Smallholder farming systems are challenging environments for collection of frequent, accurate, and sufficient socio-environmental data. This undermines capacity to design and monitor effective policy to support this often vulnerable segment of the globe’s population. This workshop will develop a research methodology to assess the potential to monitor smallholder farmer well-being through relationships between household survey measures of well-being metrics (e.g. consumption) with remote-sensing derived crop yield estimates. Specific areas of interest include: i) capitalizing on the spatio-temporal coverage of remote sensing data to interpolate patterns in smallholder well-being at different spatial scales at different time-periods, ii) the use of frequentist and Bayesian statistics to model the spatial and temporal patterns in uncertainty in remote sensing derived predictions of smallholder well-being and, iii) methods to compare smallholder farmer sensitivity to a climate shock observed from remote-sensing proxies of well-being to actual responses to the shock based on household survey data.