Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts
Abstract
:1. Introduction
2. Materials and Methods
2.1. Soil Moisture Products
2.1.1. ESA CCI SM
2.1.2. ERA-Interim Reanalysis
2.1.3. In Situ Soil Moisture Measurements
2.2. Ancillary Data
2.3. Machine Learning
2.3.1. Random Forest
2.3.2. Artificial Neural Network
2.4. Reconstruction of Soil Moisture in the Temporal and Spatial Domains
3. Results
3.1. Performances Evaluation of the Machine Learning Approaches
3.2. Comparison of Spatial and Temporal Reconstructed Series
3.3. Performances for Tracing Typhoon Rainstorm and Drought Extreme Events
3.3.1. Performances for Tracing Typhoon Rainstorm Events
3.3.2. Performances for Tracing Drought Events
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Climate Zone | Statistics | In Situ | ERA | Original | ANNt | ANNs | RFt | RFs |
---|---|---|---|---|---|---|---|---|
(m3∙m−3) | ||||||||
Arid | Mean | 0.23 | 0.26 | 0.26 | 0.25 | 0.23 | 0.25 | 0.24 |
Min | 0.13 | 0.18 | 0.14 | 0.14 | 0.15 | 0.17 | 0.16 | |
Max | 0.37 | 0.38 | 0.41 | 0.4 | 0.44 | 0.35 | 0.4 | |
s.d. | 0.05 | 0.03 | 0.03 | 0.04 | 0.05 | 0.03 | 0.04 | |
Semi-arid | Mean | 0.25 | 0.26 | 0.25 | 0.25 | 0.24 | 0.25 | 0.24 |
Min | 0.08 | 0.17 | 0.12 | 0.13 | 0.02 | 0.16 | 0.11 | |
Max | 0.32 | 0.39 | 0.42 | 0.4 | 0.47 | 0.35 | 0.4 | |
s.d. | 0.03 | 0.04 | 0.03 | 0.04 | 0.05 | 0.03 | 0.04 | |
Semi-humid | Mean | 0.25 | 0.31 | 0.25 | 0.24 | 0.24 | 0.24 | 0.24 |
Min | 0.12 | 0.19 | 0.12 | 0.07 | 0.02 | 0.12 | 0.1 | |
Max | 0.39 | 0.38 | 0.42 | 0.38 | 0.47 | 0.35 | 0.38 | |
s.d. | 0.05 | 0.03 | 0.05 | 0.05 | 0.06 | 0.05 | 0.05 | |
Humid | Mean | 0.28 | 0.31 | 0.25 | 0.24 | 0.24 | 0.24 | 0.24 |
Min | 0.16 | 0.19 | 0.12 | 0.07 | 0.02 | 0.11 | 0.09 | |
Max | 0.39 | 0.38 | 0.42 | 0.38 | 0.47 | 0.35 | 0.38 | |
s.d. | 0.05 | 0.03 | 0.04 | 0.06 | 0.06 | 0.05 | 0.05 |
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Liu, Y.; Chen, R.; Yuan, S.; Ren, L.; Zhang, X.; Liu, C.; Ma, Q. Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts. Remote Sens. 2022, 14, 4841. https://doi.org/10.3390/rs14194841
Liu Y, Chen R, Yuan S, Ren L, Zhang X, Liu C, Ma Q. Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts. Remote Sensing. 2022; 14(19):4841. https://doi.org/10.3390/rs14194841
Chicago/Turabian StyleLiu, Yi, Ruiqi Chen, Shanshui Yuan, Liliang Ren, Xiaoxiang Zhang, Changjun Liu, and Qiang Ma. 2022. "Satellite Soil Moisture Data Reconstruction in the Temporal and Spatial Domains: Latent Error Assessments and Performances for Tracing Rainstorms and Droughts" Remote Sensing 14, no. 19: 4841. https://doi.org/10.3390/rs14194841