A recently developed data assimilation technique offers the potential to greatly expand the geographic domain over which remotely sensed surface soil moisture retrievals can be evaluated by effectively substituting (relatively plentiful) rain-gauge observations for (less commonly available) ground-based soil moisture measurements. The technique is based on calculat-ing the Pearson correlation coefficient (R value) between rainfall errors and Kalman filter analysis increments realized during the assimilation of a remotely sensed soil moisture product into the antecedent precipitation index (API). Here, the existing R value approach is modified by reformulating it to run on an anomaly ba-sis where long-term seasonal trends are explicitly removed and by calculating API analysis increments using a Rauch–Tung–Striebel smoother instead of a Kalman filter. This reformulated approach is then applied to a number of Advanced Microwave Scanning Radiometer soil moisture products acquired within three heavily instrumented watershed sites in the southern U.S. R value -based evaluations of soil moisture products within these sites are verified based on comparisons with available ground-based soil mois-ture measurements. Results demonstrate that, without access to ground-based soil moisture measurements, the R value method-ology can accurately mimic anomaly correlation coefficients cal-culated between remotely sensed soil moisture products and soil moisture observations obtained from dense ground-based net-works. Sensitivity results also indicate that the predictive skill of the R value metric is enhanced by both proposed modifications to its methodology. Finally, R value calculations are expanded to a quasi-global (50 • S–50 • N) domain using rainfall measurements derived from the Tropical Rainfall Measurement Mission Precip-itation Analysis. Spatial patterns in calculated R value fields are compared to regions of strong land–atmosphere coupling and used to refine expectations concerning the global distribution of land areas in which remotely sensed surface soil moisture retrievals will contribute to atmospheric forecasting applications.