Producing quality data to support land management decisions is the goal of every rangeland monitoring program. However, the results of quality assurance (QA) and quality control (QC) efforts to improve data quality are rarely reported. The purpose of QA and QC is to prevent and describe non-sampling errors that introduce noise into monitoring datasets, thereby increasing the repeatability, defensibility, and usability of the data collected. Quality assurance is a proactive process designed to prevent errors from occurring, while QC is a reactive process whereby the number, nature, and implications of errors are identified. Common QA practices include careful design and documentation of the monitoring programs and protocols; training and calibration of data collectors; and structured management of resulting data. Quality control describes errors via data checks for incomplete or invalid values, variance decomposition, and evaluation of signal-to-noise ratios. We analyzed the calibration results and field data of two national monitoring datasets collected between 2011 and 2016. Field and lab calibration data demonstrate that the iterative learning by data collectors which occurs in QA improves field sampling results and can point to areas of future training. We also explored quantitative QC approaches for identifying and visualizing erroneous observations by comparing results to other data sources; and analyzing between-observer variability, patterns of missing data, and seasonal trends in method implementation - all of which help to describe the errors in these national datasets. We conclude that the results of the QA and QC processes can and should be evaluated to improve and document the quality of monitoring data. These evaluations can be used to improve monitoring data collection efforts and to support the role of rangeland monitoring in decision making.