Abstract
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.
Highlights
Soil moisture is a key variable in hydrological, ecological and biogeochemical processes
Based on the random forest (RF) downscaling model constructed at coarse resolution, the soil moisture was estimated at high resolution using MODIS variables with a resolution of 1 km, and the coarse resolution residuals were resampled to the residuals at 1 km while bilinear interpolation was used
The results showed the ET has a greater impact on RF downscaling model and RF downscaling method is strongly applicable in the study area
Summary
Soil moisture is a key variable in hydrological, ecological and biogeochemical processes. Long-term observations of soil moisture over large areas are essential for numerous climate and hydrological studies [6,7,8,9] and accurate knowledge of the spatiotemporal behavior of soil moisture can greatly improve hydrological forecasting capability [10,11] It can be obtained from various methods: in situ measurement from ground meteorological stations [12,13], data assimilation products based on surface models [14], and real-time remote sensing monitoring [15,16]. Such low spatial resolution products cannot meet the application research of hydrological modeling, land surface process, and soil drought prediction in small and medium-scale areas [1], it is necessary to obtain higher spatial resolution and more accurate soil moisture data through downscaling to provide accurate soil moisture data while reducing the difficulty of ground verification
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