Standard indicators and measurements are key to cross-scale agroecosystem assessments. While the need to standardize indicators is broadly recognized, inconsistencies in indicator calculations from common measurements may yield discrepancies in indicator meaning and consequently reduce the accuracy of agroecosystem assessments. Here we present a framework that provides a mechanism for standardizing indicator vegetation and ground cover calculations across datasets and agroecosystems yet also provides flexibility for local or process-specific indicators. This framework, demonstrated in an R package terradactyl, 1) harmonizes standardized agroecosystem measurements into analysis-friendly datasets, 2) produces a standard, yet flexible indicator calculation approach, 3) aggregates indicators into commonly used sets of indicators or data models. This paper provides a workflow that can be applied across agroecosystem measurements and indicators globally to produce scale-able indicators that are relevant for land managers, conservation planners, and policymakers alike.