A global analysis of soil moisture derived from satellite observations and a land surface model

摘要

Soil moisture availability is important in regulating photosynthesis andcontrolling land surface-climate feedbacks at both the local and globalscale. Recently, global remote-sensing datasets for soil moisture havebecome available. In this paper we assess the possibility of using remotelysensed soil moisture – AMSR-E (LPRM) – to similate soil moisture dynamics of theprocess-based vegetation model ORCHIDEE by evaluating the correspondencebetween these two products using both correlation and autocorrelationanalyses. We find that the soil moisture product of AMSR-E (LPRM) and thesimulated soil moisture in ORCHIDEE correlate well in space and time, inparticular when considering the root zone soil moisture of ORCHIDEE.However, the root zone soil moisture in ORCHIDEE has on average a highertemporal autocorrelation relative to AMSR-E (LPRM) and in situ measurements.This may be due to the different vertical depth of the two products – AMSR-E(LPRM) at the 2–5 cm surface depth and ORCHIDEE at the root zone (max. 2 m)depth – to uncertainty in precipitation forcing in ORCHIDEE, and to the factthat the structure of ORCHIDEE consists of a single-layer deep soil, whichdoes not allow simulation of the proper cascade of time scales thatcharacterize soil drying after each rain event. We conclude that assimilatingsoil moisture, using AMSR-E (LPRM) in a land surface model like ORCHIDEE withan improved hydrological model of more than one soil layer, may significantlyimprove the soil moisture dynamics, which could lead to improved CO₂and energy flux predictions.