Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-169-2023
© Author(s) 2023. 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-27-169-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jan 2023
Research article |
| 10 Jan 2023
| 10 Jan 2023
SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation
Kunlong He
Institute of Mountain Hazards and Environment, Chinese Academy of
Sciences, Chengdu 610299, China
School of Civil Engineering, Sun Yat-sen University, Guangzhou 510275, China
Institute of Mountain Hazards and Environment, Chinese Academy of
Sciences, Chengdu 610299, China
Luca Brocca
Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
Pere Quintana-Seguí
Ebro Observatory (OE), Ramon Llull University – CSIC, Roquetes,
Spain
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Cited
15 citations as recorded by crossref.
- Advancing Satellite-Derived Precipitation Downscaling in Data-Sparse Area Through Deep Transfer Learning H. Zhu & Q. Zhou 10.1109/TGRS.2024.3367332
- SMPD-MERG: A Hybrid Downscaling Model for High-Resolution Daily Precipitation Estimation via Merging Surface Soil Moisture and Multisource Precipitation Data K. He et al. 10.1109/TGRS.2025.3561253
- A refined method for the simulation of catchment rainfall–runoff based on satellite–precipitation downscaling J. Jian et al. 10.1016/j.jhydrol.2025.132795
- Feasibility of Downscaling Satellite-Based Precipitation Estimates Using Soil Moisture Derived from Land Surface Temperature A. Strehz et al. 10.3390/atmos14030435
- Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery S. Karalasingham et al. 10.1016/j.rsase.2024.101333
- A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information K. He et al. 10.1016/j.scitotenv.2023.162378
- A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network M. Sun et al. 10.1109/JSTARS.2024.3373884
- Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product K. He et al. 10.1016/j.jhydrol.2024.131536
- Machine-learning downscaling of GPM satellite precipitation products in mountainous regions: A case study in Chongqing Y. Gan et al. 10.1016/j.atmosres.2024.107698
- Fusion of Surface Soil Moisture Data for Spatial Downscaling of Daily Satellite Precipitation Data Q. Wang et al. 10.1109/JSTARS.2023.3336930
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
- Comprehensive performance evaluation of satellite-based and reanalysis rainfall estimate products in Ethiopia: For drought, flood, and water resources applications. D. Wodebo et al. 10.1016/j.ejrh.2024.102150
- Exploring machine learning approaches for precipitation downscaling H. Zhu et al. 10.1080/10095020.2025.2477547
- Evaluation of the Effectiveness of Downscaled Precipitation Data in Drought Monitoring P. Ji & Q. Wang 10.1109/JSTARS.2024.3502691
- Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study Y. Gong et al. 10.3390/land13091374
15 citations as recorded by crossref.
- Advancing Satellite-Derived Precipitation Downscaling in Data-Sparse Area Through Deep Transfer Learning H. Zhu & Q. Zhou 10.1109/TGRS.2024.3367332
- SMPD-MERG: A Hybrid Downscaling Model for High-Resolution Daily Precipitation Estimation via Merging Surface Soil Moisture and Multisource Precipitation Data K. He et al. 10.1109/TGRS.2025.3561253
- A refined method for the simulation of catchment rainfall–runoff based on satellite–precipitation downscaling J. Jian et al. 10.1016/j.jhydrol.2025.132795
- Feasibility of Downscaling Satellite-Based Precipitation Estimates Using Soil Moisture Derived from Land Surface Temperature A. Strehz et al. 10.3390/atmos14030435
- Wavelet-fusion image super-resolution model with deep learning for downscaling remotely-sensed, multi-band spectral albedo imagery S. Karalasingham et al. 10.1016/j.rsase.2024.101333
- A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information K. He et al. 10.1016/j.scitotenv.2023.162378
- A New Spatial Downscaling Method for Long-Term AVHRR NDVI by Multiscale Residual Convolutional Neural Network M. Sun et al. 10.1109/JSTARS.2024.3373884
- Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product K. He et al. 10.1016/j.jhydrol.2024.131536
- Machine-learning downscaling of GPM satellite precipitation products in mountainous regions: A case study in Chongqing Y. Gan et al. 10.1016/j.atmosres.2024.107698
- Fusion of Surface Soil Moisture Data for Spatial Downscaling of Daily Satellite Precipitation Data Q. Wang et al. 10.1109/JSTARS.2023.3336930
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
- Comprehensive performance evaluation of satellite-based and reanalysis rainfall estimate products in Ethiopia: For drought, flood, and water resources applications. D. Wodebo et al. 10.1016/j.ejrh.2024.102150
- Exploring machine learning approaches for precipitation downscaling H. Zhu et al. 10.1080/10095020.2025.2477547
- Evaluation of the Effectiveness of Downscaled Precipitation Data in Drought Monitoring P. Ji & Q. Wang 10.1109/JSTARS.2024.3502691
- Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study Y. Gong et al. 10.3390/land13091374
Latest update: 31 May 2025
Short summary
In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the GPM daily precipitation product by exploiting the connection between surface soil moisture and precipitation according to the soil water balance equation. Based on this physical method, the spatial resolution of the daily precipitation product was downscaled to 1 km and the SMPD method shows good potential for the development of the high-resolution precipitation product.
In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for...
