Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. GPS Solutions
2.1.2. GRACE Solutions
2.1.3. Products of GLDAS and ERA 5
2.2. Methods
2.2.1. Random Forest
2.2.2. Inversion of TWSA Using Crustal Load–Deformation
2.2.3. Construction of the MLLIM
2.3. Evaluation Index
3. Results
3.1. Inversion of the TWSA Based on the MLLIM
3.2. Comparison with Traditional Method of GPS Inversion
3.3. Comparison with GRACE
3.4. Comparison with the GLDAS Solutions
4. Discussion
5. Conclusions
- (1)
- To increase the inverted accuracy for TWSA, the MLLIM is constructed using RF and the crustal load model. To simulate the crustal displacement in unobserved grids, the sequences of temperature and atmospheric pressure are used as the input data, and the GPS vertical sequence is used as the output data. Thus, the unobserved grids’ crustal deformation can be simulated based on the MLLIM. Furthermore, all the corrected crustal sequences are employed as the input data for the crustal load model; specifically, the corrected sequences include the GPS vertical sequences and the simulated sequences.
- (2)
- To verify the reliability of the MLLIM, the results of traditional GPS inverted method (without adding the simulated crustal deformation), GRACE, and GLDAS are used to compare with the TWSA based on the MLLIM. The compared results show that this inverted method can detect the raised region of annual amplitude in Yunnan, central Sichuan, and Guangxi provinces, and is consistent with the results of GRACE Mascon and GLDAS. However, the traditional GPS inverted method loses this characteristic signals due to the fitting of inversion (Figure 8d–f). From the comparison with GRACE, the values of PCC with GRACE and GRACE-FO equal 0.91 and 0.88, and the values of R2 equal 0.76 and 0.65, respectively. The MLLIM improves PCC and R2 by 7.98% and 9.30% than traditional method, respectively. From the comparison with GLDAS, the values of PCC and R2 equal 0.79 and 0.64, respectively, it improves PCC and R2 by 9.72% and 6.67% compared with the traditional method, respectively. On the whole, compared with the traditional GPS inverted method, the MLLIM effectively improves the accuracy of TWSA inversion.
- (3)
- The data of precipitation is combined to analyze the relationship between the TWSA and the rainfall. We apply the MLLIM to derive the TWSA in the five provinces, and the results indicate that the precipitation in Yunnan and Guangxi is significantly higher than other provinces. Meanwhile, the spatial distribution characteristics of TWSA is consistent with the precipitation in southwest China. From the comparison with GRACE and GLDAS solutions, the maximum PCC values equal 0.86 and 0.94, respectively. The above conclusions show that the TWSA can be inverted accurately based on the MLLIM in the region where the distribution of GPS stations is uneven.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Shen, Y.; Zheng, W.; Yin, W.; Xu, A.; Zhu, H.; Yang, S.; Su, K. Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China. Remote Sens. 2021, 13, 3358. https://doi.org/10.3390/rs13173358
Shen Y, Zheng W, Yin W, Xu A, Zhu H, Yang S, Su K. Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China. Remote Sensing. 2021; 13(17):3358. https://doi.org/10.3390/rs13173358
Chicago/Turabian StyleShen, Yifan, Wei Zheng, Wenjie Yin, Aigong Xu, Huizhong Zhu, Shuai Yang, and Kai Su. 2021. "Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China" Remote Sensing 13, no. 17: 3358. https://doi.org/10.3390/rs13173358