  We present planning challenges, methods and preliminary results for a new
model-based paradigm for earth observing systems in adaptive remote sensing.
Our heuristically guided constraint optimization planner produces coordinated
plans for multiple satellites, each with multiple instruments (payloads). The
satellites are agile, meaning they can quickly maneuver to change viewing
angles in response to rapidly changing phenomena. The planner operates in a
closed-loop context, updating the plan as it receives regular sensor data and
updated predictions. We describe the planner's search space and search
procedure, and present preliminary experiment results. Contributions include
initial identification of the planner's search space, constraints, heuristics,
and performance metrics applied to a soil moisture monitoring scenario using
spaceborne radars.
