###### Abstract  
This paper compared the predictive performance of different regression models for trend component estimation in the spatial downscaling of coarse resolution satellite data using area-to-point regression kriging in the context of the sensitivity to input data errors. Three regression models, linear regression, random forest, and support vector regression, were applied to trend component estimation. An experiment on downscaling synthetic Landsat data with different noise levels demonstrated that a regression model with higher explanatory power and residual correction led to the highest predictive performance only when the input coarse resolution data were assumed to be error-free. Through an experiment on spatial downscaling of coarse resolution monthly Advanced Microwave Scanning Radiometer-2 soil moisture products with significant errors, we found that the higher explanatory power of regression models did not always lead to better predictive performance. The residual correction and normalization of trend components also degraded the predictive performance. Using trend components as a final downscaling result showed the best performance in both experiments as the input errors increased. As the predictive performance of spatial downscaling results is susceptible to input errors, the findings of this study should be considered to evaluate downscaling results and develop advanced spatial downscaling methods. spatial downscaling; trend component; residual; spatial scale +
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Since the late 2000s, spatial downscaling has been widely applied to enhance the spatial resolution of various remote sensing data, such as precipitation [5; 6; 7; 8; 9; 10], soil moisture (SM) [11; 12; 13; 14; 15], and land surface temperature (LST) [16; 17; 18; 19; 20]. Most studies have tried to downscale the coarse spatial resolution remote sensing data using fine spatial resolution auxiliary variables associated with the target attribute. From a methodological perspective, there are two approaches to spatial downscaling. The first approach is regression modeling, where quantitative relationships between a target variable and auxiliary variables are first quantified, and the relationships are then used to predict the target variable at a fine scale [6; 8; 13; 16; 17; 18; 19]. Various regression models, ranging from linear regression (LR) to machine learning, have been applied to spatial downscaling, as summarized in [PERSON] et al. [2]. The performance of this regression-modeling approach depends heavily on the explanatory power of the applied regression model. When the regression model fails to explain the variability of the target variable, the downscaling results cannot preserve the spatial pattern of the input coarse resolution data [2]. The second approach, complementing the limitations of the regression-based approach, is a component-decomposition-based one. It decomposes a target attribute into a deterministic trend component (TC) and a stochastic residual component (RC). The TC at fine resolution is estimated using regression modeling similar to the abovementioned approach. The RC that cannot be explained from regression modeling is predicted at fine resolution using spatial interpolation. The final downscaling result is generated by summing the above two components. The advantage of this hybrid approach over the regression-based approach is that the spatial pattern of the coarse resolution data can be reproduced by considering the residual correction, even when the regression model does not have high explanatory power [2]. Area-to-point regression kriging (ATPRK) [21] is a representative model for this approach and has been widely applied to the spatial downscaling of remote sensing images and products [22; 23; 24; 25]. Despite the great potential of ATPRK for spatial downscaling, there is a practical issue to be resolved. An important issue, which is the main focus of this study, is an error propagation problem.