{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T09:04:42Z","timestamp":1790672682763,"version":"4.1.0"},"reference-count":26,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC17K0625"],"award-info":[{"award-number":["80NSSC17K0625"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2014agricultural yields, household asset ownership, and household consumption expenditures\u2014and 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 na\u00efve 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.<\/jats:p>","DOI":"10.3390\/rs13163160","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T08:57:14Z","timestamp":1628585834000},"page":"3160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5969-3476","authenticated-orcid":false,"given":"David B.","family":"Lobell","sequence":"first","affiliation":[{"name":"Department of Earth System Science and the Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0664-3651","authenticated-orcid":false,"given":"Stefania","family":"Di Tommaso","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and the Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marshall","family":"Burke","sequence":"additional","affiliation":[{"name":"Department of Earth System Science and the Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA"},{"name":"National Bureau of Economic Research, Cambridge, MA 02138, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talip","family":"Kilic","sequence":"additional","affiliation":[{"name":"Living Standards Measurement Study (LSMS), Data Production and Methods Unit, Development Data Group, The World Bank, Washington, DC 20433, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1126\/science.aaf7894","article-title":"Combining satellite imagery and machine learning to predict poverty","volume":"353","author":"Jean","year":"2016","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1126\/science.aac4420","article-title":"Supplementary Materials for Predicting poverty and wealth from mobile phone metadata","volume":"350","author":"Blumenstock","year":"2015","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sheehan, E., Meng, C., Jean, N., Tan, M., Burke, M., Ermon, S., Uzkent, B., and Lobell, D. 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