Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data
2.2.1. SPL2SMAP_S SM Products
2.2.2. Sentinel-2 Images
2.2.3. Vegetation Index
2.2.4. Topographic Data
2.2.5. In Situ Measurement Data
3. Methods
3.1. SM-RDNet
3.2. Downscaling Strategy
- Preparation of training dataset. The training dataset consists of a label dataset and a feature dataset. The label dataset is the residual value (∆SM) between the SM data with a higher spatial resolution and that with a lower spatial resolution. This process requires the SM product with a lower spatial resolution to be resized to have a higher spatial resolution to perform difference calculation. The feature dataset is obtained by resampling the auxiliary data to the same spatial resolution as the SM data, including Sentinel-2 reflectances, NDVI, elevation and slope. It should be noted that the high-spatial-resolution SM data in the first loop of downscaling is generated by conducting the nearest-neighbor interpolation on the original SM products. For the other loop of downscaling, the SM data we used is the SM predictions from the previous downscaling loops.
- Training of SM-RDNet model. The training dataset is input into the SM-RDNet to generate a prediction model. The prediction model is consecutively trained 200 times and each training evaluates the prediction model by the statistical evaluation indexes root mean square error (RMSE) and coefficient of determination (R2) based on the truth SM values. As the number of training increases, the predicted SM accuracy gradually improves and eventually stabilizes. Finally, the best prediction model is selected. In our experiments, each prediction model has an R2 greater than 0.85 and an RMSE less than 0.004 m3/m3 with respect to the truth SM value.
- Downscaling of SPL2SMAP_S SM products. The auxiliary data are resampled to the target spatial resolution, while the auxiliary data of the feature dataset with the higher spatial resolution are resized to the same dimension, and the two datasets are input into the trained SM-RDNet model to obtain ∆SM. The obtained ∆SM is added to the SM product with the high spatial resolution in the label dataset to obtain the estimated SM values.
- The expansion of spatial resolution in progressively downscaling. To reduce the information loss, the SM with a close resolution to the target is obtained through several downscaling processes. The expansion multiplier in each step is calculated as Equation (2).Rn_after = Rn_before/K,In this paper, the expansion multipliers are 3, 3, 3 and 5 in turn for downscaling the 3 km SPL2SMAP_S SM product into the 7.4 m product, which is close to the target spatial resolution (10 m), and are 3, 3 and 5 in turn for downscaling the 1 km SPL2SMAP_S SM products.
- Iterative downscaling process. Each downscaling process includes the training and prediction of the model. In the first training of the model, the higher resolution SM data in the ∆SM (label data) calculation process is generated by conducting the nearest-neighbor interpolation on the original SM products, while the lower resolution SM data is the original SM products. In the n-th training of the model, the higher resolution SM data in the ∆SM (label data) calculation process are the SM predicted results obtained from the (n − 1)-th downscaling, while the lower resolution SM data are the SM predicted results obtained from the (n − 2)-th downscaling. The auxiliary data are used as feature data after the same processing as in step 2. In the n-th model prediction process, the target resolution is calculated by step 4. The auxiliary data processed to the same resolution as the target will be used as input to the prediction model. The output ∆SM is added to the SM predicted from the (n − 1)-th downscaling to obtain the estimated SM values. In particular, the reason we chose iterative downscaling is that Xu et al. showed that the iterative downscaling method performs better than directly downscaling SM products to the target resolution [61].
- When the downscaling is finished, the downscaled SM further needs a resampling to fit the target spatial resolution.
3.3. Accuracy Validation
4. Results
4.1. Performance of Downscaled SM Derived from 1 Km SPL2SMAP_S SM Products
4.2. Performance of Downscaled SM Derived from 3 Km SPL2SMAP_S SM Products
4.3. Spatial Patterns of Downscaled SM in Shangwan Area
5. Discussion
5.1. Overview of the Downscaling Methodology
5.2. Role of Geoinformation in Improving the Accuracy of the Downscaled SM
5.3. The Effect of Vegetation Cover on the Downscaling of SM
5.4. Comparison of Downscaled SM Derived from 1-Km and 3-Km SPL2SMAP_S SM Products
5.5. Comparison of Downscaled SM based on SM-RDNet and RF Model
5.6. Analysis of Error Sources in Downscaling SM
6. Conclusions
- (1)
- The 3 km SPL2SMAP_S SM product is a more reliable data source for SM downscaling, since its spatial scale matches better with the original observations of satellites. The RSME of the downscaled SM is 0.0366 m3/m3.
- (2)
- The combination of NDVI, DEM, slope and reflectances as auxiliary data obtains the highest downscaling accuracy due to the adequate information they contain.
- (3)
- The SM-RDNet performs better than the RF model, especially for mining areas with complex human activities. In addition, the downscaled SMs are better under a single vegetation cover, especially grassland.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Band Name | Resolution | Description | Band Name | Resolution | Description |
---|---|---|---|---|---|
B2 | 10 m | Blue | B3 | 10 m | Green |
B4 | 10 m | Red | B5 | 20 m | Red Edge 1 |
B6 | 20 m | Red Edge 2 | B7 | 20 m | Red Edge 3 |
B8 | 10 m | NIR | B8A | 20 m | Red Edge 4 |
B11 | 20 m | SWIR 1 | B12 | 20 m | SWIR 2 |
Input Data Sets | Whole Area (n 1 = 54) | Sophora Davidii (n 1 = 9) | Siberian Almond Tree (n 1 = 14) | Sparse Grass (n 1 = 13) |
---|---|---|---|---|
reflectances | 0.1932 | 0.1911 | 0.1860 | 0.1841 |
reflectances + NDVI | 0.0944 | 0.0912 | 0.0867 | 0.0882 |
reflectances + DEM | 0.1121 | 0.1105 | 0.1043 | 0.1063 |
reflectances + slope | 0.1207 | 0.1156 | 0.1152 | 0.1139 |
reflectances + NDVI + DEM | 0.1236 | 0.1201 | 0.1179 | 0.1153 |
reflectances + NDVI + slope | 0.1341 | 0.1324 | 0.1271 | 0.1255 |
reflectances + NDVI + slope | 0.1281 | 0.1250 | 0.1229 | 0.1195 |
reflectances + NDVI + DEM + slope | 0.1065 | 0.1040 | 0.0995 | 0.0979 |
Input Data Sets | Whole Area (n 1 = 54) | Sophora Davidii (n 1 = 9) | Siberian Almond Tree (n 1 = 14) | Sparse Grass (n 1 = 13) |
---|---|---|---|---|
reflectances | 0.1510 | 0.1504 | 0.1426 | 0.1416 |
reflectances + NDVI | 0.0509 | 0.0483 | 0.0437 | 0.0396 |
reflectances + DEM | 0.0774 | 0.0761 | 0.0676 | 0.0675 |
reflectances + slope | 0.1046 | 0.1053 | 0.0943 | 0.0969 |
reflectances + NDVI + DEM | 0.2776 | 0.2772 | 0.2681 | 0.2696 |
reflectances + NDVI + slope | 0.2189 | 0.2158 | 0.2115 | 0.2101 |
reflectances + DEM + slope | 0.2331 | 0.2307 | 0.2263 | 0.2238 |
reflectances + NDVI + DEM + slope | 0.0366 | 0.0328 | 0.0305 | 0.0324 |
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Sang, X.; Li, J.; Zhang, C.; Xing, J.; Liu, X.; Wang, H.; Zhang, C. Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas. Water 2022, 14, 1792. https://doi.org/10.3390/w14111792
Sang X, Li J, Zhang C, Xing J, Liu X, Wang H, Zhang C. Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas. Water. 2022; 14(11):1792. https://doi.org/10.3390/w14111792
Chicago/Turabian StyleSang, Xiao, Jun Li, Chengye Zhang, Jianghe Xing, Xinhua Liu, Hongpeng Wang, and Caiyue Zhang. 2022. "Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas" Water 14, no. 11: 1792. https://doi.org/10.3390/w14111792
APA StyleSang, X., Li, J., Zhang, C., Xing, J., Liu, X., Wang, H., & Zhang, C. (2022). Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas. Water, 14(11), 1792. https://doi.org/10.3390/w14111792