ABSTRACT
Earth observing satellites have revolutionized our understanding and prediction of the Earth system over the last 30 years, particularly in the meteorologic and oceanographic sciences. However, historically remote sensing data has not been widely used in hydrology. This can be attributed to (1) a lack of dedicated hydrologic remote sensing instruments, (2) inadequate retrieval algorithms for deriving global hydrologic information from remote sensing observations, (3) a lack of suitable distributed hydrologic models for digesting remote sensing information, and (4) an absence of techniques to objectively improve and constrain hydrologic model predictions using remote sensing data. Three ways that remote sensing observations have been used in distributed hydrologic models are (1) as parametric input data including soil and land cover properties, (2) as initial condition data, such as initial snow water storage, and (3) as timevarying hydrologic state data such as soil moisture content to constrain model predictions. This chapter focuses on the latter.
