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Article

Downscaling Microwave Soil Moisture Products with SM-RDNet for Semiarid Mining Areas

1
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology-Beijing, Beijing 100083, China
3
State Key Laboratory of Water Resource Protection and Utilization in Coal Mining, Beijing 102209, China
*
Author to whom correspondence should be addressed.
Academic Editor: Pankaj Kumar
Water 2022, 14(11), 1792; https://doi.org/10.3390/w14111792
Received: 31 March 2022 / Revised: 23 May 2022 / Accepted: 31 May 2022 / Published: 2 June 2022
(This article belongs to the Section Soil and Water)
Surface soil moisture (SM), as a crucial ecological element, is significant to monitor in semiarid mining areas characterized by aridity and little rainfall. The passive microwave remote sensing, which is not affected by weather, provides more accurate SM information, but the resolution is too coarse for mining areas. The existing downscaling method is usually pointed to natural scenarios like agricultural fields rather than mining areas with high-intensity mining. In this paper, combined with geoinformation related to SM, we designed a convolutional neural network (SM-Residual Dense Net, SM-RDNet) to downscale SMAP/Sentinel-1 Level-2 radiometer/radar soil moisture data (SPL2SMAP_S SM) into 10 m spatial resolution. Based on the in-site measured data, the root mean square error (RMSE) was utilized to verify the downscaling accuracy of SM-RDNet. In addition, we analyzed its performance for different data combinations, vegetation cover types and the advantages compared with random forest (RF). Experimental results show that: (1) The downscaling from the 3 km product with the combination of auxiliary data NDVI + DEM + slope performs best (RMSE 0.0366 m3/m3); (2) Effective data combinations can improve the downscaling accuracy at the range of 0.0477–0.1176 m3/m3 (RMSE); (3) The SM-RDNet shows better spatial completeness, details and accuracy than RF (RMSE improved by 0.0905 m3/m3). The proposed SM-RDNet can effectively obtain the fine-grained SM in semiarid mining areas. Our method bridges the gap between coarse-resolution microwave SM products and ecological applications of small-scale mining areas, and provides data and technical support for future research to explore how the mining effect SM in semiarid mining areas. View Full-Text
Keywords: surface soil moisture; convolutional neural network; SPL2SMAP_S; fine-grained surface soil moisture; convolutional neural network; SPL2SMAP_S; fine-grained
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MDPI and ACS Style

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

AMA Style

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 Style

Sang, 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

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