The Muon Space GNSS-R Surface Soil Moisture Product
This provides a new soil moisture product for environmental monitoring, though it is incremental as it builds on existing missions like CYGNSS and SMAP.
The paper tackles the problem of retrieving near-surface soil moisture using GNSS-R data from satellites, developing a deep learning pipeline that achieves an ubRMSE of 0.032 cm³ cm⁻³ against in situ measurements, with improvements in spatial resolution over SMAP and outperforming the v1.0 CYGNSS product in most aspects.
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.