# **1 Introduction**

Accurate measurement in agricultural survey data is key to official agricultural statistics and central to tracking progress towards national and international development goals. Recent work has shown that there is systematic measurement error in agricultural survey data on a range of topics, including cultivated area, crop production, yields, and crop variety, among others (Carletto et al., 2017; Abay et al., 2019; Kosmowski et al., 2019; Lobell et al., 2020; Gollin and Udry, 2021; Kilic et al., 2021). Such mismeasurement creates challenges for generating unbiased point estimates, making valid inferences, and, ultimately, for providing sound policy recommendations.

Lacking from this strand of empirical research is an exploration of the consequences of measurement error in remote sensing weather data. The goal of a remote sensing weather product is to document an objective fact: that is, the volume of precipitation or the temperature in a given location at a given time. Inaccuracies introduced by either the sensor (e.g. infrared, microwave, optical), the algorithm used to convert sensor data into rainfall or temperature (e.g. reanalysis, interpolation), or the resolution of the data (e.g. spatial, temporal) means remote sensing products may mismeasure the objective fact. Simply with respect to the “raw” weather data, there can be substantial variation in what a remote sensing product reports as the actual rainfall or temperature in a given location. Figures 1 and 2 show this variation across six remote sensing precipitation products and three temperature products. One precipitation product reports rainfall of 0-5mm in the southeast corner of the grid cell while a different product reports 47-64mm for the same location on the same day. Temperature also varies by remote sensing product, with one product reporting a maximum temperature of 23 degrees Celsius while another reports the maximum temperature that day as 27 degrees Celsius.

In this paper, we quantify the significance and magnitude of the effect of measurement error in remote sensing data from each of the above sources. We test this by modeling the relationship between weather and smallholder agricultural productivity, as measured through nationallyrepresentative, panel household surveys. Besides being a topic of research itself, agricultural production is often used to proxy for a variety of economic outcomes, including economic growth (Deschˆene and Greenstone, 2007), intra-household bargaining power (Corno et al., 2020), and migration (Jayachandran, 2006). We combine nine geospatial weather data sets (six precipitation, three temperature) with the geo-referenced household survey data from six Sub-Saharan African countries that are being supported by the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. The objective is to provide systematic evidence on mismeasurement in remote sensing data due to methods used to obfuscate exact household coordinates, metrics used to quantify the weather, and remote sensing data source.

Our goal is to provide guidance to researchers looking to use remote sensing weather data in economic applications regarding what data sources and weather metrics have strong predictive power over a large set of contexts along with which are only useful in highly specific settings.

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