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. The effective applications of land surface models (LSMs) and hydrologic modelspose a varied set of data input and processing needs, ranging from ensuringconsistency checks to more derived data processing and analytics. Thisarticle describes the development of the Land surface Data Toolkit (LDT),which is an integrated framework designed specifically for processing inputdata to execute LSMs and hydrological models. LDT not only serves as apreprocessor to the NASA Land Information System (LIS), which is anintegrated framework designed for multi-model LSM simulations and dataassimilation (DA) integrations, but also as a land-surface-based observationand DA input processor. It offers a variety of user options and inputs toprocessing datasets for use within LIS and stand-alone models. The LDT designfacilitates the use of common data formats and conventions. LDT is alsocapable of processing LSM initial conditions and meteorological boundaryconditions and ensuring data quality for inputs to LSMs and DA routines. Themachine learning layer in LDT facilitates the use of modern data sciencealgorithms for developing data-driven predictive models. Through the use ofan object-oriented framework design, LDT provides extensible features for thecontinued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and dataassimilation.

The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems