Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates
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 performanc...
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doaj-3674377e6bec498bbbbc726155107f1f2021-08-26T14:17:29ZengMDPI AGRemote Sensing2072-42922021-08-01133160316010.3390/rs13163160Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability EstimatesDavid B. Lobell0Stefania Di Tommaso1Marshall Burke2Talip Kilic3Department of Earth System Science and the Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USADepartment of Earth System Science and the Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USADepartment of Earth System Science and the Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USALiving Standards Measurement Study (LSMS), Data Production and Methods Unit, Development Data Group, The World Bank, Washington, DC 20433, USASatellite 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 (<i>r</i><sup>2</sup>), 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.https://www.mdpi.com/2072-4292/13/16/3160agriculturecrop yieldshousehold consumptionsustainable development |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
David B. Lobell Stefania Di Tommaso Marshall Burke Talip Kilic |
spellingShingle |
David B. Lobell Stefania Di Tommaso Marshall Burke Talip Kilic Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates Remote Sensing agriculture crop yields household consumption sustainable development |
author_facet |
David B. Lobell Stefania Di Tommaso Marshall Burke Talip Kilic |
author_sort |
David B. Lobell |
title |
Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates |
title_short |
Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates |
title_full |
Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates |
title_fullStr |
Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates |
title_full_unstemmed |
Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates |
title_sort |
twice is nice: the benefits of two ground measures for evaluating the accuracy of satellite-based sustainability estimates |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-08-01 |
description |
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 (<i>r</i><sup>2</sup>), 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. |
topic |
agriculture crop yields household consumption sustainable development |
url |
https://www.mdpi.com/2072-4292/13/16/3160 |
work_keys_str_mv |
AT davidblobell twiceisnicethebenefitsoftwogroundmeasuresforevaluatingtheaccuracyofsatellitebasedsustainabilityestimates AT stefaniaditommaso twiceisnicethebenefitsoftwogroundmeasuresforevaluatingtheaccuracyofsatellitebasedsustainabilityestimates AT marshallburke twiceisnicethebenefitsoftwogroundmeasuresforevaluatingtheaccuracyofsatellitebasedsustainabilityestimates AT talipkilic twiceisnicethebenefitsoftwogroundmeasuresforevaluatingtheaccuracyofsatellitebasedsustainabilityestimates |
_version_ |
1721190254197604352 |