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Evaluation of Sentinel-1 Satellite-based Soil Moisture Products for Runoff Modelling with Karst Formation Characteristics

Author

Listed:
  • Hanggar Ganara Mawandha

    (Universitas Gadjah Mada)

  • Afinafghani Duta Pratama

    (Universitas Gadjah Mada)

  • M. Ramadhan Al Ghifari

    (Universitas Gadjah Mada)

  • Nasywa Hanin Hanifah

    (Universitas Gadjah Mada)

  • Issiami Nursafa

    (Universitas Gadjah Mada)

  • Prieskarinda Lestari

    (Universitas Gadjah Mada)

  • Satoru Oishi

    (Kobe University)

Abstract

High soil moisture levels reduce the soil’s ability to absorb rainfall, leading to increased surface runoff. In karst regions, the presence of underground channels and conduits means that water can quickly move through the subsurface. High soil moisture can lead to accelerated groundwater flow through these karst features. Increased subsurface flow might result in delayed but intense surface flooding. Recently, remote sensing technology has demonstrated considerable potential for the assessment of soil moisture conditions. This research aims to identify the soil moisture characteristics in karst formations for runoff estimation based on remote sensing imagery obtained from the Sentinel-1 satellite. The soil moisture was calculated by the TOPP equation based on the soil dielectric value obtained from the Dubois model. By employing a variety of land use types and soil moisture data obtained from the Sentinel-1 satellite, Curve Number (CN) values were generated and subsequently utilized to estimate runoff. The remaining biases in the estimation were attributable to factors such as dense shade, land slope, and cloud cover. Based on the extracted vertical transmit-vertical receive polarization in Sentinel-1 A for karst regions, soil moisture was calculated as 0.055 cm3/cm3. The model produces real-time Curve Number-based Soil Moisture data that can be integrated with a rainfall runoff model utilizing the SCS-CN, so-called CN-SM model. The runoff estimate gives a difference of 0.052 m³/s greater then the observed runoff value. As a result of these findings, soil moisture monitoring is essential for determining CN values accurately for runoff estimates.

Suggested Citation

  • Hanggar Ganara Mawandha & Afinafghani Duta Pratama & M. Ramadhan Al Ghifari & Nasywa Hanin Hanifah & Issiami Nursafa & Prieskarinda Lestari & Satoru Oishi, 2025. "Evaluation of Sentinel-1 Satellite-based Soil Moisture Products for Runoff Modelling with Karst Formation Characteristics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(2), pages 821-846, January.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:2:d:10.1007_s11269-024-03992-9
    DOI: 10.1007/s11269-024-03992-9
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    References listed on IDEAS

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    1. Saad Mazhar Khan & Imran Shafi & Wasi Haider Butt & Isabel de la Torre Diez & Miguel Angel López Flores & Juan Castanedo Galán & Imran Ashraf, 2023. "A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions," Land, MDPI, vol. 12(8), pages 1-37, July.
    2. Vijay P. Santikari & Lawrence C. Murdoch, 2019. "Accounting for Spatiotemporal Variations of Curve Number Using Variable Initial Abstraction and Antecedent Moisture," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 641-656, January.
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