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The analysis of using satellite soil moisture observations for flood detection, evaluating over the Thailand’s Great Flood of 2011

Author

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  • Natthachet Tangdamrongsub

    (University of Maryland
    NASA Goddard Space Flight Center)

  • Chalita Forgotson

    (NASA Goddard Space Flight Center
    Science Applications International Corporation)

  • Chandana Gangodagamage

    (University of Maryland
    NASA Goddard Space Flight Center)

  • Joshua Forgotson

    (ICF)

Abstract

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyzes the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS, the Advanced SCATterometer on MetOp, the Global Land Data Assimilation System, and the WaterGAP Global Hydrology Model. In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission, and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Suggested Citation

  • Natthachet Tangdamrongsub & Chalita Forgotson & Chandana Gangodagamage & Joshua Forgotson, 2021. "The analysis of using satellite soil moisture observations for flood detection, evaluating over the Thailand’s Great Flood of 2011," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2879-2904, September.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:3:d:10.1007_s11069-021-04804-8
    DOI: 10.1007/s11069-021-04804-8
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    References listed on IDEAS

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    1. Jinghua Xiong & Zhaoli Wang & Shenglian Guo & Xushu Wu & Jiabo Yin & Jun Wang & Chengguang Lai & Qiangjun Gong, 2022. "High effectiveness of GRACE data in daily-scale flood modeling: case study in the Xijiang River Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(1), pages 507-526, August.

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