![]() Land Parameter Retrieval Model (LPRM) and NOAH soil moisture datasets are rescaled into the space of in-situ measurements obtained over four United States Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds and later merged using a simple linear weighting method. Nonlinear rescaling methods implemented in this study include: multivariate adaptive regression splines (MARS), Support vector machines (SVM), and artificial neural network (ANN), while the linear methods include linear regression, variance-matching, and triple collocation. ![]() In this study, the added utility of nonlinear rescaling methods relative to linear methods in the framework of data fusion has been explored. ![]() However, the time series of these retrievals often contain systematic differences, which need to be removed via different rescaling approaches before these data sets could be used in data fusion type studies. There are different ways available for the retrieval of this essential variable (e.g., remote sensing, hydrological models, insitu measurements, and etc.). Soil moisture is one of the terrestrial essential climate variables that has critical role in the water, energy, and carbon cycles.
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