In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model
Abstract
:1. Introduction
2. Materials and Methodology
2.1. Materials
2.1.1. In Situ SSM
2.1.2. Land Surface Features and Precipitation
- Land Surface Temperature
- Vegetation Index
- Soil Texture
- Precipitation
2.1.3. ESA-CCI SSM
2.2. Methodology
- Step 1 Data Pre-processing and Harmonization
- Step 2 Training and Validation of the Prediction Model
- Step 3 Gridded SSM prediction and evaluation.
2.2.1. Data Pre-Processing and Harmonization
- Daily LST and Daily LST Difference
- Vegetation Index Reconstruction
- Antecedent Precipitation Index
- Spatial Resampling
- Data splitting
2.2.2. Training and Validation of RF Model
2.2.3. Gridded SSM Prediction and Evaluation
3. Results and Discussion
3.1. Training and Testing of the Prediction Model
3.2. Predicted SSM Time-Series of Validation and Evaluation Set
3.3. Global Scale Comparison
3.4. Regional Scale Comparison
3.4.1. Spatial Patterns
3.4.2. Spatio-Temporal Patterns
3.5. Influence of Predictor Variables
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Name of Predictors * | Description | Source | Original Spatial Resolution | Original Temporal Resolution |
---|---|---|---|---|
Daily LST | The arithmetic average of LST of daytime and night-time | MOD11A1 daily LST product https://doi.org/10.5067/MODIS/MOD11A1.006 (accessed on 13 September 2021) | 1 km | Daily |
Daily LST Difference | The difference between the LST at daytime and night-time | MOD11A1 daily LST product https://doi.org/10.5067/MODIS/MOD11A1.006 (accessed on 13 September 2021) | 1 km | Daily |
NDVI | Interpolated daily NDVI | MOD13A1 https://doi.org/10.5067/MODIS/MOD13A1.006 (accessed on 13 September 2021) | 500 m | 16 d |
EVI | Interpolated daily EVI | MOD13A1 https://doi.org/10.5067/MODIS/MOD13A1.006 (accessed on 13 September 2021) | 500 m | 16 d |
API | Calculated Antecedent Precipitation Index | ERA-5 daily precipitation https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview (accessed on 13 September 2021) | 0.25° | Daily |
Soil texture (clay, silt and sand) | ML-based global soil texture estimation | SoilGrids https://soilgrids.org/ (accessed on 13 September 2021) | 250 m | Static |
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Zhang, L.; Zeng, Y.; Zhuang, R.; Szabó, B.; Manfreda, S.; Han, Q.; Su, Z. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sens. 2021, 13, 4893. https://doi.org/10.3390/rs13234893
Zhang L, Zeng Y, Zhuang R, Szabó B, Manfreda S, Han Q, Su Z. In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model. Remote Sensing. 2021; 13(23):4893. https://doi.org/10.3390/rs13234893
Chicago/Turabian StyleZhang, Lijie, Yijian Zeng, Ruodan Zhuang, Brigitta Szabó, Salvatore Manfreda, Qianqian Han, and Zhongbo Su. 2021. "In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model" Remote Sensing 13, no. 23: 4893. https://doi.org/10.3390/rs13234893