Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1569-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-1569-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Mar 2021
Research article |
| 29 Mar 2021
| 29 Mar 2021
The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis
Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
Southern Laboratory of Ocean Science and Engineering (Guangdong,
Zhuhai), Zhuhai, 519000, China
Jianzhi Dong
USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Wade T. Crow
USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Xiaohu Zhang
National Engineering and Technology Center for Information
Agriculture, Nanjing Agricultural University, Nanjing, China
Jiangsu Key Laboratory for Information Agriculture, Nanjing
Agricultural University, Nanjing, China
Rolf H. Reichle
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD, USA
Gabrielle J. M. De Lannoy
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
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Cited
13 citations as recorded by crossref.
- Microwave-based soil moisture improves estimates of vegetation response to drought in China J. Qiu et al. 10.1016/j.scitotenv.2022.157535
- Bias correction of satellite soil moisture through data assimilation J. Qin et al. 10.1016/j.jhydrol.2022.127947
- Simulation of Sentinel-1A observations and constraint of water cloud model at the regional scale using a discrete scattering model X. Bai et al. 10.1016/j.rse.2022.113308
- Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China A. Nadeem et al. 10.3390/rs14040982
- Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland S. Feng et al. 10.1016/j.jhydrol.2022.129015
- Historical Drought Events in the Early Years of Qing Dynasty in Shanxi Based on Hydrological Reconstructions Y. Qu et al. 10.3390/w15050995
- Can real-time NDVI observations better constrain SMAP soil moisture retrievals? S. Feng et al. 10.1016/j.rse.2024.114569
- A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions F. Brakhasi et al. 10.1109/JSTARS.2023.3344764
- Deducing land–atmosphere coupling regimes from SMAP soil moisture P. Makhasana et al. 10.5194/hess-28-5087-2024
- Long-Term Vegetation Phenology Changes and Responses to Preseason Temperature and Precipitation in Northern China R. Zhang et al. 10.3390/rs14061396
- Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events X. Fan et al. 10.3390/rs14143339
- Leveraging Pre‐Storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins W. Crow et al. 10.1029/2021WR031565
- Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019 Q. Zhang et al. 10.5194/essd-13-1385-2021
12 citations as recorded by crossref.
- Microwave-based soil moisture improves estimates of vegetation response to drought in China J. Qiu et al. 10.1016/j.scitotenv.2022.157535
- Bias correction of satellite soil moisture through data assimilation J. Qin et al. 10.1016/j.jhydrol.2022.127947
- Simulation of Sentinel-1A observations and constraint of water cloud model at the regional scale using a discrete scattering model X. Bai et al. 10.1016/j.rse.2022.113308
- Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China A. Nadeem et al. 10.3390/rs14040982
- Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland S. Feng et al. 10.1016/j.jhydrol.2022.129015
- Historical Drought Events in the Early Years of Qing Dynasty in Shanxi Based on Hydrological Reconstructions Y. Qu et al. 10.3390/w15050995
- Can real-time NDVI observations better constrain SMAP soil moisture retrievals? S. Feng et al. 10.1016/j.rse.2024.114569
- A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-Bands Under Bare and Vegetated Soil Conditions F. Brakhasi et al. 10.1109/JSTARS.2023.3344764
- Deducing land–atmosphere coupling regimes from SMAP soil moisture P. Makhasana et al. 10.5194/hess-28-5087-2024
- Long-Term Vegetation Phenology Changes and Responses to Preseason Temperature and Precipitation in Northern China R. Zhang et al. 10.3390/rs14061396
- Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events X. Fan et al. 10.3390/rs14143339
- Leveraging Pre‐Storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins W. Crow et al. 10.1029/2021WR031565
1 citations as recorded by crossref.
Latest update: 16 Sep 2025
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.
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use...
