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

Hanggar Ganara Mawandha (), Afinafghani Duta Pratama, M. Ramadhan Al Ghifari, Nasywa Hanin Hanifah, Issiami Nursafa, Prieskarinda Lestari and Satoru Oishi
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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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 2, No 15, 846 pages

Abstract: 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.

Keywords: Soil Moisture; Karst; Runoff; Sentinel-1; Curve Number; TOPP; Dubois (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-03992-9

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