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United States Department of Agriculture

Agricultural Research Service

Title: Recent advances in land data assimilation for remote sensing observations)

item Crow, Wade

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 5/1/2010
Publication Date: 6/1/2010
Citation: Crow, W.T. 2010. Recent advances in land data assimilation for remote sensing observations [abstract]. Online Proceedings of the French-American Symposium on Developing Partnerships for Sustainable Water Management and Agriculture in the context of Climate and Global Change. 2010 CDROM.

Interpretive Summary:

Technical Abstract: For a number of 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 remote 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 such observations 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. 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 these 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 surface (0 to 5-cm) soil moisture observations 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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