DARLA was a collaborative project that coupled state-of-the-art remote sensing and in situ measurements with advanced data assimilation (DA) modeling to (a) evaluate and improve remote sensing retrieval algorithms for environmental parameters based on signature physics and sensor fusion, (b) determine the extent to which remote sensing data can be used in place of in situ data in models, and (c) infer bathymetry for a range of littoral environments by combining remotely-sensed parameters and data assimilation models.

The project used mature microwave, electro-optical, and infrared techniques to characterize the littoral ocean with a focus on wave and current parameters required for data assimilation modeling. Extensive in situ measurements provided ground truth for both the remote sensing retrieval algorithms and the data assimilation modeling. Our results demonstrate how prediction schemes and remote sensing observing systems can be combined for maximal operational impact.

Our overall goal is to use remote sensing observations to constrain a data assimilation model of wave and circulation dynamics in an area characterized by a river mouth or tidal inlet and surrounding beaches. As a result of this activity, we will improve environmental parameter estimation via remote sensing fusion, determine the success of using remote sensing data to drive DA models, and produce a dynamically consistent representation of the wave, circulation, and bathymetry fields in complex environments. The objectives are to test three hypotheses.

• Environmental parameter estimation using remote sensing techniques can be significantly improved by fusion of multiple sensor products.
• Data assimilation models can be adequately constrained (i.e., forced or guided) with environmental parameters derived from remote sensing measurements.
• Bathymetry on open beaches, river mouths, and at tidal inlets can be inferred from a combination of remotely-sensed parameters and data assimilation models.

Our overall approach will be to conduct a series of field experiments combining remote sensing and in situ measurements to investigate signature physics and to gather data for developing and testing DA models. We will collaborate closely with the field activities anticipated under the ONR-sponsored Inlets and Rivers Mouth Dynamics Departmental Research Initiative (IRMD-DRI), which will have its first experiment at the New River Inlet. The combined capabilities provide an innovative solution that couples spatially dense sampling with data assimilation methods to study the complicated dynamics of interacting wave, bathymetry, and current fields.

The research has a high potential for significant and immediate impact on DoD capabilities. Upon successfully demonstrating the extent to which remote sensing and modeling can be coupled, transition to operational use will depend primarily on the availability of UAV-based sensors comparable to the ones we will employ. The miniature Synthetic Aperture Radar, Electo-Optical, and Infrared sensors we will use are suitable for UAV deployment. By determining the feasibility of the remotely-sensed geophysical variables to drive predictive DA numerical models, a rapid and straightforward diagnostic tool for determining nearshore, coastal, and riverine bathymetry can be developed.