High Spatio-Temporal Resolution CYGNSS Reflectivity Reconstruction via TCN for Enhanced Freeze/Thaw Retrieval.
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| Title: | High Spatio-Temporal Resolution CYGNSS Reflectivity Reconstruction via TCN for Enhanced Freeze/Thaw Retrieval. |
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| Authors: | Li, Xiangle1,2,3 (AUTHOR), Yang, Wentao2,4 (AUTHOR), Wang, Dong1,2,3 (AUTHOR), Li, Weixin1,2,3,4 (AUTHOR), Wang, Dandan1,2,3 (AUTHOR), Yang, Lei1,2,3 (AUTHOR) ise_yangl@ujn.edu.cn |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 7, p1056. 24p. |
| Subjects: | Satellite-based remote sensing, Spatial resolution, Soil moisture, Remote sensing, Interpolation algorithms, Artificial neural networks, United States. National Aeronautics & Space Administration |
| Abstract: | Highlights: What are the main findings? A model named Partial Convolution–Time Convolutional Network (PTCN) is proposed to reconstruct 3 km resolution CYGNSS data without relying on auxiliary data, which increases the coverage to six times that of the original. The reconstructed data is used for freeze/thaw (F/T) retrieval at different spatial resolutions. The temporal resolution of the reconstructed F/T retrieval is increased by 256%, filling 92% of the gaps in SMAP data, while maintaining accuracy comparable to ground-based observations. What are the implications of the main findings? The model effectively fills the void in high spatio-temporal benchmark data for F/T retrieval, establishing a reliable observational foundation for high-altitude regions. The reconstructed high spatio-temporal resolution data contributes to a more accurate understanding of surface-atmosphere interactions, offering significant reference value for remote sensing applications such as disaster forecasting, hydrological processes, and climate change. In recent years, the Cyclone Global Navigation Satellite System (CYGNSS) of NASA has attracted widespread attention for the retrieval of freeze/thaw (F/T) states through the analysis of reflected signals. F/T variations in high-altitude regions have long been a focal point in this field. However, these areas lack benchmark observational data with high temporal and spatial resolution. A model named Partial Convolution–Time Convolutional Network (PTCN) is proposed in this paper to reconstruct CYGNSS data at a 3 km resolution. This model integrates partial convolution with a time convolutional network (TCN) and does not rely on any auxiliary data. Partial convolution is employed to distinguish valid pixels, with the interference of missing values being removed. TCN is employed to capture temporal features, which results in the reconstruction of observational data. Compared with the original observational data (at a 3 km resolution), the coverage of the reconstructed data is six times that of the original. A simulation of missing data is applied for the first time in the quantitative evaluation of observational data reconstruction. The results show that the value of R for the reconstructed data reaches 0.92, and the value of the root mean square error (RMSE) reaches 2.7. The reconstructed data is used for daily F/T retrieval. At both 36 km and 9 km resolutions, the F/T retrieval accuracy after reconstruction is comparable to that before reconstruction. The temporal resolution is improved by 256%, which successfully fills 92% of the observational gaps in soil moisture passive–active (SMAP) data. Compared with ground-based F/T retrievals, the reconstructed F/T accuracies are 87.71% at 36 km and 82.3% at 9 km.The model successfully reconstructs high-temporal and spatial resolution CYGNSS data while maintaining accuracy. In the future, this method holds significant potential for the application of global GNSS-R high-temporal and spatial resolution remote sensing observations. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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