Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method
Abstract
:1. Introduction
2. Methods
2.1. Gap-Filling Method Based on Random Forest Algorithm
2.1.1. Principle of Random Forest
2.1.2. Selection of Feature Variables
2.1.3. RF Model Establishment for SM Gap-Filling
2.2. Evalution Metrics
3. Data
3.1. Surface Soil Moisture Product
3.2. Feature Variables Data
- (a)
- Normalized Difference Vegetation Index
- (b) Land surface temperature
- (c) ERA 5 Precipitation
- (d) Land Cover Type
3.3. In Situ Observations Data
4. Results
4.1. RF Training Results
4.1.1. Importance of Feature Variables in RF Model Construction
4.1.2. Evaluation of Model Performance
4.2. Reconstruction Results and Cross-Comparison
4.3. Validation Using the In Situ Observations
5. Discussion
5.1. The Proposed Pixel-Wise RF Method and the Gap-Filling Results
5.2. Uncertainty Analysis of the Gap-Free Results
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Variable Name | Data Name | Temporal Resolution | Spatial Resolution | Reference |
---|---|---|---|---|
Surface soil moisture | NN-SM | Daily | 36 km | [28] |
NDVI | MOD13C1 | 16 days | 0.05° | [43] |
LST | Global daily 0.05° spatiotemporal continuous land surface temperature dataset | Daily | 0.05° | [44] |
Precipitation | ERA5 | Hourly | 0.25° | [45] |
Land Cover Type | MCD12C1 | Yearly | 0.05° | [46] |
Networks | Sites Names | Stations | Climate Regime | IGBP Land Cover | Measured Depth | Reference |
---|---|---|---|---|---|---|
Tibetan Plateau (Asia) | Pali | 24 | Arid | Barren/sparse | 5 cm | [55] |
Naqu | 57 | Polar | Grasslands | |||
OZNET (Australia) | Yanco | 12 | Semi-arid | Croplands/Grasslands | 5–8 cm | [56] |
Kyeamba | 8 | Temperate | Croplands | |||
REMEDHUS (Europe) | REMEDHUS | 23 | Temperate | Croplands | 5 cm | [57] |
AMMA (Africa) | Benin | 4 | Arid | Savannas | 5 cm | [58,59,60,61] |
Niger | 3 | Arid | Grasslands | |||
USDA (North America) | Little River | 33 | Temperate | Croplands | 5 cm | [62,63,64,65,66] |
Little Washita | 20 | Temperate | Grasslands | |||
Walnut Gulch | 19 | Arid | Shrub open rangeland | |||
Fort Cobb | 15 | Temperate | Croplands | |||
Reynolds Creek | 20 | Arid | Grasslands |
R | RMSE (m3/m3) | ubRMSE (m3/m3) | Bias (m3/m3) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sites | SM -Ori | SM -Recon | SM -Gapfree | SM -Ori | SM -Recon | SM -Gapfree | SM -Ori | SM -Recon | SM -Gapfree | SM -Ori | SM -Recon | SM -Gapfree |
Benin | 0.800 | 0.853 | 0.818 | 0.120 | 0.112 | 0.117 | 0.058 | 0.042 | 0.052 | 0.105 | 0.104 | 0.104 |
Fort Cobb | 0.495 | 0.447 | 0.453 | 0.061 | 0.049 | 0.061 | 0.061 | 0.048 | 0.061 | −0.004 | −0.006 | −0.002 |
Kyemba | 0.740 | 0.613 | 0.708 | 0.105 | 0.108 | 0.105 | 0.079 | 0.074 | 0.076 | 0.070 | 0.079 | 0.073 |
Little River | 0.539 | 0.552 | 0.505 | 0.138 | 0.126 | 0.137 | 0.054 | 0.037 | 0.051 | 0.127 | 0.121 | 0.127 |
Little Washita | 0.498 | 0.391 | 0.452 | 0.066 | 0.052 | 0.065 | 0.062 | 0.051 | 0.061 | 0.024 | 0.011 | 0.023 |
Naqu | 0.810 | 0.751 | 0.809 | 0.075 | 0.072 | 0.074 | 0.071 | 0.054 | 0.053 | −0.025 | −0.049 | −0.052 |
Niger | 0.786 | 0.669 | 0.708 | 0.033 | 0.044 | 0.038 | 0.025 | 0.023 | 0.026 | 0.021 | 0.037 | 0.029 |
Pali | 0.598 | 0.811 | 0.811 | 0.051 | 0.03 | 0.038 | 0.028 | 0.029 | 0.032 | −0.043 | −0.009 | −0.02 |
Remedhus | 0.835 | 0.76 | 0.826 | 0.036 | 0.034 | 0.034 | 0.036 | 0.034 | 0.034 | −0.001 | 0.002 | −0.001 |
Reynolds Creek | 0.497 | 0.423 | 0.468 | 0.069 | 0.057 | 0.07 | 0.067 | 0.057 | 0.067 | 0.016 | 0.003 | 0.018 |
Walnut Gulch | 0.574 | 0.553 | 0.572 | 0.065 | 0.051 | 0.058 | 0.044 | 0.028 | 0.036 | 0.047 | 0.043 | 0.046 |
Yanco | 0.770 | 0.628 | 0.737 | 0.080 | 0.072 | 0.076 | 0.062 | 0.062 | 0.061 | 0.050 | 0.036 | 0.045 |
all | 0.662 | 0.621 | 0.656 | 0.075 | 0.067 | 0.073 | 0.054 | 0.045 | 0.051 | 0.032 | 0.031 | 0.033 |
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Mi, P.; Zheng, C.; Jia, L.; Bai, Y. Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method. Remote Sens. 2023, 15, 2116. https://doi.org/10.3390/rs15082116
Mi P, Zheng C, Jia L, Bai Y. Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method. Remote Sensing. 2023; 15(8):2116. https://doi.org/10.3390/rs15082116
Chicago/Turabian StyleMi, Pei, Chaolei Zheng, Li Jia, and Yu Bai. 2023. "Reconstruction of Global Long-Term Gap-Free Daily Surface Soil Moisture from 2002 to 2020 Based on a Pixel-Wise Machine Learning Method" Remote Sensing 15, no. 8: 2116. https://doi.org/10.3390/rs15082116