Abstract
Satellite data offer great promise for improving measures related to sustainable development goals. However, assessing satellite estimates is complicated by the fact that traditional ground-based measures of these same outcomes are often very noisy, leading to underestimation of satellite performance. Here, we quantify the amount of noise in traditional measures for three commonly studied outcomes in prior work—agricultural yields, household asset ownership, and household consumption expenditures—and present a theoretical basis for properly characterizing satellite performance in the presence of noisy ground data. We find that for both yield and consumption, repeated ground measures often disagree with each other, with less than half of the variability in one ground measure captured by the other. Estimates of the performance of satellite measures, in terms of squared correlation (r2), which account for this noise in ground data are accordingly higher, and occasionally even double, the apparent performance based on a naïve comparison of satellite and ground measures. Our results caution against evaluating satellite measures without accounting for noise in ground data and emphasize the benefit of estimating that noise by collecting at least two independent ground measures.
Highlights
Researchers and policy makers working on issues of poverty and food security often face a paucity of reliable data on outcomes of interest
Recent work has demonstrated promising results for using satellites to estimate outcomes such as agricultural crop yields, village-level measures of wealth based on asset ownership, average household consumption, income inequality, and the prevalence of informal settlements [4,5,6]
The approach relies primarily on having multiple, independent ground-based measures of outcomes, in the case of crop yields, we present an alternative that uses the satellite measures themselves to estimate likely errors in the ground data
Summary
Researchers and policy makers working on issues of poverty and food security often face a paucity of reliable data on outcomes of interest. Decisions about resource allocation to improve these outcomes are often made on the basis of very limited information. This longstanding situation has motivated efforts to develop alternate measurement approaches, including using satellite imagery, mobile phone data, crowdsourcing platforms, and social media [1,2,3]. Recent work has demonstrated promising results for using satellites to estimate outcomes such as agricultural crop yields, village-level measures of wealth based on asset ownership, average household consumption, income inequality, and the prevalence of informal settlements [4,5,6].
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