演生成的土壤湿度产品。该产品的性能通过现场测量数据进行了量化评估，并与目标数据集（土壤湿度主被动卫星SMAP的反演结果）及CYGNSS任务的v1.0土壤湿度产品进行了对比。Muon Space产品在保持多数区域性能相当的同时，实现了相较于SMAP的空间分辨率提升。在SMAP核心验证站点的现场土壤湿度观测中，该产品显示出0.032 cm$^3$ cm$^{-3}$的无偏均方根误差（ubRMSE），但在森林和/或山区地形中的表现仍逊于SMAP。Muon Space产品在几乎所有方面均优于v1.0版CYGNSS土壤湿度产品。此次初始发布版本构成了我们业务化土壤湿度产品的基础，后续将逐步纳入来自Muon Space卫星的数据。