Publications

SM2RAIN rainfall estimation is limited by infiltration physics and soil moisture depth. This study introduces a Green-Ampt based approach and optimizes parameters using Bayesian methods, improving rainfall retrieval accuracy, though performance decreases with depth. Results highlight land cover dependent behavior and provide more reliable rainfall estimates with quantified uncertainty.

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This study presents a refined optimal estimation approach to simultaneously retrieve soil moisture and soil organic matter from microwave dielectric measurements. Unlike conventional models that focus only on soil moisture, the proposed method explicitly incorporates organic matter into a continuous dielectric mixing framework, reducing ambiguity in dual-parameter estimation. Validation using SMAPVEX12 field data demonstrates strong agreement for both soil moisture and organic matter estimates. The approach improves retrieval accuracy and enables the use of microwave sensors for assessing soil carbon content, with direct applications in irrigation management, soil health monitoring, and carbon accounting.

This study introduces a self-calibration framework for bottom-up rainfall estimation that eliminates the need for rain gauge data by using clustering-based parameter identification. Applied to SM2RAIN-NWF, the approach demonstrates robust performance across validation strategies, with K-means and Growing Neural Gas showing strong reliability and accuracy, making it well-suited for large-scale and data-sparse regions.

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This paper demonstrates that simultaneously assimilating radar and radiometer soil moisture retrievals produces measurable improvements in numerical weather prediction, beyond what is achieved using a single sensor.

By jointly using ASCAT radar soil moisture and SMAP radiometer soil moisture within the Korean Integrated Model coupled with the NASA Land Information System, the study shows how complementary microwave observations strengthen land atmosphere coupling in forecasting systems.

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Accurate flood maps are critical for early warning and disaster response. GNSS-Reflectometry (GNSS-R) at L-band can detect flooding, but land surface conditions and the sensor’s geometry make it challenging. In this study, we use machine learning to estimate how much of an area is covered by water using GNSS-R data from CYGNSS, supported by land surface information. High-resolution flood maps from Sentinel-1 SAR are used as reference data to train the model.

The approach combines flood detection and water fraction estimation, with a sequential model showing the best performance. Across the United States, our method produces daily flood estimates at 3-km resolution, with good agreement to Sentinel-1 data. Comparisons with other flood products confirm that our CYGNSS-based model reliably maps inundation at fine spatial and temporal scales.

This study introduces a novel self-calibrating framework that combines supervised and unsupervised clustering with genetic algorithm optimization to enhance the SM2RAIN-NWF algorithm for accurate, calibration-free, continental-scale rainfall estimation from soil moisture dynamics across diverse environmental conditions.

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