An Assessment of Concurrency in Evapotranspiration Trends Across Multiple Global Datasets
Kim, S., Anabalon, A., Sharma, A.
Journal of Hydrometeorology, 2021
22
A triple collocation-based comparison of three L-band soil moisture datasets, SMAP, SMOS-IC, and SMOS, over varied climates and land covers
Kim, S., Dong, J., Sharma, A.
Frontiers in Water, 2021
23
Identifying relative strengths of SMAP, SMOS-IC, and ASCAT to capture temporal variability
Zhang, R.[PG], Kim, S., Sharma, A., Lakshmi, V.
Remote Sensing of Environment, 2021
24
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
Kim, S., Pham, H., Liu, Y., Marshall, L., Sharma, A.
IEEE Transactions on Geoscience and Remote Sensing, 2020
25
Using Remotely Sensed Information to Improve Vegetation Parameterization in a Semi-Distributed Hydrological Model (SMART) for Upland Catchments in Australia
Kim, S., Ajami, H., Sharma, A.
Remote Sensing, 2020
26
Impact of atmospheric circulation on the rainfall-temperature relationship in Australia
Magan, B.[UG], Kim, S., Wasko, C., Barbero, R., Moron, V., Nathan, R., Sharma, A.
Environmental Research Letters, 2020
27
Quantifying natural organic matter concentration in water from climatological parameters using different machine learning algorithms
Moradi, S., Agostino, A., Gandomkar, Z., Kim, S., Hamilton, L., Sharma, A., Henderson, R., Leslie, G.
h2oj, 2020
28
Quantification of uncertainty in projections of extreme daily precipitation
Kim, S., Eghdamirad, S., Sharma, A., Kim, J. H.
Earth and Space Science, 2020
29
Maximizing temporal correlations in long-term global satellite soil moisture data-merging
Hagan, D.F.T., Wang, G., Kim, S., Parinussa, R.M., Liu, Y., Ullah, W., Bhatti, A.S., Ma, X., Jiang, T., Su, B.
Remote Sensing, 2020
30
Predicting cyanobacteria occurrence using climatological and environmental controls