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| Hauptseite > Publikationsdatenbank > Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses |
| Hauptseite > Publikationsdatenbank > Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses |
| Journal Article | FZJ-2025-02209 |
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2025
IEEE
New York, NY
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Please use a persistent id in citations: doi:10.1109/JSTARS.2025.3557956 doi:10.34734/FZJ-2025-02209
Abstract: Surface soil Moisture (SSM) is a crucial climate variable of the Earth system that regulates water and energy exchanges between the land and atmosphere, directly influencing hydrological, biogeochemical, and energy cycles. However, satellite-derived SSM, particularly from the Advanced Microwave Scanning Radiometer AMSR-E/2, is limited by radio frequency interference (RFI), vegetation effects, frozen ground, and significant spatial and temporal data gaps. By excluding data points affected by these problems, we are able to train an unaffected system and fill the gaps with high accuracy predictions. We developed a sophisticated deep learning ConvLSTM model, that combines Convolutional Long ShortTerm Memory (ConvLSTM2D) layers and Convolutional Neural Network (Conv2D) layers. The model initially enhances AMSR-2 SSM values across time and space using Advanced SCATterometer (ASCAT) SSM as input. The ConvLSTM model, trained to enhance AMSR-2 SSM, is then fine-tuned by using the transfer learning technique to enhance AMSR-E data. The enhanced AMSR-2 data is used as a target to guide the enhancement of AMSR-E. This approach ensures that gaps in AMSR-E data are filled, while aligning the characteristics with the more consistent AMSR-2 SSM, resulting in a seamless AMSR-E/2 dataset from 2003 to 2023. Unlike previous studies incorporating additional datasets like precipitation, temperature, and Digital Elevation Models, our approach avoids these to prevent redundancy and potential inaccuracies when generating land surface reanalyses based on data assimilation, since such data are already integrated into the land surface model. The ConvLSTM model achieved a lower RMSE of 0.07 for AMSR-2 prediction and 0.04 for AMSR-E via transfer learning demonstrating significant gap-filling accuracy. The enhanced SSM demonstrated a 26% improvement in the correlation with in-situ SSM measurements, while maintaining accuracy and consistency in spatial and temporal patterns.
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