Performance comparison of a point-scale LSP model and the NOAH distributed SVAT model for soil moisture estimation using microwave remote sensing

Abstract

Soil moisture is a critical state variable in land surface hydrology, and has a significant influence on interactions between the land surface and the atmosphere through the exchange of water vapor and sensible heat. Assessment of the ability of passive microwave remote sensing to measure accurately the spatial and temporal variations of soil moisture is an essential first step for the successful incorporation of soil moisture data in many important applications such as mesoscale weather forecasting, climate modeling, agricultural productivity, watershed hydrology, ecological processes, and natural hazards research. An important part of this assessment is to understand the. relationship between soil moisture derived from microwave remote sensing and soil moisture as used and predicted by various models of the land surface water and energy balance, including soil-vegetation atmosphere transfer schemes (SVATS). Characterization of the relationship between microwave-derived and SVAT-modeled soil moisture is important at both point and distributed spatial scales, and this issue forms the basis of our current research. Two models used in this study include (1) the new modular vectorized version of the University of Michigan Land Surface Process/Radiobrightness (LSP/R) model. and (2) the NOAH Land Surface Model by NOAA NCEP, Oregon State University, the Army, and the Office of Hydrology. Both models are initialized with appropriate soil and vegetation parameters and are driven by local meteorological data.