NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled ProcessesEarth'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.
Document ID
20160009145
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Kumar, S. V.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Peters-Lidard, C. D.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Santanello, J. A.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Reichle, R. H.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Draper, C. S.
(Universities Space Research Association Greenbelt, MD, United States)
Koster, R. D.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Nearing, G.
(Science Applications International Corp. Beltsville, MD, United States)
Jasinski, M. F.
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Date Acquired
July 19, 2016
Publication Date
November 6, 2015
Publication Information
Publication: Hydrology and Earth System Sciences
Publisher: EGU
Volume: 19
Issue: 11
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN33892
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
CONTRACT_GRANT: NNG15HQ01C
Distribution Limits
Public
Copyright
Other
Keywords
land data
irrigated
Moisture

Available Downloads

There are no available downloads for this record.
No Preview Available