- 1Sapienza University of Rome, Department of Information Engineering, Electronics, and Telecommunication, Roma, Italy (hamed.izadgoshasb@uniroma1.it)
- 2German Research Centre for Geosciences GFZ, Potsdam, Germany
- 3Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, Berlin, Germany
- 4Data Science in Earth Observation, Technical University of Munich, Munich, Germany
Recent advancements in Global Navigation Satellite System Reflectometry (GNSS-R) have led to significant progress in retrieving soil moisture (SM) and other land parameters such as Above Ground Biomass (AGB). The 2016 launch of NASA’s Cyclone GNSS (CYGNSS) mission provided high spatiotemporal resolution GNSS-R data, enabling more accurate soil moisture estimation. This is a key factor influencing the dielectric constant of scattering surfaces. Recent studies have demonstrated the effectiveness of Artificial Neural Networks (ANN) and Deep Learning (DL) models, including convolutional neural networks (CNNs), in addressing the non-linear complexities of soil moisture retrieval [1], [2]. In this study, CyGNSSnet, which was originally designed for global ocean wind speed estimation [3], is being adapted and optimized for global soil moisture estimation.
The research utilizes CyGNSS Level 1 version 3.2 data, specifically Delay Doppler Maps (DDMs), as the primary input. Each reflection point includes additional parameters like measurement geometry and reflectivity. Auxiliary datasets, including topography, soil texture, vegetation indices, and climate-related variables, are incorporated alongside SMAP soil moisture data as the target variable. These datasets, covering August 2018 to July 2021, are matched with CyGNSS specular points using the nearest neighbor method. Data division for training, testing, and validation follows a year-based approach. The CyGNSSnet architecture includes three main components: a map feature extractor using CNNs, an ancillary feature extractor, and a target regressor. Hyperparameters are fine-tuned to achieve optimal performance, with training conducted on HAICORE servers using PyTorch Lightning and an early-stop scheme to minimize training time.
To evaluate the model’s performance across diverse climates and land covers, the global map is stratified by intersecting land cover data from the Climate Change Initiative (CCI) with temperature regimes from the FAO's Global Agro-Ecological Zones (GAEZ v4). This stratification ensures a comprehensive assessment of CyGNSSnet's soil moisture estimation capabilities under varying environmental conditions. The study highlights the potential of advanced DL models like CyGNSSnet to address complex geospatial challenges, enabling more accurate and efficient global soil moisture retrieval.
References:
[1] M. M. Nabi, V. Senyurek, A. C. Gurbuz, and M. Kurum, “Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps,” 2022, doi: 10.1109/JSTARS.2022.3196658.
[2] T. M. Roberts, I. Colwell, C. Chew, S. Lowe, and R. Shah, “A Deep-Learning Approach to Soil Moisture Estimation with GNSS-R,” 2022, doi: 10.3390/RS14143299.
[3] M. Asgarimehr, C. Arnold, T. Weigel, C. Ruf, and J. Wickert, “GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet,” Remote Sens Environ, vol. 269, p. 112801, Feb. 2022, doi: 10.1016/J.RSE.2021.112801.
How to cite: Izadgoshasb, H., Xiao, T., Zhao, D., Pierdicca, N., Wickert, J., and Asgarimehr, M.: Adapting and Evaluating CyGNSSnet: A Deep Learning Approach to estimate Global Soil Moisture using GNSS Reflectometry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3607, https://doi.org/10.5194/egusphere-egu25-3607, 2025.
