The Muon Space GNSS-R Surface Soil Moisture Product

TL;DR

This paper presents a deep learning-based soil moisture retrieval product using GNSS-R data from NASA's CYGNSS, demonstrating improved spatial resolution and performance over existing products, and laying the groundwork for future Muon Space satellite data integration.

Contribution

Developed a generalized deep learning pipeline for GNSS-R soil moisture retrievals, achieving improved spatial resolution and performance compared to existing satellite products.

Findings

Achieved ubRMSE of 0.032 cm³/cm³ against in situ measurements.

Outperformed the v1.0 CYGNSS soil moisture product in most regions.

Demonstrated comparable performance to SMAP in many areas.

Abstract

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.