Land area is a fundamental component of agricultural statistics, and of analyses undertaken by agricultural economists. While household surveys in developing countries have traditionally relied on farmers' own, potentially error-prone, land area assessments, the availability of affordable and reliable Global Positioning System (GPS) units has made GPS-based area measurement a practical alternative. Nonetheless, in an attempt to reduce costs, keep interview durations within reasonable limits, and avoid the difficulty of asking respondents to accompany interviewers to distant plots, survey implementing agencies typically require interviewers to record GPS-based area measurements only for plots within a given radius of dwelling locations. It is, therefore, common for as much as a third of the sample plots not to be measured, and research has not shed light on the possible selection bias in analyses relying on partial data due to gaps in GPS-based area measures. This paper explores the patterns of missingness in GPS-based plot areas, and investigates their implications for land productivity estimates and the inverse scale-land productivity relationship. Using Multiple Imputation (MI) to predict missing GPS-based plot areas in nationally-representative survey data from Uganda and Tanzania, the paper highlights the potential of MI in reliably simulating the missing data, and confirms the existence of an inverse scale-land productivity relationship, which is strengthened by using the complete, multiply-imputed dataset. The study demonstrates the usefulness of judiciously reconstructed GPS-based areas in alleviating concerns over potential measurement error in farmer-reported areas, and with regards to systematic bias in plot selection for GPS-based area measurement.