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Article

Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China

1
College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
2
State Key Laboratory of Cryosphere Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Academic Editors: Zheng Duan and Babak Mohammadi
Water 2021, 13(20), 2875; https://doi.org/10.3390/w13202875
Received: 6 September 2021 / Revised: 11 October 2021 / Accepted: 12 October 2021 / Published: 14 October 2021
Passive microwave surface soil moisture (SSM) products tend to have very low resolution, which massively limits their application and validation in regional or local-scale areas. Many climate and hydrological studies are urgently needed to evaluate the suitability of satellite SSM products, especially in alpine mountain areas where soil moisture plays a key role in terrestrial atmospheric exchanges. Aiming to overcome this limitation, a downscaling method based on random forest (RF) was proposed to disaggregate satellite SSM products. We compared the ability of the downscaled soil moisture active passive (SMAP) SSM and soil moisture and ocean salinity satellite (SMOS) SSM products to capture soil moisture information in upstream of the Heihe River Basin by using in situ measurements, the triple collocation (TC) method and temperature vegetation dryness index (TVDI). The results showed that the RF downscaling method has strong applicability in the study area, and the downscaled results of the two products after residual correction have more details, which can better represent the spatial distribution of soil moisture. The validation with the in situ SSM measurements indicates that the correlation between downscaled SMAP and in situ SSM is better than downscaled SMOS at both point and watershed scales in the Babaohe River Basin. From the TC method, the root mean square error (RMSE) of the CLDAS (CMA land data assimilation system), downscaled SMAP and downscaled SMOS were 0.0265, 0.0255 and 0.0317, respectively, indicating that the downscaled SMAP has smaller errors in the study area than others. However, the soil moisture distribution in the study area shown by the SMOS downscaled results is closer than the downscaled SMAP to the degree of drought reflected by TVDI. Overall, this study suggests that the proposed RF-based downscaling method can capture the variation of SSM well, and the downscaled SMAP products perform significantly better than the downscaled SMOS products after the accuracy verification and error analysis of the downscaled results, and it should be helpful to facilitate applications for satellite SSM products at small scales. View Full-Text
Keywords: downscaling; random forest; SMAP; SMOS; surface soil moisture; triple collocation; validation downscaling; random forest; SMAP; SMOS; surface soil moisture; triple collocation; validation
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MDPI and ACS Style

Wen, Y.; Zhao, J.; Zhu, G.; Xu, R.; Yang, J. Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China. Water 2021, 13, 2875. https://doi.org/10.3390/w13202875

AMA Style

Wen Y, Zhao J, Zhu G, Xu R, Yang J. Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China. Water. 2021; 13(20):2875. https://doi.org/10.3390/w13202875

Chicago/Turabian Style

Wen, Yuanyuan, Jun Zhao, Guofeng Zhu, Ri Xu, and Jianxia Yang. 2021. "Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China" Water 13, no. 20: 2875. https://doi.org/10.3390/w13202875

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