Summary of The Muon Space Gnss-r Surface Soil Moisture Product, by Max Roberts et al.
The Muon Space GNSS-R Surface Soil Moisture Product
by Max Roberts, Ian Colwell, Clara Chew, Dallas Masters, Karl Nordstrom
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV); Space Physics (physics.space-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a deep learning pipeline for retrieving near-surface soil moisture using Global Navigation Satellite System-Reflectometry (GNSS-R) receivers on small satellites. The pipeline is trained on NASA’s Cyclone GNSS (CYGNSS) mission data and produces operational retrievals with comparable performance to the Soil Moisture Active-Passive (SMAP) satellite in many regions, achieving an ubRMSE of 0.032 cm^3 cm^-3 for in situ soil moisture observations from SMAP core validation sites. The Muon Space product outperforms the v1.0 CYGNSS soil moisture product in almost all aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers at Muon Space are building a constellation of small satellites, many of which will carry GNSS-R receivers. They’ve developed a deep learning pipeline to retrieve near-surface soil moisture using data from NASA’s Cyclone GNSS mission. The paper explains the input datasets, preprocessing methods, and model architecture used to generate the soil moisture products. These products are compared to in situ measurements and other existing satellite data. |
Keywords
» Artificial intelligence » Deep learning
