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United States Department of Agriculture

Agricultural Research Service

Title: Assimilation of Satellite Remote Sensing Retrievals into Land Surface Models)

item Crow, Wade

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2010
Publication Date: 6/15/2010
Citation: Crow, W.T. 2010. Assimilation of satellite remote sensing retrievals into land surface models [abstract]. Proceedings of the 40th Biological Systems Simulation Conference. 2010 CDROM.

Interpretive Summary:

Technical Abstract: For at least two decades, remote sensing observations have been used to define static model parameters and/or forcing inputs for a range of land surface models. However, recent advances in remote sensing theory have also enabled the satellite-based retrieval of dynamic land model states (e.g. leaf area index for a crop growth model, stream temperature for a water quality model, or surface soil moisture for a hydrologic model). The integration of these observations into a dynamic model requires data assimilation techniques to optimally merge prior model predictions with uncertain remote sensing information in order to obtain the best possible dynamic state prediction. Ideally, such merging is based on an accurate statistical understanding of error sources in both the remote sensing observations and the land surface model. In addition, since remote sensing observations do not typically observe all model states (due to e.g. temporally sampling limitations and/or the inability of sensors to penetrate beyond the near-surface), a critical aspect of land data assimilation techniques is the extrapolation of information from time/space locations with observations to those without. This talk will describe recent advances in the application of data assimilation systems to land surface modeling. Particularly attention will be paid to the problem of constraining soil moisture profile predictions within a crop root-zone using only surface (0 to 5-cm) soil moisture observations, difficulties in obtaining the statistical error information required as input by land data assimilations systems, and opportunities afforded by current and upcoming satellite missions designed to measure surface soil moisture from space.

Last Modified: 05/28/2017
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