# **1. Introduction**

<mark>Agriculture is an integral part of livelihoods in Sub-Saharan Africa, where it can contribute up to 69 percent of household income in rural areas (Davis et al. 2017). As such, improving the productivity of smallholder farmers has been a long-standing goal in many African countries that aim to eliminate poverty and food insecurity.</mark> <mark>To monitor progress towards national and international development goals related to agricultural productivity, countries need accurate, crop-specific measures of area under cultivation, production and yields – not only at the national level but with sufficient within-country disaggregation that can guide targeting and evaluation of policies and programs promoting agricultural and rural development and resilience against disasters and extreme weather events.</mark> <mark>With the commencement of the European Space Agency’s Sentinel-2 mission in 2015 and the subsequent surge in the public availability of high-resolution satellite imagery, research has shown the feasibility of satellite-based monitoring of agricultural outcomes in smallholder farming systems (Becker-Reshef et al., 2020; Burke and Lobell, 2017; Jin et al., 2017, 2019; Lambert et al., 2018; Lobell et al., 2019, 2020). Latest advances in satellite imagery and remote sensing techniques have the potential to provide timely insights into conditions on the ground and can fill gaps in agricultural monitoring and statistics (Nakalembe, 2020).</mark> Satellite-based approaches to mapping agricultural outcomes, such as crop-specific estimates of cultivated areas and yields, require data for training and validating the underlying remote sensing models. The quality and spatial resolution of satellite-based estimates is directly impacted by the data used for model training and validation (Lobell et al., 2019, 2020). Recent earth observation research that has focused on low-income countries has relied largely on two sources of training and validation data: (i) manually-labeled optical imagery (DeFourny et al., 2019; Xiong et al., 2017; Wei et al., 2020),<sup>2</sup> and (ii) ground data collection, including as part of household and farm surveys (Hegarty-Craver et al., 2020; Jin et al., 2017, 2019; Kerner et al., 2020; Lambert et al., 2018; Richard et al., 2017).<sup>3</sup> Our paper is related to earth observation applications that rely on georeferenced survey data to meet model training and validation needs.

> 2 In order to map cropland across the African continent, researchers have manually labeled land cover types for sample points using high-resolution optical imagery as a reference. For example, Xiong et al (2017) created a training data set covering Africa by labeling homogenous crop/non-crop areas with a minimum area of 90 x 90 meters (0.81 ha). Several satellite-based cropland data sets have been created for the African continent (using manually-labeled satellite imagery - see Wei et al.’s (2020) comparison of the available data sets), and at sub-regional scales (DeFourny et al. 2019).

> 3 In order to train and validate remote sensing models for mapping cropland and areas cultivated with maize and nonmaize crops, including cassava and beans, at sub-national areas in Tanzania and Kenya, researchers either have used existing survey data that were collected by non-governmental organizations and government agencies to (Jin et al., 2017, 2019; Kerner et al., 2020) or have conducted new survey data collection to meet model training needs (Richard et al., 2017). Similarly, Lambert et al. (2018) rely on sub-national in situ data collected in Mali for satellite-based crop

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