First, we find no clear evidence that different obfuscation methods have an impact on estimates of agricultural production. At this time, publicly available remote sensing weather products are too coarse a resolution for any of the ten obfuscation methods tested to make a substantial difference in which pixel a household ends up in. Second, we find mixed evidence regarding how different metrics used to quantify precipitation and temperature impact estimates of production. Of the 22 metrics (14 rainfall and eight temperature) that we test, only six performed consistently well across different models and countries. These tended to be simpler metrics, such as total seasonal rainfall or mean daily temperature instead of more complicated metrics, such as deviations in rainfall from the long run average or growing degree days (GDD). That said, some metrics performed particularly well in specific circumstances, such as longest dry spell in Niger and the variance of daily temperature in Tanzania. Lastly, we find substantial evidence of variation in how data drawn from different remote sensing products correlates with agricultural production. Remote sensing precipitation products that merge gauge and satellite data, such as ARC2, TAMSAT, and CHIRPS, all perform consistently well across a wide variety of settings and tend to produce results similar to each other.<sup>1</sup> Precipitation products that rely on assimilation models, such as ERA5 and MERRA-2, tend to report much higher volumes of precipitation compared to the other remote sensing products. In some cases, data drawn from these different products result in opposite signs for coefficients on rainfall metrics, meaning that precipitation as measured by ARC2, TAMSAT, and CHIRPS purportedly increases crop output while the same metrics drawn from ERA5 and MERRA-2 purportedly reduces crop output. The starkly different results, between precipitation products that merge gauge and satellite data and those that rely on assimilation models, are not present in interpolated gauge data products or in any of the temperature products.

That measurement error exists in remote sensing data is important. There is a large body of literature that relies on remote sensing weather data for identification of causal effects (Dell et al., 2014; Donaldson and Storeygard, 2016). This includes important contributions to our understanding of human capital formation (Maccini and Yang, 2009; Shah and Steinberg, 2017; Garg et al., 2020), labor markets (Jayachandran, 2006; Chen et al., 2017; Kaur, 2019; Morten, 2019), conflict and institutions (Br ̈uckner and Ciccone, 2011; Sarsons, 2015; K ̈onig et al., 2017), agricultural production and economic growth (Miguel et al., 2004; Deschˆene and Greenstone, 2007; Barrios et al., 2010; Dell et al., 2012; Yeh et al., 2020), intra-household bargaining (Corno et al., 2020), technology adoption (Suri, 2011; Taraz, 2018; Jagnani et al., 2021; Arag ́on et al., 2021; Tesfaye et al., 2021), and extreme weather impacts (Wineman et al., 2017; Michler et al., 2019; McCarthy et al., 2021). Although these studies take care to establish the robustness of their results to different modeling assumptions and potential measurement errors in administrative or survey data, none of them examines the robustness of their results to potential mismeasurement in their choice of remote sensing data source.<sup>2</sup>

> 1See Section 2.1 for a full description of each of these products.

> 2The question of whether weather data is mismeasured is distinct from the question of whether weather is exogenous

3