Articles | Volume 27, issue 2
https://doi.org/10.5194/hess-27-577-2023
© 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-27-577-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jan 2023
Research article |
| 30 Jan 2023
| 30 Jan 2023
A robust gap-filling approach for European Space Agency Climate Change Initiative (ESA CCI) soil moisture integrating satellite observations, model-driven knowledge, and spatiotemporal machine learning
Kai Liu
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
Institute at Brown for Environment and Society, Brown University,
Providence, RI 02912, USA
Shudong Wang
CORRESPONDING AUTHOR
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing 210044, China
Hongyan Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences,
Beijing 100094, China
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Cited
26 citations as recorded by crossref.
- Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China A. Nadeem et al.
- Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method L. Li et al.
- A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China Y. Duan et al.
- Runoff prediction under climatic variability using SWAT and machine learning models: a case study of the Hunza River basin M. Kareem et al.
- Assessing reclamation potential of abandoned drylands using knowledge-guided machine learning (KGML) and remote sensing K. Liu et al.
- Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism D. Zhang et al.
- Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States M. Valipour et al.
- Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction Y. Liu et al.
- Addressing spatial gaps in ESA CCI soil moisture product: A hierarchical reconstruction approach using deep learning model T. Ding et al.
- Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting R. Zhang et al.
- Adaptive Gap-Filling of Multispectral Images at Coarse and Fine Spatial Resolution S. Afsharipour et al.
- Relationship between carbon pool changes and environmental changes in arid and semi-arid steppe—A two decades study in Inner Mongolia, China H. Li et al.
- Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al.
- Spatially Seamless Error Characterization of ERA5, GLDAS, GLEAM, and MERRA2 ET Products Using Quadruple Collocation Analysis and Random Forest W. Yue et al.
- Detecting the human fingerprint in the summer 2022 western–central European soil drought D. Schumacher et al.
- Evaluating thermal conductivity of soil-rock mixtures in Qinghai-Tibet plateau based on theory models and machine learning methods Q. Wang et al.
- İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması F. Sönmez Erdoğan & M. Erdoğan
- Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020 A. Zhao et al.
- A regionalization based machine learning framework for bias correction and downscaling of ESACCI soil moisture in data limited region: A case study over India I. Dasari & V. Vema
- Towards a Global Water Use Scarcity Risk Assessment Framework: Integration of Remote Sensing and Geospatial Datasets Y. Wang et al.
- ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates W. Preimesberger et al.
- A high performance assimilation of surface soil moisture based on a hybrid framework of machine learning and physical hydrological model S. Zhu et al.
- Machine and deep learning in geological applications: a review of advances, challenges, and future research directions N. Hammouri et al.
- Evaluating satellite-based precipitation products for spatiotemporal drought analysis H. Khan et al.
- Graph-Transformer for Spatiotemporal Soil Moisture Forecasting Using Multimodal Remote Sensing Data M. Saki et al.
- AI in soil moisture remote sensing C. Montzka et al.
26 citations as recorded by crossref.
- Spatial Downscaling and Gap-Filling of SMAP Soil Moisture to High Resolution Using MODIS Surface Variables and Machine Learning Approaches over ShanDian River Basin, China A. Nadeem et al.
- Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method L. Li et al.
- A Deep Learning Framework for Long-Term Soil Moisture-Based Drought Assessment Across the Major Basins in China Y. Duan et al.
- Runoff prediction under climatic variability using SWAT and machine learning models: a case study of the Hunza River basin M. Kareem et al.
- Assessing reclamation potential of abandoned drylands using knowledge-guided machine learning (KGML) and remote sensing K. Liu et al.
- Spatial Downscaling of ESA CCI Soil Moisture Data Based on Deep Learning with an Attention Mechanism D. Zhang et al.
- Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States M. Valipour et al.
- Reconstruction of SMAP Soil Moisture Data Based on Residual Autoencoder Network with Convolutional Feature Extraction Y. Liu et al.
- Addressing spatial gaps in ESA CCI soil moisture product: A hierarchical reconstruction approach using deep learning model T. Ding et al.
- Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting R. Zhang et al.
- Adaptive Gap-Filling of Multispectral Images at Coarse and Fine Spatial Resolution S. Afsharipour et al.
- Relationship between carbon pool changes and environmental changes in arid and semi-arid steppe—A two decades study in Inner Mongolia, China H. Li et al.
- Gap‐Filled Multivariate Observations of Global Land–Climate Interactions V. Bessenbacher et al.
- Spatially Seamless Error Characterization of ERA5, GLDAS, GLEAM, and MERRA2 ET Products Using Quadruple Collocation Analysis and Random Forest W. Yue et al.
- Detecting the human fingerprint in the summer 2022 western–central European soil drought D. Schumacher et al.
- Evaluating thermal conductivity of soil-rock mixtures in Qinghai-Tibet plateau based on theory models and machine learning methods Q. Wang et al.
- İnsansız Hava Aracı Kullanarak Toprak Neminin Mısır Tarlası Örneğinde Haritalanması F. Sönmez Erdoğan & M. Erdoğan
- Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020 A. Zhao et al.
- A regionalization based machine learning framework for bias correction and downscaling of ESACCI soil moisture in data limited region: A case study over India I. Dasari & V. Vema
- Towards a Global Water Use Scarcity Risk Assessment Framework: Integration of Remote Sensing and Geospatial Datasets Y. Wang et al.
- ESA CCI Soil Moisture GAPFILLED: an independent global gap-free satellite climate data record with uncertainty estimates W. Preimesberger et al.
- A high performance assimilation of surface soil moisture based on a hybrid framework of machine learning and physical hydrological model S. Zhu et al.
- Machine and deep learning in geological applications: a review of advances, challenges, and future research directions N. Hammouri et al.
- Evaluating satellite-based precipitation products for spatiotemporal drought analysis H. Khan et al.
- Graph-Transformer for Spatiotemporal Soil Moisture Forecasting Using Multimodal Remote Sensing Data M. Saki et al.
- AI in soil moisture remote sensing C. Montzka et al.
Saved (final revised paper)
Latest update: 14 May 2026
Short summary
Remote sensing has opened opportunities for mapping spatiotemporally continuous soil moisture, but it is hampered by data gaps. We propose a robust gap-filling approach to reconstruct daily satellite soil moisture. The merit of our approach is to integrate satellite observations, model-driven knowledge, and spatiotemporal machine learning. We also apply the developed approach to long-term datasets. Our study provides a potential avenue for hydrological applications.
Remote sensing has opened opportunities for mapping spatiotemporally continuous soil moisture,...
