Abstract
This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.
Highlights
Soil moisture (SM) is widely recognized as a vital land surface variable that associates with land–atmosphere interaction [1,2], rainfall–runoff processes [3], water–energy balance [4], and climate change [5]
The overall 8-day relative soil moisture (RSM) was combined at a 500 m resolution by corresponding subregional RSM, which was produced with three groups of selected optimal normalized difference vegetation index (NDVI) thresholds using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI), and the average of ATI and TVDI against 20 cm depth in situ RSM observations [32]
R2 of 0.912 (i.e., 91.2% of the variation of the dependent variable RSM can be explained by the change in the independent variables) and the lowest root mean square error (RMSE) of 0.798% in winter among all four seasons in 2017
Summary
Soil moisture (SM) is widely recognized as a vital land surface variable that associates with land–atmosphere interaction [1,2], rainfall–runoff processes [3], water–energy balance [4], and climate change [5]. SM retrievals from SAR and microwave sensors are greatly affected by soil surface roughness, vegetation cover, and other relevant factors [17]. Numerical simulations, involving the use of land surface features retrieved by visible/nearinfrared/thermal-infrared bands (e.g., vegetation, land surface temperature (LST), and surface albedo), have long been the primary methods for obtaining large-scale SM. These methods are based on Moderate Resolution Imaging Spectroradiometer (MODIS) data and land surface models [2,18,19]. The main problem associated with this method is that optical sensors cannot penetrate clouds and vegetation, which highly influences the quality of the SM estimation results [18,20]
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