Remotely sensed time series of landscape biophysical states and radar estimates of precipitation are assessed in a statistical streamflow estimation model in four regionally proximate south-central Texas watersheds. Sandies Creek watershed (1420 km2) served as calibration of a streamflow estimation model based on 8-day composited time series of land surface (radiant) temperature (LST) and a vegetation moisture stress index (MSI) from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite imagery products. These time series were hypothesized to serve as proxies for soil moisture condition antecedent to streamflow-generating precipitation events as estimated by NEXRAD (Next Generation Weather Radar) precipitation products. A linear multiple regression statistical model yielding estimation equation for observed flows at Sandies Creek was validated in 3 additional proximal watersheds of varying spatial dimension (860 km2 - 2940 km2), soils, land cover, and climatology. The validation yielded encouraging results as assessed with Pearson's r, Nash-Sutcliffe's E, and relative volume error or bias. Equivalent performance of the calibrated model was seen in the watershed mostly adjacent to Sandies Creek, the mostly similar one in terms of size, land cover, precipitation, and soils. Performance of the model diminished slightly in the more distal, and climatologically-environmentally dissimilar watersheds. The estimation model was re-calibrated for all three validation watersheds, with positive results. The contribution of LST uncertainty and NEXRAD precipitation bias to model discrepancies were evaluated along with discussion of known sources of model error.