Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1569-2021
https://doi.org/10.5194/hess-25-1569-2021
Research article
 | 
29 Mar 2021
Research article |  | 29 Mar 2021

The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy

Related authors

A 1 km daily soil moisture dataset over China using in situ measurement and machine learning
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, and Yongjiu Dai
Earth Syst. Sci. Data, 14, 5267–5286, https://doi.org/10.5194/essd-14-5267-2022,https://doi.org/10.5194/essd-14-5267-2022, 2022
Short summary
A multi-isotope model for simulating soil organic carbon cycling in eroding landscapes (WATEM_C v1.0)
Zhengang Wang, Jianxiu Qiu, and Kristof Van Oost
Geosci. Model Dev., 13, 4977–4992, https://doi.org/10.5194/gmd-13-4977-2020,https://doi.org/10.5194/gmd-13-4977-2020, 2020
Short summary
Model representation of the coupling between evapotranspiration and soil water content at different depths
Jianxiu Qiu, Wade T. Crow, Jianzhi Dong, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 24, 581–594, https://doi.org/10.5194/hess-24-581-2020,https://doi.org/10.5194/hess-24-581-2020, 2020
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Remote Sensing and GIS
A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin
Mohammed Abdallah, Ke Zhang, Lijun Chao, Abubaker Omer, Khalid Hassaballah, Kidane Welde Reda, Linxin Liu, Tolossa Lemma Tola, and Omar M. Nour
Hydrol. Earth Syst. Sci., 28, 1147–1172, https://doi.org/10.5194/hess-28-1147-2024,https://doi.org/10.5194/hess-28-1147-2024, 2024
Short summary
Extent of gross underestimation of precipitation in India
Gopi Goteti and James Famiglietti
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-18,https://doi.org/10.5194/hess-2024-18, 2024
Revised manuscript accepted for HESS
Short summary
Improved soil evaporation remote sensing retrieval algorithms and associated uncertainty analysis on the Tibetan Plateau
Jin Feng, Ke Zhang, Huijie Zhan, and Lijun Chao
Hydrol. Earth Syst. Sci., 27, 363–383, https://doi.org/10.5194/hess-27-363-2023,https://doi.org/10.5194/hess-27-363-2023, 2023
Short summary
SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation
Kunlong He, Wei Zhao, Luca Brocca, and Pere Quintana-Seguí
Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023,https://doi.org/10.5194/hess-27-169-2023, 2023
Short summary
Evaluating the accuracy of gridded water resources reanalysis and evapotranspiration products for assessing water security in poorly gauged basins
Elias Nkiaka, Robert G. Bryant, Joshua Ntajal, and Eliézer I. Biao
Hydrol. Earth Syst. Sci., 26, 5899–5916, https://doi.org/10.5194/hess-26-5899-2022,https://doi.org/10.5194/hess-26-5899-2022, 2022
Short summary

Cited articles

Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., and Smets, B.: GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part 1: Principles of development and production, Remote Sens. Environ., 137, 299–309, https://doi.org/10.1016/j.rse.2013.02.030, 2013. 
Bolten, J. D. and Crow, W. T.: Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture, Geophys. Res. Lett., 39, L19406, https://doi.org/10.1029/2012GL053470, 2012. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Chan, S., Njoku, E. G., and Colliander A.: SMAP L1C radiometer half-orbit 36 km EASE-Grid brightness temperatures, version 3, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/E51BSP6V3KP7, 2016. 
Chen, F., Crow, W. T., Starks, P. J., and Moriasi, D. N.: Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture, Adv. Water Resour., 34, 526–536, https://doi.org/10.1016/j.advwatres.2011.01.011, 2011. 
Download
Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.