On surveys, research has revealed the need to use improved methods for household and farm survey data collection for enhancing our understanding of the agricultural sector, particularly in low-income countries that stand to benefit the most from the resulting data (Abay et al. 2019; Carletto et al., 2015, 2017; Desiree and Jolliffe, 2018; Kilic et al., 2020). Gourlay et al. (2019) demonstrated that the inverse scale-productivity relationship in agriculture (i.e. the hypothesis that smallholders are more productive than their larger counterparts) may be a statistical artifact, driven by systematic measurement errors in farmer-reported survey data on crop production. The authors showcased these errors by comparing objectively-measured crop cutting and farmer-reported maize yields on the same sample of plots in Eastern Uganda.<sup>4</sup> Follow-up research has demonstrated that survey methods for measuring crop yields directly affect the utility of surveys for earth observation applications, and have provided unambiguous support for the use of objective survey methods to generate the required training and validation data for remote sensing models that integrate survey and satellite data to derive high-resolution estimates of crop yields (Lobell et al. 2019, 2020 in Uganda and Mali, respectively).

Despite the expanding knowledge base regarding the use of earth observation techniques in lowincome countries that are primarily characterized by smallholder farming, research studies have largely remained sub-national in scope and have exhibited heterogeneity in terms of the ground data used to fulfill comparable analytical objectives pursued in different settings. Lack of methodological research to identify the required volume of and approach to ground data collection for training and validating remote sensing models is arguably one of the hurdles against the scaleup of satellite-based estimation of agricultural outcomes across countries and expansive geographies. Identifying ground data requirements for key earth observation applications in lowincome countries, including high-resolution crop type mapping and crop yield estimation, would be important not only for assessing the utility of existing georeferenced household survey data for earth observation research but also informing the design of future large-scale household and farm surveys that can provide the required training and validation data for downstream earth observation efforts.<sup>5</sup> Against this background, this paper addresses several operational and inter-related research questions in the context of high-resolution maize area mapping in Malawi and Ethiopia: 1) what is the minimum volume of household survey data that is required to reach an acceptable level accuracy of a crop classification algorithm? and 2) how does the approach to georeferencing plot locations in household surveys impact the accuracy of the same crop classification algorithm?

> type mapping and yield estimation at the village-level in Konigue commune. Relatedly, Hegarty-Craver et al. (2020) mapped four crop types (maize, beans, bananas, cassava) in Senegal using training data derived from high-resolution UAV imagery which they collected in the field.

> 4 These findings are corroborated by Abay et al. (2019) and Desiere and Jolliffe (2018), in Ethiopia and Uganda, respectively.

> 5 Future large-scale surveys that can be a source of training and validation data for earth observation efforts include surveys that are supported by the 50x2030 Initiative.

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