Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover (f_R) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify f_R at scale has been limited. In this study we produced annual maps of f_R for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs (n = 895) classified to f_R using SamplePoint software. Univariate linear regressions were used to compare photograph f_R to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in f_R estimation in terms of coefficient of determination (R² = 0.869) and root mean square error (RMSE = 0.111), when R² and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest f_R estimation performance (R² = 0.795; RMSE = 0.141), suggesting that SINDRI can map f_R accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a multiple linear regression analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of f_R estimation significantly, particularly for SWIRA (R² = 0.767; RMSE = 0.144), but also NDTI (R² = 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for f_R estimation, SWIRA has the potential to improve f_R estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global f_R dating back to 1982.