Comparative analysis of CYGNSS-derived soil moisture and its spatiotemporal distribution across Egypt and Africa
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Ahmed Salah El-Din
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Ibrahim Fouad Ahmed
Abstract
Soil Moisture Content (SMC) is a crucial variable influencing Earth’s environmental processes, including the water cycle, energy balance, and carbon cycle. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for monitoring SMC. The Cyclone Global Navigation Satellite System (CYGNSS), a mission consisting of eight microsatellites launched by NASA in 2016, utilizes GNSS-R to provide high-temporal-resolution soil moisture estimates. This study conducts a comparative analysis of CYGNSS-derived SMC data against SMAP, SMOS, CEDA, and ERA5 datasets over Egypt and Africa from 2017 to 2021. Using tolerance and interpolation techniques, the analysis revealed strong correlations between CYGNSS and the other datasets, with overall correlation values of 0.78 (SMAP), 0.64 (SMOS), 0.74 (CEDA), and 0.80 (ERA5). The corresponding RMSE values were 0.022, 0.018, 0.027, and 0.035 cm3/cm3, respectively. Interpolation results showed correlations of 0.76, 0.47, 0.54, and 0.39, with RMSE values of 0.03, 0.065, 0.045, and 0.076 cm3/cm3, respectively. Additionally, the study analyzes the spatiotemporal distribution of soil moisture across Egypt and Africa, revealing regional variations and trends over the study period. These findings demonstrate CYGNSS’s effectiveness in capturing soil moisture variations, particularly in Egypt.
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Research ethics: This article does not contain any studies with human participants or animals performed by any of the authors.
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Informed consent: Not applicable.
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Author contributions: A. S. E and I. F. A conceptualized and designed the study. A. S. E performed the analysis and wrote the first draft. I. F. A edited and revised the manuscript. G. S. E supervised the research, reviewed, and edited the manuscript. A. E. M also reviewed and edited the manuscript. All authors read and approved the final manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Research funding: None declared.
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Data availability: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Specific sequences from the CYGNSS dataset utilized in this study are publicly accessible at (https://podaac.jpl.nasa.gov/), SMAP datasets at (https://nsidc.org/home), SMOS datasets at (https://www.catds.fr/), CEDA datasets at (https://archive.ceda.ac.uk/), and ERA5 data sets at (https://cds.climate.copernicus.eu/).
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