We examine relationships between landscape scale measurements of rangeland complexity and cattle stocking rates in Kansas. Rangeland complexity was characterized using Advanced Very High Resolution Radiometer (AVHRR) satellite imagery and the USGS National Land Cover Dataset (NLCD). Satellite Normalized Difference Vegetation Index (NDVI) values were summed over the 2002 growing-season and then summarized by county. FRAGSTATS spatial pattern analysis software was used to derive 43 metrics of landscape complexity for each county. The metrics used were of two types: spatial pattern metrics, which quantify the shape and distribution of rangeland patches in each county; and spectral diversity metrics, which take advantage of variation in NDVI values to quantify complexity within each rangeland patch. A principle components analysis step-wise regression model revealed a strong correlation between rangeland complexity and cattle stocking rates (r² = 0.53, p =0.000). This suggests that, from a rangeland grazing perspective, intact blocks of rangeland are more desirable than fragmented rangelands. The model indicates that spatial pattern metrics are better predictors of rangeland stocking rates than spectral diversity metrics. This study also demonstrates the utility of satellite remote sensing for monitoring rangeland condition at a landscape scale.