1. Kalman Filter: optimal estimates of signal and uncertainty given noisy measurements, process model, and error structure

2. Kalman Filter/Smooth Example: vicinity of Columbus, Nebraska

4. Croplands Data Layer: mostly corn, soy, and grass/hay

5. Fit cover-group-specific dynamic linear models for weekly EVI state estimation corn soybean hay water dev/open dev/low decid forest grass/herb woody wetlands Mean daily signal for all crop/land-cover types > 1% area

6. Corn Other/hay Grasslands Example Kalman Smoothing for various cover types

8. KF Advantages: – Smoothing, but with a shape prior – Estimate uncertainty – Flexible sensor fusion What’s next: – How to best fit linear models? – How to fit initial conditions/parameters? In particular: process/measurement error – Extended KF? – Verify implementation & diagnose problems (smoothing blowing up)