Numerous government surveys of natural resources use Post-Stratifi cation to improve statistical effi ciency, where strata are defi ned by full-coverage, remotely sensed data and geopolitical boundaries. Recursive Restriction Estimation, which may be considered a special case of the static Kalman filter, is an attractive alternative. It decomposes a complex estimation problem into simple components that are sequentially processed. Compared to Post-Stratification, it more efficiently uses remotely sensed data, both continuous and categorical. It is less constrained by sample size, which is especially important with panel surveys. It produces a conditionally unbiased covariance matrix for the vector estimate of population totals without approximations or ad hoc assumptions. This facilitates variance estimates for non-linear pseudo-estimators. A robust sequential algorithm controls numerical errors inherent with Recursive Restriction Estimator, which can otherwise cause unreliable results. Analysis of residuals can detect other anomalies.