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Monitoring the drought in Southern Africa from space-borne GNSS-R and SMAP data

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Abstract

Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term.

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Funding

Financial supports of this work were provided by Shanghai Leading Talent Project (Grant No. E056061).

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Authors and Affiliations

  1. School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Komi Edokossi, Shuanggen Jin, Usman Mazhar & Iñigo Molina

  2. Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai, 200030, China

    Shuanggen Jin

  3. School of Land Surveying, Geodesy and Mapping Engineering, Universidad Politécnica de Madrid, South Campus, 28031, Madrid, Spain

    Iñigo Molina

  4. Department of Physics and Mathematics, University of Alcala, Alcalá de Henares, Spain

    Andres Calabia

  5. School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Irfan Ullah

  6. Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Centre On Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

    Irfan Ullah

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  1. Komi Edokossi
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Contributions

Data curation, K.E.; funding acquisition, S.J.; methodology, K.E., S.J. and I.M.; computation, K.E. and U.M.; supervision, S.J. and I.M.; writing—original draft, K.E.; writing—review and editing, S.J., I.M., A.C. and I.U. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Shuanggen Jin.

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Edokossi, K., Jin, S., Mazhar, U. et al. Monitoring the drought in Southern Africa from space-borne GNSS-R and SMAP data. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06546-9

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