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Description: Rice feeds more humans than any other crop on Earth. Accurate prediction of the timing and volume of rice harvests therefore has considerable global importance for food security and economic stability, especially in the developing world. Optical and thermal satellite imagery can provide critical constraints on the spatial extent of rice planting and the timing of rice phenology. We present a novel approach to the mapping & monitoring of rice agriculture using Temporal Mixture Models (TMMs) derived from parallel spatiotemporal analyses of coincident optical and thermal Landsat image time series. Using the Sacramento Valley of California as a test area, we characterize regional rice phenology in terms of both fractional vegetation abundance (Fv) and brightness temperature (Tb). We compare satellite Tb retrievals to station data and find uncorrected Tb to compare with the upper bound of the envelope of air temperature observations to within 3°C on average. Results from parallel spatiotemporal analyses of coincident Fv and Tb image time series over the 2016 & 2017 growing seasons suggest that TMMs based on single year image time series can provide simple and accurate maps of crop timing, while TMMs based on dual year image time series can provide comparable maps of year-to-year crop conversion. Fv time series data show particular promise for estimating crop timing, while Tb appears particularly well suited for discriminating between rice and other crops. We also build a sample model midway through the 2018 growing season to illustrate a potential near-realtime monitoring application. Field validation confirms that the mid-2018 monitoring model provides an accurate upper bound estimate of the spatial extent and relative timing of the rice crop, even under conditions of relative data scarcity. The implications of these results could have potential utility for further analyses of precision agriculture, pest management, evapotranspiration (ET) and cropping practice verification.