Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes
Budget control
; Irrigation
; Meteorological instruments
; Moisture
; Radiometers
; Remote sensing
; Satellites
; Soils
; Surface measurement
; Advanced microwave scanning radiometer
; Advanced Microwave Scanning Radiometer for the Earth Observing System
; Agricultural irrigation
; Land data assimilation
; Land data assimilation systems
; Satellite remote sensing
; Satellite soil moisture
; Soil moisture ocean salinities
; Soil moisture
; AMSR-E
; data assimilation
; EOS
; geographical region
; irrigation system
; land surface
; remote sensing
; satellite data
; soil moisture
; water budget
; United States
英文摘要:
Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation. � Author(s) 2015.
Science Applications International Corporation, Beltsville, MD, United States; Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Universities Space Research Association, NASA Goddard Space Flight Center, Greenbelt, MD, United States
Recommended Citation:
Kumar S,V,, Peters-Lidard C,et al. Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes[J]. Hydrology and Earth System Sciences,2015-01-01,19(11)