roughly half of the variance in full plot harvests when restricting the analysis to where both ground and satellite approaches are measuring the same output (pure stand plots) and where the satellite pixels corresponding to the plot are less likely to be contaminated by neighboring plots (plots > 0.10 hectare). The uncalibrated approach exhibits, however, a strong tendency to overestimate yields, but adequately captures spatial variation in yield. In fact, the satellite‐based estimates explained roughly the same amount of variance in full plot harvests as sub‐plot crop cuts performed within the plots.

Perhaps more convincingly, satellite‐based estimates are able to faithfully reproduce the effects of key production factors such as soil quality and fertilizer use, even when including plots of all sizes and those that are intercropped. The significance levels of the coefficients informed by the satellite‐based measures are often even higher than those underlined by the full plot harvests. This finding again emphasizes two important points. First is that any measure of yield is prone to errors, and thus an imperfect correlation with full plot harvests reflects errors in ground‐based estimates as well as those in satellite‐based estimates. Second, even if satellite‐based measures are less accurate than full plot harvests, the greater sample size can compensate for any loss in accuracy.

Also noteworthy is the fact that satellite‐based models calibrated to CC yields perform similarly to those calibrated to FP yields, in terms of both agreement with FP yields and estimation of yield response to soil quality and fertilizer. These results indicate that although CC yields are imperfect approximations of actual yields, the errors do not substantially bias remote sensing calibrations. Thus, sub‐plot crop cutting appears to be a suitable replacement for full‐plot harvests when the latter are not possible. Of course, crop model simulations can also be used as a replacement for any ground‐based measures, if the potential bias in estimated yields is recognized and acceptable. The possibility of combining simulations with a small number of ground samples for providing improved accuracy at a minimal cost could be explored in the future.

Finally, even though our study placed emphasis on measuring plot‐level yields, many applications, such as forecasting regional food supply or assessing local conditions for insurance payouts, will care more about accuracy at aggregate scales. What is expected to become increasingly more useful and insightful will be the ability to integrate georeferenced micro survey data on agriculture, such as the LSMS‐ISA, with the expanding, publicly‐available high‐resolution satellite imagery. Such ability, combined with advances in remote sensing methods as well as mobile technology and handheld sensors for cost‐effective, objective ground data capture, has the potential to create an unparalleled scope for research on entire landscapes of agricultural plots. Collectively, these measurement tools will allow more rapid feedback on the effectiveness of different efforts to raise productivity, which in turn can enable more effective food policy.

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