SCI(E)

2026

1. Shah, S.[PG], Liu, Y., Kim, S., & Sharma, A. (2026). Constrained negentropy optimisation (CoNE-opt): Using independent components to merge satellite data products. Remote Sensing of Environment, 334, 115170. 

2025

2. Han, K.[PG], Kim, S., Mehrotra, R., & Sharma, A. (2025). A Scalable Framework for Reservoir Water Level Monitoring Using Optical–SAR Integration in Data-Sparse Regions. IEEE Transactions on Geoscience and Remote Sensing, 63, 4212312.

3. Guragain, S., Shah, S., Albano, R., Kim, S., Hammad, M., & Asif, M. (2025). Quantifying the Added Values of a Merged Precipitation Product in Streamflow Prediction over the Central Himalayas. Remote Sensing, 17(13), 2170.

4. Hagan, D. F., Kim, S., Wang, G., Ma, X., Hu, Y., Liu, Y. Y., ... & Bhatti, S. A. (2025). Seamless finer-resolution soil moisture from the synergistic merging of the FengYun-3 satellite series. Scientific Data, 12(1), 1-14. 

5. Sharma, Y. K.[PG], Mohanasundaram, S., Kim, S., Shrestha, S., Babel, M. S., & Loc, H. H. (2025). Enhancing Aquifer Reliability and Resilience Assessment in Data-Scarce Regions Using Satellite Data: Application to the Chao Phraya River Basin. Remote Sensing, 17(10), 1731.

6. Yoon, H.N., Marshall, L., Sharma, A., & Kim, S. (2025). Doing Hydrology when no in-situ data exists: Surrogate River discharge Model (SRM). Environmental Modelling & Software, 186, 106334. 

7. Sharma, Y. K.[PG], Kim, S., Tayerani Charmchi, A. S., Kang, D., & Batelaan, O. (2025). Strategic Imputation of Groundwater Data Using Machine Learning: Insights from Diverse Aquifers in the Chao-Phraya River Basin. Groundwater for Sustainable Development, 28,  101394. 

2024

8. Shah, S., Liu, Y., Kim, S., & Sharma, A. (2024). Advancing High-Mountain Precipitation Reconstruction through Merging of Multiple Data Sources: Triple Collocation versus Signal-to-Noise Ratio Optimization. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15.

9. Han, K.[PG], Kim, S., Mehrotra, R., & Sharma, A. (2024). Enhanced Water Level Monitoring for Small and Complex Inland Water Bodies Using Multi-Satellite Remote Sensing. Environmental Modelling & Software, 106169. 

10. Kim, S., Wasko, C., Sharma, A., & Nathan, R. (2024). The role of regional water vapor dynamics in creating precipitation extremes. Journal of Hydrology X, 24, 100181. 

11. Sinha, J., Sharma, A., Marshall, L., & Kim, S. (2024). Characterizing Satellite Soil Moisture Drydown: A Bivariate Filtering Approach. Water Resources Research, 60(7), e2022WR034019. 

12. Yoo, S., Kim, S., & Paik, K. (2024). Optimal combinations of global evapotranspiration and terrestrial water storage products for catchment water balance. International Journal of Remote Sensing, 45(9), 2865-2892.

13. Liu, S., Kim, S., Glamore, W., Tamburic, B., & Johnson, F. (2024). Remote sensing of water colour in small southeastern Australian waterbodies. Journal of Environmental Management, 352, 120096.

2023

14. Zhang, R., Kim, S., Kim, H., Fang, B., Sharma, A., & Lakshmi, V. (2023). Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting, Water Resources Research, 59(12), e2023WR034457.

2022

15. Visser, J., Kim, S., Wasko, C., Nathan, R., & Sharma, A. (2022) The impact of climate change on operational estimates of Probable Maximum Precipitation, Water Resources Research, 58(11), e2022WR032247.

16. He, W.[UG], Kim, S., Wasko, C., & Sharma, A. (2022). A global assessment of change in flood volume with surface air temperature, Advances in Water Resources, 165, 104241.

17. Lee, S., Kim, S., & Moon, S. (2022). Development of Car-free Street Mapping (CfSM) Model using an Integrated System with Unmanned Aerial Vehicle, Aerial Mapping Camera and Deep Learning Algorithm. Journal of Computing in Civil Engineering, 36(3), 04022003.

18. Kim, S., Sharma, A., Liu, Y., & Young, S.I. (2022). Rethinking Satellite Data Merging: From Averaging to SNR Optimization. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15.

19. Yoon, H.N., Marshall, L., Sharma, A., & Kim, S. (2022). Bayesian model calibration using surrogate streamflow in ungauged catchments. Water Resources Research, 58(1), e2021WR031287. 

20. Kim, S., Sharma, A., Wasko, C., & Nathan R. (2022). Linking total precipitable water to precipitation extremes globally. Earth’s Future, 10(2), e2021EF002473.

2021

21. Kim, S.[PG], Mehrotra, R., Kim, S., & Sharma, A. (2021). Assessing countermeasure effectiveness in controlling cyanobacterial exceedance in riverine systems using probabilistic forecasting alternatives. Journal of Water Resources Planning and Management, 147(10), 04021062.

22. Kim, S.[PG], Mehrotra, R., Kim, S., & Sharma, A. (2021). Probabilistic forecasting of cyanobacterial concentration in riverine systems using environmental drivers. Journal of Hydrology, 593, 125626.

23. Kim, S., Anabalon, A., & Sharma, A. (2021). An Assessment of Concurrency in Evapotranspiration Trends Across Multiple Global Datasets. Journal of Hydrometeorology, 22(1), 231-244. 

24. Kim, S., Dong, J., & Sharma, A. (2021). A triple collocation-based comparison of three L-band soil moisture datasets, SMAP, SMOS-IC, and SMOS, over varied climates and land covers. Frontiers in Water, 3, 64.

25. Zhang, R.[PG], Kim, S., Sharma, A., & Lakshmi, V. (2021). Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability. Remote Sensing of Environment, 252, 112126.

2020

26. Kim, S., Pham, H., Liu, Y., Marshall, L. & Sharma, A. (2020). Improving the combination of satellite soil moisture datasets by considering error cross-correlation: A comparison between triple collocation (TC) and extended double instrumental variable (EIVD) alternatives. IEEE Transactions on Geoscience and Remote Sensing, 59(9), 7285-7295. 

27. Kim, S., Ajami, H., & Sharma, A. (2020). Using Remotely Sensed Information to Improve Vegetation Parameterization in a Semi-Distributed Hydrological Model (SMART) for Upland Catchments in Australia. Remote Sensing, 12(18), 3051.

28. Magan, B.[UG], Kim, S., Wasko, C., Barbero, R., Moron, V., Nathan, R., & Sharma, A. (2020). Impact of atmospheric circulation on the rainfall-temperature relationship in Australia. Environmental Research Letters, 15(9), 094098. 

29. Moradi, S., Agostino, A., Gandomkar, Z., Kim, S., Hamilton, L., Sharma, A., Henderson, R. & Leslie, G. (2020). Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms. h2oj, 3(1), 328-342. 

30. Kim, S., Eghdamirad, S., Sharma, A., & Kim, J. H. (2020). Quantification of uncertainty in projections of extreme daily precipitation. Earth and Space Science, 7(8), e2019EA001052. 

31. Hagan, D.F.T., Wang, G., Kim, S., Parinussa, R.M., Liu, Y., Ullah, W., Bhatti, A.S., Ma, X., Jiang, T. and Su, B., 2020. Maximizing temporal correlations in long-term global satellite soil moisture data-merging. Remote Sensing, 12(13), 2164.

32. Kim, S.[PG], Kim, S., Mehrotra, R., & Sharma, A. (2020). Predicting cyanobacteria occurrence using climatological and environmental controls. Water Research, 115639.

33. Kim, T., Ley, M. T., Kang, S., Davis, J. M., Kim, S., & Amrollahi, P. (2020). Using particle composition of fly ash to predict concrete strength and electrical resistivity. Cement and Concrete Composites, 107, 103493. 

2019

34. Pham, H. T., Kim, S., Marshall, L., & Johnson, F. (2019). Using 3D robust smoothing to fill land surface temperature gaps at the continental scale. International Journal of Applied Earth Observation and Geoinformation, 82, 101879.

35. Kim, S., Jun, H. D., Yoo, D. G., & Kim, J. H. (2019). A framework for improving reliability of water distribution systems based on a segment-based minimum cut-set approach. Water, 11(7), 1524.

36. Zhang, R.[PG], Kim, S., & Sharma, A. (2019). A comprehensive validation of the SMAP Enhanced Level-3 Soil Moisture product using ground measurements over varied climates and landscapes. Remote Sensing of Environment, 223, 82-94.

37. Kim, S., & Sharma, A. (2019). The role of floodplain topography in deriving basin discharge using passive microwave remote sensing. Water Resources Research, 55(2), 1707-1716. 

2018

38. Khan, U., Ajami, H., Tuteja, N. K., Sharma, A., & Kim, S. (2018). Catchment scale simulations of soil moisture dynamics using an equivalent cross-section based hydrological modelling approach. Journal of Hydrology, 564, 944-966.

39. Kim, S., Paik, K., Johnson, F. M., & Sharma, A. (2018). Building a flood-warning framework for ungauged locations using low resolution, open-access remotely sensed surface soil moisture, precipitation, soil, and topographic information. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 375-387.

2017

40. Kim, S., Balakrishnan, K., Liu, Y., Johnson, F., & Sharma, A. (2017). Spatial disaggregation of coarse soil moisture data by using high-resolution remotely sensed vegetation products. IEEE Geoscience and Remote Sensing Letters, 14(9), 1604-1608. 

2016

41. Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., & Sharma, A. (2016). Merging alternate remotely-sensed soil moisture retrievals using a non-static model combination approach. Remote Sensing, 8(6), 518. 

2015

42. Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., & Sharma, A. (2015). A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation. Geophysical Research Letters, 42(16), 6662-6670. 

43. Kim, S., Liu, Y. Y., Johnson, F. M., Parinussa, R. M., & Sharma, A. (2015). A global comparison of alternate AMSR2 soil moisture products: Why do they differ? Remote Sensing of Environment, 161, 43-62. 

1. Kim, S., Lee, G. & Sharma, A. Evaluating the impact of rainfall duration on the relationship between atmospheric moisture and extreme precipitation, MODSIM 2023, Darwin, Australia

2. Kim, S., Sharma, A., Wasko, C., & Nathan, R. How does total precipitable water link to precipitation extremes?, MODSIM 2021, Sydney, Australia

3. Kim, S., Zhang, R., Sharma, A., & Lakshmi, V. Improvements of satellite observations through data merging: status and challenges, AGU fall meeting 2020, San Francisco, CA, USA

4. Kim, S., Pham, H., Liu, Y., Sharma, A., & Marshall, L. Combining geophysical variables for maximizing temporal correlation without reference data, MODSIM 2019, Canberra, Australia

5. Kim, S. [Invited], Guo, Y., Wasko, C., & Sharma, A. On soil moisture, rain and flood extremes in a warming climate – using satellite remote sensing to define future antecedent conditions, KSCC 2018, Jeju, Republic of Korea

6. Kim, S., Ajami, H., & Sharma, A. Incorporating an operational satellite-derived leaf area index into a computationally efficient semi-distributed hydrologic modelling application (SMART), MODSIM 2017, Hobart, Australia

7. Kim, S., Liu, Y., Johnson, F., & Sharma, A. A temporal correlation-based approach for spatial disaggregation of remotely sensed soil moisture, AGU fall meeting 2016, San Francisco, CA, USA

8. Kim, S., Liu, Y., Johnson, F., Parinussa, R., & Sharma, A. Reducing Structural Uncertainty in AMSR2 Soil Moisture Using a Model Combination Approach, AGU fall meeting 2014, San Francisco, CA, USA

9. Kim, S., Liu, Y., Johnson, F., Parinussa, R., & Sharma, A. Improvement of Soil Moisture Dataset Combining AMSR2 Soil Moisture Products, OzEWEX 2014, Canberra, ACT, Australia